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Journal articles on the topic 'Fault diagnosis'
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1
Boubaker, Sahbi, Souad Kamel, Nejib Ghazouani, and Adel Mellit. "Assessment of Machine and Deep Learning Approaches for Fault Diagnosis in Photovoltaic Systems Using Infrared Thermography." Remote Sensing 15, no. 6 (March 21, 2023): 1686. http://dx.doi.org/10.3390/rs15061686.
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Nowadays, millions of photovoltaic (PV) plants are installed around the world. Given the widespread use of PV supply systems and in order to keep these PV plants safe and to avoid power losses, they should be carefully protected, and eventual faults should be detected, classified and isolated. In this paper, different machine learning (ML) and deep learning (DL) techniques were assessed for fault detection and diagnosis of PV modules. First, a dataset of infrared thermography images of normal and failure PV modules was collected. Second, two sub-datasets were built from the original one: The first sub-dataset contained normal and faulty IRT images, while the second one comprised only faulty IRT images. The first sub-dataset was used to develop fault detection models referred to as binary classification, for which an image was classified as representing a faulty PV panel or a normal one. The second one was used to design fault diagnosis models, referred to as multi-classification, where four classes (Fault1, Fault2, Fault3 and Fault4) were examined. The investigated faults were, respectively, failure bypass diode, shading effect, short-circuited PV module and soil accumulated on the PV module. To evaluate the efficiency of the investigated models, convolution matrix including precision, recall, F1-score and accuracy were used. The results showed that the methods based on deep learning exhibited better accuracy for both binary and multiclass classification while solving the fault detection and diagnosis problem in PV modules/arrays. In fact, deep learning techniques were found to be efficient for the detection and classification of different kinds of defects with good accuracy (98.71%). Through a comparative study, it was confirmed that the DL-based approaches have outperformed those based on ML-based algorithms.
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Zhu, Liangyu, Shuilong He, Li Ouyang, Chaofan Hu, and Yanxue Wang. "Hierarchical Diagnosis Network Based on Easy Transfer Learning and Its Application in Bearing Fault Diagnosis." Journal of Physics: Conference Series 2184, no. 1 (March 1, 2022): 012013. http://dx.doi.org/10.1088/1742-6596/2184/1/012013.
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Abstract Aiming at the problem of inconsistent distribution of rolling bearing vibration data under variable operating conditions, insufficient diagnostic data of the target bearing affects the accuracy of fault diagnosis, and the unknown severity of rolling bearing faults, a hierarchical diagnosis network based on easy transfer learning is presented in this paper and its application in the qualitative and quantitative diagnosis of rolling bearing faults. First, the wavelet transform is used to extract the fault features conducive to identifying the rolling bearing vibration data under various working conditions. Then, input the features extracted from the vibration signals of different fault types into the first layer easy transfer learning fault type recognizer to determine whether the target bearing is faulty and the fault type. After the fault type is determined, the features extracted from the vibration signals of the known fault types and different fault sizes are input into the second layer easy transfer learning fault size recognizer to determine the fault size of the rolling bearing. The proposed method is validated by the bearing data set of Case Western Reserve University and compared with other transfer learning methods that perform the same processing. The experimental results show the effectiveness and superiority of the method.
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Elmishali, Amir, Roni Stern, and Meir Kalech. "Data-Augmented Software Diagnosis." Proceedings of the AAAI Conference on Artificial Intelligence 30, no. 2 (February 18, 2016): 4003–9. http://dx.doi.org/10.1609/aaai.v30i2.19076.
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Software fault prediction algorithms predict which software components is likely to contain faults using machine learning techniques. Software diagnosis algorithm identify the faulty software components that caused a failure using model-based or spectrum based approaches. We show how software fault prediction algorithms can be used to improve software diagnosis. The resulting data-augmented diagnosis algorithm overcomes key problems in software diagnosis algorithms: ranking diagnoses and distinguishing between diagnoses with high probability and low probability. We demonstrate the efficiency of the proposed approach empirically on three open sources domains, showing significant increase in accuracy of diagnosis and efficiency of troubleshooting. These encouraging results suggests broader use of data-driven methods to complement and improve existing model-based methods.
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Tang, Jing, Guangkuo Guo, Ji Wang, and Wei Xu. "Fault injection and diagnosis of the centrifugal fan." Journal of Physics: Conference Series 2366, no. 1 (November 1, 2022): 012023. http://dx.doi.org/10.1088/1742-6596/2366/1/012023.
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Abstract Centrifugal fan is widely used in industry, vehicle and ship application to provide air cycle power. To detect the fan fault at early stage, this paper explores the fault feature in the vibration signal and fault diagnosis method through the fault injection test, where impeller unbalance, rotor unbalance, and bearing fault are involved. Firstly, the fault mechanism is introduced; then, the fault injection methods of the three faults are introduced to obtain the faulty fans; finally, health and fault tests are performed, where the vibration sensors are employed and distributed on the fan in the axial and radial direction. Moreover, the acquired vibration data is analyzed by using time-domain and frequency-domain methods, and further the fault features between health and fault are compared and discussed. The analysis results indicate that the three faults can be detected through the vibration intensity and magnitude comparison at their individual characteristic frequencies in the spectrum.
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Sun, Yin Qiu, and Hai Lin Feng. "Intermittent Faults Diagnosis in Wireless Sensor Networks." Applied Mechanics and Materials 160 (March 2012): 318–22. http://dx.doi.org/10.4028/www.scientific.net/amm.160.318.
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Sensor node intermittent faults which sometimes behave as fault-free are common in wireless sensor networks. Intermittent faults also affect network performance and faults detection accuracy, so it is important to diagnose the intermittent faulty nodes accurately. This paper proposes a distributed clustering intermittent faults diagnosis method. First, the network is divided into several clusters with the cluster heads should be diagnosed as good. Then, the cluster members are diagnosed by their cluster head. In order to improve the validity of proposed diagnose method, a strategy which collect data for many times is adopted. Analysis of fault diagnosable is given, and simulation results indicate the proposed algorithm has high fault detection accuracy.
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Chu, Kenny Sau Kang, Kuew Wai Chew, Yoong Choon Chang, and Stella Morris. "An Open-Circuit Fault Diagnosis System Based on Neural Networks in the Inverter of Three-Phase Permanent Magnet Synchronous Motor (PMSM)." World Electric Vehicle Journal 15, no. 2 (February 16, 2024): 71. http://dx.doi.org/10.3390/wevj15020071.
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Three-phase motors find extensive applications in various industries. Open-circuit faults are a common occurrence in inverters, and the open-circuit fault diagnosis system plays a crucial role in identifying and addressing these faults to enhance the safety of motor operations. Nevertheless, the current open-circuit fault diagnosis system faces challenges in precisely detecting specific faulty switches. The proposed work presents a neural network-based open-circuit fault diagnosis system for identifying faulty power switches in inverter-driven motor systems. The system leverages trained phase-to-phase voltage data from the motor to recognize the type and location of faults in each phase with high accuracy. Employing separate neural networks for each of the three phases in a three-phase permanent magnet synchronous motor, the system achieves an outstanding overall fault detection accuracy of approximately 99.8%, with CNN and CNN-LSTM architectures demonstrating superior performance. This work makes two key contributions: (1) implementing neural networks to significantly improve the accuracy of locating faulty switches in open-circuit fault scenarios, and (2) identifying the optimal neural network architecture for effective fault diagnosis within the proposed system.
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Sabna M. "A Single Detection And Diagnosis Algorithm For Electrical Faults in a Five-Phase.Permanent.Magnet Synchronous Motor Drive." Journal of Electrical Systems 20, no. 11s (November 16, 2024): 3369–87. https://doi.org/10.52783/jes.8091.
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In all processing and manufacturing industries, approximately half of the operating cost is contributed to the maintenance process. Due to high reliability, and fault-tolerant capability, five-phase Permanent Magnet Synchronous Motors (5ϕ-PMSM) are commonly used in high-power and fault-tolerant applications. Early-stage detection and diagnosis of faults can reduce maintenance costs. This paper proposes a single algorithm for detecting and diagnosing electrical faults such as inter-turn short circuit faults, phase-to-phase faults, phase-to-ground to ground faults, and open circuit faults in a 5ϕ-PMSM drive. The discrete wavelet transforms and statistical parameters extract the fault features from the normalized stator currents under normal and faulty conditions. A fuzzy logic system is adopted to diagnose electrical faults and faulty phases. Since the algorithm uses normalized stator currents for fault detection and diagnosis, it can be used for detecting and diagnosing electrical faults in 5ϕ-PMSM drive with any capacity. The time of fault detection and diagnosis process is less than two cycles of stator current. Finally, the proposed algorithm is experimentally validated using Raspberry Pi.
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Sun, Yong Kui, and Zhi Bin Yu. "Analog Circuits Fault Diagnosis Using Multifractal Analysis." Advanced Materials Research 721 (July 2013): 367–71. http://dx.doi.org/10.4028/www.scientific.net/amr.721.367.
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Analog circuits fault diagnosis using multifractal analysis is presented in this paper. The faulty response of circuit under test is analyzed by multifratal formalism, and the fault feature consists of multifractal spectrum parameters. Support vector machine is used to identify the faults. Experimental results prove the proposed method is effective and the diagnosis accuracy reaches 98%.
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Benyettou, L., T. Benslimane, O. Abdelkhalek, T. Abdelkrim, and K. Bentata. "Faults Diagnosis in Five-Level Three-Phase Shunt Active Power Filter." International Journal of Power Electronics and Drive Systems (IJPEDS) 6, no. 3 (September 1, 2015): 576. http://dx.doi.org/10.11591/ijpeds.v6.i3.pp576-585.
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In this paper, characteristics of open transistor faults in cascaded H-bridge five-level three-phase PWM controlled shunt active power filter are determined. Phase currents can’t be trusted as fault indicator since their waveforms are slightly changed in the presence of open transistor fault. The proposed method uses H bridges output voltages to determine the faulty phase, the faulty bridge and more precisely, the open fault transistor.
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Asokan, A., and D. Sivakumar. "Model based fault detection and diagnosis using structured residual approach in a multi-input multi-output system." Serbian Journal of Electrical Engineering 4, no. 2 (2007): 133–45. http://dx.doi.org/10.2298/sjee0702133a.
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Fault detection and isolation (FDI) is a task to deduce from observed variable of the system if any component is faulty, to locate the faulty components and also to estimate the fault magnitude present in the system. This paper provides a systematic method of fault diagnosis to detect leak in the three-tank process. The proposed scheme makes use of structured residual approach for detection, isolation and estimation of faults acting on the process [1]. This technique includes residual generation and residual evaluation. A literature review showed that the conventional fault diagnosis methods like the ordinary Chisquare (?2) test method, generalized likelihood ratio test have limitations such as the "false alarm" problem. From the results it is inferred that the proposed FDI scheme diagnoses better when compared to other conventional methods.
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Kang, Zhuang, and Li Zhang. "Fault diagnostic research on the MMC output current sensors." Journal of Physics: Conference Series 2849, no. 1 (September 1, 2024): 012036. http://dx.doi.org/10.1088/1742-6596/2849/1/012036.
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Abstract The modular multilevel converter (MMC) has been extensively studied, and sensors are an important part of the MMC control link. However, few diagnostic methods exist to study the diagnosis of sensor faults in MMCs. This paper proposes a fault diagnosis strategy for output sensors in MMCs, which can complete the detection and localization of the faulty output current sensor. According to the operating principle and mathematical model of MMC, the fault mechanism and fault feature are analyzed. Secondly, the fault diagnosis method is designed according to the fault characteristics. Finally, simulation results show that the strategy proposed in this paper can accurately diagnose faults.
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Papathanasopoulos, Dimitrios A., Konstantinos N. Giannousakis, Evangelos S. Dermatas, and Epaminondas D. Mitronikas. "Vibration Monitoring for Position Sensor Fault Diagnosis in Brushless DC Motor Drives." Energies 14, no. 8 (April 16, 2021): 2248. http://dx.doi.org/10.3390/en14082248.
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A non-invasive technique for condition monitoring of brushless DC motor drives is proposed in this study for Hall-effect position sensor fault diagnosis. Position sensor faults affect rotor position feedback, resulting in faulty transitions, which in turn cause current fluctuations and mechanical oscillations, derating system performance and threatening life expectancy. The main concept of the proposed technique is to detect the faults using vibration signals, acquired by low-cost piezoelectric sensors. With this aim, the frequency spectrum of the piezoelectric sensor output signal is analyzed both under the healthy and faulty operating conditions to highlight the fault signature. Therefore, the second harmonic component of the vibration signal spectrum is evaluated as a reliable signature for the detection of misalignment faults, while the fourth harmonic component is investigated for the position sensor breakdown fault, considering both single and double sensor faults. As the fault signature is localized at these harmonic components, the Goertzel algorithm is promoted as an efficient tool for the harmonic analysis in a narrow frequency band. Simulation results of the system operation, under healthy and faulty conditions, are presented along with the experimental results, verifying the proposed technique performance in detecting the position sensor faults in a non-invasive manner.
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Tadeusiewicz, Michal, and Stanislaw Halgas. "A method for fault diagnosis of nonlinear circuits." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 38, no. 6 (October 24, 2019): 1770–81. http://dx.doi.org/10.1108/compel-03-2019-0101.
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Purpose The purpose of this paper is to develop a method for multiple soft fault diagnosis of nonlinear circuits including fault detection, identification of faulty elements and estimation of their values in real circumstances. Design/methodology/approach The method for fault diagnosis proposed here uses a measurement test leading to a system of nonlinear equations expressing the measured quantities in terms of the circuit parameters. Nonlinear functions, which appear in these equations are not given in explicit analytical form. The equations are solved using a homotopy concept. A key problem of the solvability of the equations is considered locally while tracing the solution path. Actual faults are selected on the basis of the observation that the probability of faults in fewer number of elements is greater than in a larger number of elements. Findings The results indicate that the method is an effective tool for testing nonlinear circuits including bipolar junction transistors and junction field effect transistors. Originality/value The homotopy method is generalized and associated with a restart procedure and a numerical algorithm for solving differential equations. Testable sets of elements are found using the singular value decomposition. The procedure for selecting faulty elements, based on the minimal fault number rule, is developed. The method comprises both theoretical and practical aspects of fault diagnosis.
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Shao, Ning, Qing Chen, Dan Xie, Ye Sun, and Chengao Yu. "Improved Temporal Fuzzy Reasoning Spiking Neural P Systems for Power System Fault Diagnosis." Applied Sciences 14, no. 5 (February 21, 2024): 1753. http://dx.doi.org/10.3390/app14051753.
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Fuzzy and temporal reasoning can effectively improve the accuracy of fault diagnosis methods. However, there are challenges in practical applications, such as missing alarm messages, temporal reasoning with complex calculations, and complex modeling processes. Therefore, this study proposes an improved temporal fuzzy reasoning spiking neural P (ITFRSNP) system for power system fault diagnosis. First, the ITFRSNP system and its reasoning method are proposed to perform association reasoning between confidence degrees and temporal constraints. Second, a general fault diagnosis model and process are developed based on the ITFRSNP system to diagnose various faulty components and simplify the modeling process. In addition, a search method is provided for identifying suspected faulty components, considering the missing alarm message of the circuit breaker. Simulation results of fault cases demonstrate that the proposed method exhibits high accuracy and fault tolerance. It can precisely identify faulty components despite incorrect operations or inaccurate alarm messages of protective relays and circuit breakers. Moreover, the search method effectively narrows down the diagnostic scope without missing suspected faulty components in scenarios where alarms from boundary circuit breakers are missing, thereby enhancing the fault diagnosis efficiency. The fault diagnosis model features a straightforward structure and reasoning process with minimal computational complexity, making it suitable for real-time diagnosis of complex faults within power systems.
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FENG, Yunwen, Weihuang PAN, Cheng LU, and Jiaqi LIU. "Fault diagnosis and location of hydraulic system of domestic civil aircraft based on logic data." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 40, no. 4 (August 2022): 732–38. http://dx.doi.org/10.1051/jnwpu/20224040732.
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To study the typical fault diagnosis and fault location technology of the hydraulic system of the domestic civil aircraft, the logic data of the typical fault is constructed according to the formation conditions of the fault. The operation data of the typical fault is collected, and the Bayesian network is used to realize the fault diagnosis and fault components position. First, according to the fault formation conditions in the unit operation manual of a certain type of domestic civil aircraft, referring to the construction method of the logic data, taking the typical fault of the hydraulic system as an example, the fault logic data of the domestic civil aircraft is established to intuitively reflect the logical relationship of the fault formation; secondly, based on the constructed logic data, considering the formation conditions of the fault, a Bayesian network corresponding to the logic data is established, and the logical relationship formed by the fault is represented by the value of the conditional probability distribution; obtain quick access recorder (QAR) data and its parameter information according to the input information of the logic data; finally, according to the established Bayesian network and the obtained QAR data, apply forward reasoning to realize the diagnosis of typical faults of the hydraulic system. Under the condition of partial information, reverse reasoning is applied to locate the faulty components of hydraulic system. The research shows that the proposed method can accurately diagnose faults, and can accurately locate faulty components in complete information, and give the probability of occurrence of potentially faulty components under partial information, which can effectively assist in the location of faulty components. The research work has certain reference significance for improving the fault diagnosis function of the airborne health management system and the ground health management system of domestic civil aircraft.
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Yuan, Wenyi, Tianzhen Wang, Demba Diallo, and Claude Delpha. "A Fault Diagnosis Strategy Based on Multilevel Classification for a Cascaded Photovoltaic Grid-Connected Inverter." Electronics 9, no. 3 (March 4, 2020): 429. http://dx.doi.org/10.3390/electronics9030429.
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In this paper, an effective strategy is presented to realize IGBT open-circuit fault diagnosis for closed-loop cascaded photovoltaic (PV) grid-connected inverters. The approach is based on the analysis of the inverter output voltage time waveforms in healthy and faulty conditions. It is mainly composed of two parts. The first part is to select the similar faults based on Euclidean distance and set the specific labels. The second part is the classification based on Principal Component Analysis and Support Vector Machine. The classification is done in two steps. In the first, similar faults are grouped to do the preliminary diagnosis of all fault types. In the second step the similar faults are discriminated. Compared with existing fault diagnosis strategies for several fundamental periods and under different external environments, the proposed strategy has better robustness and higher fault diagnosis accuracy. The effectiveness of the proposed fault diagnosis strategy is assessed through simulation results.
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Lamperti, Gianfranco, Stefano Trerotola, Marina Zanella, and Xiangfu Zhao. "Sequence-Oriented Diagnosis of Discrete-Event Systems." Journal of Artificial Intelligence Research 78 (September 13, 2023): 69–141. http://dx.doi.org/10.1613/jair.1.14630.
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Model-based diagnosis has always been conceived as set-oriented, meaning that a candidate is a set of faults, or faulty components, that explains a collection of observations. This perspective applies equally to both static and dynamical systems. Diagnosis of discrete-event systems (DESs) is no exception: a candidate is traditionally a set of faults, or faulty events, occurring in a trajectory of the DES that conforms with a given sequence of observations. As such, a candidate does not embed any temporal relationship among faults, nor does it account for multiple occurrences of the same fault. To improve diagnostic explanation and support decision making, a sequence-oriented perspective to diagnosis of DESs is presented, where a candidate is a sequence of faults occurring in a trajectory of the DES, called a fault sequence. Since a fault sequence is possibly unbounded, as the same fault may occur an unlimited number of times in the trajectory, the set of (output) candidates may be unbounded also, which contrasts with set-oriented diagnosis, where the set of candidates is bounded by the powerset of the domain of faults. Still, a possibly unbounded set of fault sequences is shown to be a regular language, which can be defined by a regular expression over the domain of faults, a property that makes sequence-oriented diagnosis feasible in practice. The task of monitoring-based diagnosis is considered, where a new candidate set is generated at the occurrence of each observation. The approach is based on three different techniques: .1/ blind diagnosis, with no compiled knowledge, .2/ greedy diagnosis, with total knowledge compilation, and .3/ lazy diagnosis, with partial knowledge compilation. By knowledge we mean a data structure slightly similar to a classical DES diagnoser, which can be generated (compiled) either entirely offline (greedy diagnosis) or incrementally online (lazy diagnosis). Experimental evidence suggests that, among these techniques, only lazy diagnosis may be viable in non-trivial application domains.
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Wang, Zhifu, Wei Luo, Song Xu, Yuan Yan, Limin Huang, Jingkai Wang, Wenmei Hao, and Zhongyi Yang. "Electric Vehicle Lithium-Ion Battery Fault Diagnosis Based on Multi-Method Fusion of Big Data." Sustainability 15, no. 2 (January 6, 2023): 1120. http://dx.doi.org/10.3390/su15021120.
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Power batteries are the core of electric vehicles, but minor faults can easily cause accidents; therefore, fault diagnosis of the batteries is very important. In order to improve the practicality of battery fault diagnosis methods, a fault diagnosis method for lithium-ion batteries in electric vehicles based on multi-method fusion of big data is proposed. Firstly, the anomalies are removed and early fault analysis is performed by t-distribution random neighborhood embedding (t-Sne) and wavelet transform denoising. Then, different features of the vehicle that have a large influence on the battery fault are identified by factor analysis , and the faulty features are extracted by a two-way long and short-term memory network method with convolutional neural network. Finally a self-learning Bayesian network is used to diagnose the battery fault. The results show that the method can improve the accuracy of fault diagnosis by about 12% when verified with data from different vehicles, and after comparing with other methods, the method not only has higher fault diagnosis accuracy, but also reduces the response time of fault diagnosis, and shows superiority compared to graded faults, which is more in line with the practical application of engineering.
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Xiao, Yancai, Jinyu Xue, Mengdi Li, and Wei Yang. "Low-Pass Filtering Empirical Wavelet Transform Machine Learning Based Fault Diagnosis for Combined Fault of Wind Turbines." Entropy 23, no. 8 (July 29, 2021): 975. http://dx.doi.org/10.3390/e23080975.
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Fault diagnosis of wind turbines is of great importance to reduce operating and maintenance costs of wind farms. At present, most wind turbine fault diagnosis methods are focused on single faults, and the methods for combined faults usually depend on inefficient manual analysis. Filling the gap, this paper proposes a low-pass filtering empirical wavelet transform (LPFEWT) machine learning based fault diagnosis method for combined fault of wind turbines, which can identify the fault type of wind turbines simply and efficiently without human experience and with low computation costs. In this method, low-pass filtering empirical wavelet transform is proposed to extract fault features from vibration signals, LPFEWT energies are selected to be the inputs of the fault diagnosis model, a grey wolf optimizer hyperparameter tuned support vector machine (SVM) is employed for fault diagnosis. The method is verified on a wind turbine test rig that can simulate shaft misalignment and broken gear tooth faulty conditions. Compared with other models, the proposed model has superiority for this classification problem.
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Deng, Yong, Yibing Shi, and Wei Zhang. "Diagnosis of Incipient Faults in Nonlinear Analog Circuits." Metrology and Measurement Systems 19, no. 2 (January 1, 2012): 203–18. http://dx.doi.org/10.2478/v10178-012-0018-7.
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Diagnosis of Incipient Faults in Nonlinear Analog Circuits Considering the problem to diagnose incipient faults in nonlinear analog circuits, a novel approach based on fractional correlation is proposed and the application of the subband Volterra series is used in this paper. Firstly, the subband Volterra series is calculated from the input and output sequences of the circuit under test (CUT). Then the fractional correlation functions between the fault-free case and the incipient faulty cases of the CUT are derived. Using the feature vectors extracted from the fractional correlation functions, the hidden Markov model (HMM) is trained. Finally, the well-trained HMM is used to accomplish the incipient fault diagnosis. The simulations illustrate the proposed method and show its effectiveness in the incipient fault recognition capability.
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Zou, Xiuguo, Wenchao Liu, Zhiqiang Huo, Sunyuan Wang, Zhilong Chen, Chengrui Xin, Yungang Bai, et al. "Current Status and Prospects of Research on Sensor Fault Diagnosis of Agricultural Internet of Things." Sensors 23, no. 5 (February 24, 2023): 2528. http://dx.doi.org/10.3390/s23052528.
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Sensors have been used in various agricultural production scenarios due to significant advances in the Agricultural Internet of Things (Ag-IoT), leading to smart agriculture. Intelligent control or monitoring systems rely heavily on trustworthy sensor systems. Nonetheless, sensor failures are likely due to various factors, including key equipment malfunction or human error. A faulty sensor can produce corrupted measurements, resulting in incorrect decisions. Early detection of potential faults is crucial, and fault diagnosis techniques have been proposed. The purpose of sensor fault diagnosis is to detect faulty data in the sensor and recover or isolate the faulty sensors so that the sensor can finally provide correct data to the user. Current fault diagnosis technologies are based mainly on statistical models, artificial intelligence, deep learning, etc. The further development of fault diagnosis technology is also conducive to reducing the loss caused by sensor failures.
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Manikandan, V., and N. Devarajan. "SBT Approach towards Analog Electronic Circuit Fault Diagnosis." Active and Passive Electronic Components 2007 (2007): 1–12. http://dx.doi.org/10.1155/2007/59856.
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An approach for the fault diagnosis of single and multiple faults in linear analog electronic circuits is proposed in this paper. The simulation-before-test (SBT) diagnosis approach proposed in this write up basically consists of obtaining the frequency response of fault free/faulty circuit. The peak frequency and the peak amplitude from the error response are observed and processed suitably to extract distinct signatures for faulty and nonfaulty conditions under maximum tolerance conditions for other network components. The artificial neural network classifiers are then used for the classification of fault. Networks of reasonable dimensions are shown to be capable of robust diagnosis of analog circuits including effects due to tolerances. This is a unique contribution of this paper. Fault computation time is drastically reduced from the traditional analysis techniques. This results in a direct dollar savings at test time. A comparison of the proposed work with the previous works which also employ preprocessing techniques, reveals that our algorithm performs significantly better in fault diagnosis of analog circuits due to our proposed preprocessing techniques.
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Kherif, Omar, Tahar Zebbadji, Youcef Gherbi, Mohamed Larbi Azzouze, and Madjid Teguar. "Simplified Diagnosis Method for CHBMIs under Open-circuit Switch or Battery Failure." ENP Engineering Science Journal 1, no. 2 (December 31, 2021): 17–25. http://dx.doi.org/10.53907/enpesj.v1i2.23.
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This paper deals with the diagnosis of cascaded H-bridge multilevel inverters controlled by a sinusoidal level-shifted pulse-width modulation technique. For this purpose, the behaviour of 3, 5, 7 and 9-level inverters is studied for regular and faulty operation modes. Three types of recurring faults are considered, namely open-circuit of a switch, damaged and disconnected battery. Under a single fault, the output voltage signals are presented where the impact of each fault is discussed. In order to detect, identify and locate the three types of fault, a signal processing method is proposed, elaborating the output voltage of inverters with and without fault. The obtained results are convincing for the considered cases. The study shows no real correlation between the selected features from one to the other type of fault. Indeed, each fault type has its own trajectory with respect to the evolution of the output voltage characteristics. Thus, localizing the faulty component within the multilevel inverter can be made with no ambiguity. Such findings obviously solve a large part of problems associated with the presence of faults in multilevel inverters. They can help improving the reliability of the inverter in such way it continues working.
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Al-Zuriqat, Thamer, Carlos Chillón Geck, Kosmas Dragos, and Kay Smarsly. "Adaptive Fault Diagnosis for Simultaneous Sensor Faults in Structural Health Monitoring Systems." Infrastructures 8, no. 3 (February 22, 2023): 39. http://dx.doi.org/10.3390/infrastructures8030039.
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Structural health monitoring (SHM) is a non-destructive testing method that supports the condition assessment and lifetime estimation of civil infrastructure. Sensor faults may result in the loss of valuable data and erroneous structural condition assessments and lifetime estimations, in the worst case with structural damage remaining undetected. As a result, the concepts of fault diagnosis (FD) have been increasingly adopted by the SHM community. However, most FD concepts for SHM consider only single-fault occurrence, which may oversimplify actual fault occurrences in real-world SHM systems. This paper presents an adaptive FD approach for SHM systems that addresses simultaneous faults occurring in multiple sensors. The adaptive FD approach encompasses fault detection, isolation, and accommodation, and it builds upon analytical redundancy, which uses correlated data from multiple sensors of an SHM system. Specifically, faults are detected using the predictive capabilities of artificial neural network (ANN) models that leverage correlations within sensor data. Upon defining time instances of fault occurrences in the sensor data, faults are isolated by analyzing the moving average of individual sensor data around the time instances. For fault accommodation, the ANN models are adapted by removing faulty sensors and by using sensor data prior to the occurrence of faults to produce virtual outputs that substitute the faulty sensor data. The proposed adaptive FD approach is validated via two tests using sensor data recorded by an SHM system installed on a railway bridge. The results demonstrate that the proposed approach is capable of ensuring the accuracy, reliability, and performance of real-world SHM systems, in which faults in multiple sensors occur simultaneously.
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Nguyen, Ngoc Phi, and Sung Kyung Hong. "Active Fault-Tolerant Control of a Quadcopter against Time-Varying Actuator Faults and Saturations Using Sliding Mode Backstepping Approach." Applied Sciences 9, no. 19 (September 25, 2019): 4010. http://dx.doi.org/10.3390/app9194010.
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Fault-tolerant control is becoming an interesting topic because of its reliability and safety. This paper reports an active fault-tolerant control method for a quadcopter unmanned aerial vehicle (UAV) to handle actuator faults, disturbances, and input constraints. A robust fault diagnosis based on the H ∞ scheme was designed to estimate the magnitude of a time-varying fault in the presence of disturbances with unknown upper bounds. Once the fault estimation was complete, a fault-tolerant control scheme was proposed for the attitude system, using adaptive sliding mode backstepping control to accommodate the actuator faults, despite actuator saturation limitation and disturbances. The Lyapunov theory was applied to prove the robustness and stability of the closed-loop system under faulty operation. Simulation results show the effectiveness of the fault diagnosis scheme and proposed controller for handling actuator faults.
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Islampurkar, Mangesh, Kishanprasad Gunale, Sunil Somani, and Nikhil Bagade. "Multiple Stuck At Fault Diagnosis System For Digital Circuit On FPGA Using Vedic Multiplier and ANN." International Journal of Circuits, Systems and Signal Processing 16 (May 30, 2022): 985–92. http://dx.doi.org/10.46300/9106.2022.16.120.
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In an electronics circuit, the presence of a Fault leads to undesired or unexpected results. The output of many nodes on the circuit is changed due to the presence of the Fault at one node. So, it is necessary to detect the nature of the Fault present in a particular faulty node. To detect the fault present in the digital circuit, it is necessary to understand logical behavior using mathematical modeling. After the successful modeling, parameters are extracted, and the database is generated. The mathematical model uses Hebbian Artificial Neural Network algorithms [1] [2]. The database generated is used by the fault detection system to find the masked and multiple faults. A fault detection system monitors the faults present in the test circuit and finds the origin and nature of the Fault [3] [4]. The database generated for single stuck-at faults is used to find the multiple faults present in the faulty circuit. In this paper, Modified Vedic Multiplication [5] [4] method is used to optimize the utilization of the proposed system. In this proposed design multiplier of {N x N} bit input and {N} bit output is used, due to which device utilization is decreased, which is the expected outcome from the design. This system is designed using ISE Design Suite and implemented on Spartan-6 FPGA [6] [7].
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Nedjah, Nadia, Jalber Dinelli Luna Galindo, Luiza de Macedo Mourelle, and Fernanda Duarte Vilela Reis de Oliveira. "Fault Diagnosis in Analog Circuits Using Swarm Intelligence." Biomimetics 8, no. 5 (August 25, 2023): 388. http://dx.doi.org/10.3390/biomimetics8050388.
Full textAbstract:
Open or short-circuit faults, as well as discrete parameter faults, are the most commonly used models in the simulation prior to testing methodology. However, since analog circuits exhibit continuous responses to input signals, faults in specific circuit elements may not fully capture all potential component faults. Consequently, diagnosing faults in analog circuits requires three key aspects: identifying faulty components, determining faulty element values, and considering circuit tolerance constraints. To tackle this problem, a methodology is proposed and implemented for fault diagnosis using swarm intelligence. The investigated optimization techniques are Particle Swarm Optimization (PSO) and the Bat Algorithm (BA). In this methodology, the nonlinear equations of the tested circuit are employed to calculate its parameters. The primary objective is to identify the specific circuit component that could potentially exhibit the fault by comparing the responses obtained from the actual circuit and the responses obtained through the optimization process. Two circuits are used as case studies to evaluate the performance of the proposed methodologies: the Tow–Thomas Biquad filter (case study 1) and the Butterworth filter (case study 2). The proposed methodologies are able to identify or at least reduce the number of possible faulty components. Four main performance metrics are extracted: accuracy, precision, sensitivity, and specificity. The BA technique demonstrates superior performance by utilizing the maximum combination of accessible nodes in the tested circuit, with an average accuracy of 95.5%, while PSO achieved only 93.9%. Additionally, the BA technique outperforms in terms of execution time, with an average time reduction of 7.95% reduction for the faultless circuit and an 8.12% reduction for the faulty cases. Compared to the machine-learning-based approach, using BA with the proposed methodology achieves similar accuracy rates but does not require any datasets nor any time-demanding training to proceed with circuit diagnostic.
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Zhou, Zhiyong, Junzhong Sun, Wei Cai, and Wen Liu. "Test Investigation and Rule Analysis of Bearing Fault Diagnosis in Induction Motors." Energies 16, no. 2 (January 6, 2023): 699. http://dx.doi.org/10.3390/en16020699.
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In this paper, a series of tests were conducted on the bearings of induction motors to investigate vibration signal analysis-based diagnosis of bearing faults, and a thorough analysis was also conducted. In the engineering field, the kurtosis coefficient of vibration acceleration and the root mean square of vibration velocity, as well as resonant demodulated spectrum analysis of vibration acceleration, have been widely used for bearing fault diagnosis. These are integrated in almost any commercially available device for diagnosing bearing faults. However, the unsuitable use of these devices results in many false diagnoses. In light of this, they were selected as research objects and were investigated experimentally. In three induction motors, faults of different severity in the bearing outer race and cage were modeled for tests, and the corresponding results were used to evaluate the performance of the selected diagnosis methods. Some vague information in engineering was clarified, and some instructive rules were outlined to improve the bearing fault diagnosis performance. Taking the kurtosis coefficient of vibration acceleration (Ku) as an example, in engineering, Ku = 4 is generally taken as the diagnostic threshold of bearing faults. This means the following rule applies: if Ku ≤ 4, the bearing is healthy; otherwise, the bearing is faulty. However, the test results in this paper show that even if Ku ≤ 4, the bearing might be faulty; if Ku > 4, the bearing is indeed faulty. Therefore, the diagnostic rule should be improved as follows: if Ku > 4, the bearing is faulty (which can be assured), and if Ku ≤ 4, the status of the bearing is still undetermined. Thus, this paper can be helpful for researchers to gain an experimental understanding of the selected diagnosis methods and provides some improved rules on their use for reducing false diagnoses.
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Zhang, Zhao, and Xiao He. "Fault-Structure-Based Active Fault Diagnosis: A Geometric Observer Approach." Energies 13, no. 17 (August 31, 2020): 4475. http://dx.doi.org/10.3390/en13174475.
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Fault diagnosis techniques can be classified into passive and active types. Passive approaches only utilize the original input and output signals of the system. Because of the small amplitudes, the characteristics of incipient faults are not fully represented in the data of the system, so it is difficult to detect incipient faults by passive fault diagnosis techniques. In contrast, active methods can design auxiliary signals for specific faults and inject them into the system to improve fault diagnosis performance. Therefore, active fault diagnosis techniques are utilized in this article to detect and isolate incipient faults based on the fault structure. A new framework based on observer approach for active fault diagnosis is proposed and the geometric approach based fault diagnosis observer is introduced to active fault diagnosis for the first time. Based on the dynamic equations of residuals, auxiliary signals are designed to enhance the diagnosis performance for incipient faults that have specific structures. In addition, the requirements that auxiliary signals need to meet are discussed. The proposed method can realize the seamless combination of active fault diagnosis and passive fault diagnosis. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed approach, and it is indicated that the proposed method significantly improves the accuracy of the diagnosis for incipient faults.
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Zhang, Xi, and Hui Lin. "Fault Diagnosis and Compensation Strategy of BLDC Motor Drives with Hall Sensors." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 37, no. 6 (December 2019): 1278–84. http://dx.doi.org/10.1051/jnwpu/20193761278.
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In a brushless DC motor drive system, two fault diagnosis methods were proposed in order to investigate the faults of Hall position sensors, and the corresponding compensation strategy was carried out. Firstly, the differences of Hall signal sequences during normal and fault situation of motor were analyzed, then a fault diagnosis method based on the characteristics of Hall signal sequences was proposed. In order to detect the faults of Hall position sensors in real time, a sliding mode-based super-twisting speed observer was established and combined with the Hall signal sequences. Under fault situations, the compensation controller was established, and a second-order speed prediction method was used to generate the compensation Hall signals of the faulty sensors. Finally, theoretical analysis and experiments demonstrate the effectiveness of the present fault diagnosis methods and compensation strategy.
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Wang, Ziyi, Shaohua Li, Wei He, Ruohan Yang, Zhichao Feng, and Guowen Sun. "A New Topology-Switching Strategy for Fault Diagnosis of Multi-Agent Systems Based on Belief Rule Base." Entropy 24, no. 11 (November 2, 2022): 1591. http://dx.doi.org/10.3390/e24111591.
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Effective fault-diagnosis strategies have been the focus of research on multi-agent systems (MASs). In this paper, the belief rule base (BRB)-based distributed fault-diagnosis problem for MASs is investigated, and a topology-switching strategy is developed to increase the reliability of fault-diagnosis model. Firstly, a BRB-based distributed fault-diagnosis model is constructed for the MAS with multiple faults, then expert knowledge is used to judge whether the agent is faulty. Then, considering that the system may be influenced by the fault or some other factors and thus leading to a decrease in the accuracy of the fault-diagnosis results, a topology-switching strategy based on the average distance of the output diagnosis accuracy is proposed to update the topology of the agent so that the fault-diagnosis results can be more reliable. Note that the topology-switching threshold is designed based on the average distance between the accuracy of the fault diagnosis of each agent. The method proposed in this paper can solve the problem when the fault-diagnosis accuracy of the model is affected by some common factors and thus decreases, and can improve the reliability of the fault-diagnosis model very well. Finally, the effectiveness of the BRB-based distributed fault-diagnosis model and the proposed topology-switching strategy to improve the fault-diagnosis accuracy is verified by simulation examples.
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Park, Pangun, Piergiuseppe Di Marco, Hyejeon Shin, and Junseong Bang. "Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network." Sensors 19, no. 21 (October 23, 2019): 4612. http://dx.doi.org/10.3390/s19214612.
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Fault detection and diagnosis is one of the most critical components of preventing accidents and ensuring the system safety of industrial processes. In this paper, we propose an integrated learning approach for jointly achieving fault detection and fault diagnosis of rare events in multivariate time series data. The proposed approach combines an autoencoder to detect a rare fault event and a long short-term memory (LSTM) network to classify different types of faults. The autoencoder is trained with offline normal data, which is then used as the anomaly detection. The predicted faulty data, captured by autoencoder, are put into the LSTM network to identify the types of faults. It basically combines the strong low-dimensional nonlinear representations of the autoencoder for the rare event detection and the strong time series learning ability of LSTM for the fault diagnosis. The proposed approach is compared with a deep convolutional neural network approach for fault detection and identification on the Tennessee Eastman process. Experimental results show that the combined approach accurately detects deviations from normal behaviour and identifies the types of faults within the useful time.
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Tian, Feng, Wen Jie Li, Zhi Gang Feng, and Rui Zhang. "Fault Diagnosis for Aircraft Engine Based on SVM Multiple Classifiers Fusion." Applied Mechanics and Materials 433-435 (October 2013): 607–11. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.607.
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Support vector machine (SVM) could well solve the over-learning and the low generalization ability of the neural network. But the single classifier cannot achieve satisfactory recognition rate and anti-interference ability. An aircraft engine fault diagnosis method based on support vector machine multiple classifiers is proposed in this paper. Firstly, sample characteristic information which constitutes the fault feature vectors obtained from the existing engine fault. Then, after training the SVM multiple classifier by faulty feature vectors, the SVM model of the fault diagnosis system is established; Finally, the trained SVM multiple classifier is used to recognize and classify the test faults. Applying the noise on the test samples, SVM multiple classifiers can still get a good diagnosis effect. It shows that the fault diagnosis algorithm has good robustness and can be applied to the study of aero engine fault diagnosis.
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Liu, Xiaoyang, Haizhou Huang, and Jiawei Xiang. "A Personalized Diagnosis Method to Detect Faults in a Bearing Based on Acceleration Sensors and an FEM Simulation Driving Support Vector Machine." Sensors 20, no. 2 (January 11, 2020): 420. http://dx.doi.org/10.3390/s20020420.
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Classification of faults in mechanical components using machine learning is a hot topic in the field of science and engineering. Generally, every real-world running mechanical system exhibits personalized vibration behaviors that can be measured with acceleration sensors. However, faulty samples of such systems are difficult to obtain. Therefore, machine learning methods, such as support vector machine (SVM), neural network (NNs), etc., fail to obtain agreeable fault detection results through smart sensors. A personalized diagnosis fault method is proposed to activate the smart sensor networks using finite element method (FEM) simulations. The method includes three steps. Firstly, the cosine similarity updated FEM models with faults are constructed to obtain simulation signals (fault samples). Secondly, every simulation signal is separated into sub-signals to solve the time-domain indexes to generate the faulty training samples. Finally, the measured signals of unknown samples (testing samples) are inserted into the trained SVM to classify faults. The personalized diagnosis method is applied to detect bearing faults of a public bearing dataset. The classification accuracy ratios of six types of faults are 90% and 92.5%, 87.5% and 87.5%, 85%, and 82.5%, respectively. It confirms that the present personalized diagnosis method is effectiveness to detect faults in the absence of fault samples.
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Shah, Ronit, Naveen Venkatesh, Arun Balaji, and V. Sugumaran. "Weightless neural network-based fault diagnosis in suspension system." FME Transactions 52, no. 1 (2024): 115–27. http://dx.doi.org/10.5937/fme2401115s.
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Vehicle suspension systems play a critical role in ensuring passenger comfort and safety. Detecting faults in these systems is vital for maintaining safety, performance, and cost-effectiveness. Traditional inspection methods have limitations, such as visual checks, bounce tests, and alignment assessments. This study explores Wilkie, Stonham, and Aleksander Recognition Device (WiSARD), a weightless neural network (WNN), for suspension fault diagnosis. A WNN model is employed to classify suspension system faults using sensor data. The dataset includes both normal and faulty conditions to train the model. The study assesses WiSARD under various fault conditions, including strut damage, mount failure, worn-out components, and low wheel pressure. Comparative evaluations demonstrate that the approach outperforms other classification techniques, achieving an impressive 95.63% accuracy with a rapid 0.05-second computation time for test data. This WNN-based method proves superior in detecting suspension faults and holds potential as a candidate for real-time vehicle fault diagnosis systems.
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Zhang, Fan, Xiao Zheng, Zixuan Xing, and Minghu Wu. "Fault Diagnosis Method for Lithium-Ion Power Battery Incorporating Multidimensional Fault Features." Energies 17, no. 7 (March 26, 2024): 1568. http://dx.doi.org/10.3390/en17071568.
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Accurately identifying a specific faulty monomer in a battery pack in the early stages of battery failure is essential to preventing safety accidents and minimizing property damage. While there are existing lithium-ion power battery fault diagnosis methods used in laboratory settings, their effectiveness in real-world vehicle conditions is limited. To address this, fault diagnosis methods for real-vehicle conditions should incorporate fault characteristic parameters based on external battery fault characterization, enabling the accurate identification of different fault types. However, these methods are constrained when confronted with complex fault types. To overcome these limitations, this paper proposes a battery fault diagnosis method that combines multidimensional fault features. By merging different fault feature parameters and mapping them to a high-dimensional space, the method utilizes a local outlier factor (LOF) algorithm to detect anomalous values, enabling fault diagnosis in complex working conditions. This method improves the detection time by an average of 22 min compared to the extended RMSE method and maintains strong robustness while correctly detecting faults compared to other conventional methods.
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Grzechca, Damian E. "Construction of an Expert System Based on Fuzzy Logic for Diagnosis of Analog Electronic Circuits." International Journal of Electronics and Telecommunications 61, no. 1 (March 1, 2015): 77–82. http://dx.doi.org/10.1515/eletel-2015-0010.
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Abstract The paper presents construction of the fuzzy logic system to analog circuits parametric fault diagnosis. The classical dictionary construction is replaced by fuzzy rule system. The first part refers to analog fault diagnosis, its techniques, approaches and goals. It clarifies common strategy and define differences between detecting, locating and identifying a fault in analog electronic circuit. The second part is focused on a creation of fuzzy rule expert system with use of sensitivity functions and known circuit topology. To detect, locate and identify a faulty element in a circuit the sensitivity matrix is used. The advantage of the method is its utilization in all, AC, DC and time domain. The fuzzy system, like the classical fault dictionary, can detect and locate single catastrophic faults and, on the contrary to the classical one, it also detects and locates parametric faults. Moreover, it allows identification of these faults, such that sign of the faulty parameter deviation is designated. The method has deterministic character as well as it can be applied on the verification and production stage
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Han, Zhiyan, and Jian Wang. "A Fault Diagnosis Method Based on Active Example Selection." Journal of Circuits, Systems and Computers 27, no. 01 (August 23, 2017): 1850013. http://dx.doi.org/10.1142/s0218126618500135.
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The fault diagnosis in the real world is often complicated. It is due to the fact that not all relevant fault information is available directly. In many fault diagnosis situations, it is impossible or inconvenient to find all fault information before establishing a fault diagnosis model. To deal with this issue, a method named active example selection (AES) is proposed for the fault diagnosis. AES could actively discover unseen faults and choose useful samples to improve the fault detection accuracy. AES consists of three key components: (1) a fusion model of combining the advantage of the unsupervised and supervised fault diagnosis methods, where the unsupervised fault diagnosis methods could discover unseen faults and the supervised fault diagnosis methods could provide better fault detection accuracy on seen faults, (2) an active learning algorithm to help the supervised fault diagnosis methods actively discover unseen faults and choose useful samples to improve the fault detection accuracy, and (3) an incremental learning scheme to speed up the iterative training procedure for AES. The proposed method was evaluated on the benchmark Tennessee Eastman Process data. The proposed method performed better on both unseen and seen faults than the stand-alone unsupervised, supervised fault diagnosis methods, their joint and referenced support vector machines based on active learning.
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YE, TAI-LING, DUN-WEI CHENG, and SUN-YUAN HSIEH. "Improved Precise Fault Diagnosis Algorithm for Hypercube-Like Systems Based on the Comparison Diagnosis Model." Journal of Interconnection Networks 16, no. 03n04 (September 2016): 1650009. http://dx.doi.org/10.1142/s0219265916500092.
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Multiprocessor systems are being increasingly adopted and the system reliability is an important perspective for multiprocessor systems. The fault diagnosis has become crucial for achieving high reliability in multiprocessor systems. The precise fault diagnosis diagnoses all processors correctly. In the comparison-based model, it allows a processor to perform diagnosis by contrasting the responses from a pair of neighboring processors through sending the identical assignment. On the basis of comparison-based model, Sengupta and Dahbura (“On self-diagnosable multiprocessor systems: diagnosis by the comparison approach,” IEEE Transaction on Computers, vol. 41, no. 11, pp. 1386–1396, 1992) put forward the MM* model, any processor c diagnoses two processors c1 and c2 if c has direct communication links to them. Sengupta and Dahbura also designed an O(N5)-time precise fault diagnosis algorithm to diagnose faulty processors for general topologies by using the MM* model, where N is the cardinality of processor set in multiprocessor systems. Lately, Ye and Hsieh (“A scalable comparison-based diagnosis algorithm for hypercube-like net-works,” IEEE Transaction on Reliability, vol. 62, no. 4, pp. 789–799, 2013) devised an precise fault diagnosis algorithm to diagnose all faulty processors for hypercube-like networks by using the MM* model with O(N(log2N)2) time complexity. On the basis of Hamiltonian cycle properties, we improve the aforementioned results by presenting an O(N)-time precise fault diagnosis algorithm to diagnose all faulty processors for hypercube-like networks by using the MM* model.
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Fu, Yanfang, Yu Ji, Gong Meng, Wei Chen, and Xiaojun Bai. "Three-Phase Inverter Fault Diagnosis Based on an Improved Deep Residual Network." Electronics 12, no. 16 (August 15, 2023): 3460. http://dx.doi.org/10.3390/electronics12163460.
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This study addresses the challenges of limited fault samples, noise interference, and low accuracy in existing fault diagnosis methods for three-phase inverters under real acquisition conditions. To increase the number of samples, Wavelet Packet Decomposition (WPD) denoising and a Conditional Variational Auto-Encoder (CVAE) are used for sample enhancement based on the existing faulty samples. The resulting dataset is then normalized, pre-processed, and used to train an improved deep residual network (SE-ResNet18) fault diagnosis model with a channel attention mechanism. Results show that the augmented fault samples improve the diagnosis accuracy compared with the original samples. Furthermore, the SE-ResNet18 model achieves higher fault diagnosis accuracy with fewer iterations and faster convergence, indicating its effectiveness in accurately diagnosing inverter open-circuit faults across various sample situations.
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Gmati, Badii, Amine Ben Rhouma, Houda Meddeb, and Sejir Khojet El Khil. "Diagnosis of Multiple Open-Circuit Faults in Three-Phase Induction Machine Drive Systems Based on Bidirectional Long Short-Term Memory Algorithm." World Electric Vehicle Journal 15, no. 2 (February 5, 2024): 53. http://dx.doi.org/10.3390/wevj15020053.
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Availability and continuous operation under critical conditions are very important in electric machine drive systems. Such systems may suffer from several types of failures that affect the electric machine or the associated voltage source inverter. Therefore, fault diagnosis and fault tolerance are highly required. This paper presents a new robust deep learning-based approach to diagnose multiple open-circuit faults in three-phase, two-level voltage source inverters for induction-motor drive applications. The proposed approach uses fault-diagnosis variables obtained from the sigmoid transformation of the motor stator currents. The open-circuit fault-diagnosis variables are then introduced to a bidirectional long short-term memory algorithm to detect the faulty switch(es). Several simulation and experimental results are presented to show the proposed fault-diagnosis algorithm’s effectiveness and robustness.
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Hu, Chengbo, Xueqiong Zhu, Yongling Lu, Ziquan Liu, Zhen Wang, Zhengyu Liu, and Kangyong Yin. "Localization and Diagnosis of Short-Circuit Faults in Transformer Windings Injected by Damped Oscillatory Wave." Energies 17, no. 24 (December 11, 2024): 6259. https://doi.org/10.3390/en17246259.
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Power transformers, as critical components in regional power distribution and transmission systems, require early fault detection to ensure system reliability. This paper presents a scalable design capable of rapidly simulating winding faults in experimental transformers. By diagnosing three-phase transformer winding short-circuit faults using oscillatory shock voltages and numerical statistical methods, the relationship between the transfer function and winding short-circuit faults is investigated. The experimental results show that winding short-circuit faults cause significant changes in the transfer function curve. By analyzing transfer function variations across different phases, the location of a fault can be effectively determined. Furthermore, the correlation coefficient and absolute logarithmic deviation provide a clear indication of the fault severity. The transfer function of the high-voltage phase-to-phase is particularly sensitive to winding short-circuit faults. In non-fault phases, after the application of damped oscillatory waves, the transfer function correlation coefficient becomes negative and the absolute logarithmic deviation increases linearly with fault severity. These findings provide a rapid diagnostic solution for determining both the faulty phase and the severity of damage in three-phase transformer winding short-circuit faults.
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Pi, Jun, Zhi Wei Li, and Guo Hua Yan. "Improved Fault Diagnosis Method for Aeroaccessory Gear." Advanced Materials Research 516-517 (May 2012): 718–21. http://dx.doi.org/10.4028/www.scientific.net/amr.516-517.718.
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The gear is widely used in aviation engines to transmit power. The gear faults affect somwhat the safety of the engines and aircafts. The vibration signal of gear is a carrier of gear situation information, it contains a lot of information about normal gear or faulty gear, so an effective signal process way is the important method of diagnosis the gear in good situation or not.The hybrid method of Wigner-Viller distribution (WVD) and singularity value decompositio(SVD) was introduced and applied to diagnose the gear faults in this paper. The results show that the hybrid method investigated is successfully to ascertain the gear fault.
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Yanghong, Tan, Zhang Haixia, and Zhou Ye. "A Simple-to-Implement Fault Diagnosis Method for Open Switch Fault in Wind System PMSG Drives without Threshold Setting." Energies 11, no. 10 (September 26, 2018): 2571. http://dx.doi.org/10.3390/en11102571.
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The conversion system is a major contributor to failure rates. These faults lead to time and cost consuming. Fault diagnosis capabilities pay as a solver to achieve a steady system. This paper presents a full analysis of permanent magnet synchronous generator wind system (PMSGWS) and proposes a special RMS voltage-based fault diagnosis method. The full analysis presents a comprehensive knowledge of faulty behaviors especially under arm current flowing or cutting off. Due to enough knowledge of faulty behaviors, the implementation of the detection method without threshold setting is contributed by the special RMS voltage. Its sample period is set longer than the time of the maximum pulse width ratio (MPR) and shorter than the fault show time of lower tube voltage. Due to this, the detection speed and robustness are achieved. By these simple settings for the fault diagnosis method, the faulty switch is detected in less than 1/4 of the period. Simulation and experimental results confirm the validity and feasibility of the new proposed fault detection method.
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Chen, Chaobo, Ying Yang, Binbin Zhang, and Song Gao. "The Diagnostic Method for Open-Circuit Faults in Inverters Based on Extended State Observer." Mathematical Problems in Engineering 2021 (April 26, 2021): 1–11. http://dx.doi.org/10.1155/2021/5526173.
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To reduce the influence of unknown disturbance on open-circuit fault diagnosis of inverters in the motor drive system, an open-circuit fault diagnosis method, which is based on extended state observer, is proposed for inverters. A mixed logic dynamic model of the inverters is established by analyzing the current flow path when the system works normally and there are open-circuit faults. A voltage extended state observer is designed for the mixed logic dynamic model. The open-circuit faults are detected according to the phase voltage residual between the observed voltage and the actual voltage. The position of the faulty switches is determined by querying the voltage residual information table. Finally, the simulation results show that the method can effectively reduce the influence of the unknown interference on the inverter faults diagnosis, improve the fault diagnosis rate, and verify the effectiveness and feasibility of the method.
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Chu, Kenny Sau Kang, Kuewwai Chew, and Yoong Choon Chang. "Fault-Diagnosis and Fault-Recovery System of Hall Sensors in Brushless DC Motor Based on Neural Networks." Sensors 23, no. 9 (April 27, 2023): 4330. http://dx.doi.org/10.3390/s23094330.
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This paper proposes a neural-network-based framework using Convolutional Neural Network and Long-Short Term Memory (CNN-LSTM) for detecting faults and recovering signals from Hall sensors in brushless DC motors. Hall sensors are critical components in determining the position and speed of motors, and faults in these sensors can disrupt their normal operation. Traditional fault-diagnosis methods, such as state-sensitive and transition-sensitive approaches, and fault-recovery methods, such as vector tracking observer, have been widely used in the industry but can be inflexible when applied to different models. The proposed fault diagnosis using the CNN-LSTM model was trained on the signal sequences of Hall sensors and can effectively distinguish between normal and faulty signals, achieving an accuracy of the fault-diagnosis system of around 99.3% for identifying the type of fault. Additionally, the proposed fault recovery using the CNN-LSTM model was trained on the signal sequences of Hall sensors and the output of the fault-detection system, achieving an efficiency of determining the position of the phase in the sequence of the Hall sensor signal at around 97%. This work has three main contributions: (1) a CNN-LSTM neural network structure is proposed to be implemented in both the fault-diagnosis and fault-recovery systems for efficient learning and feature extraction from the Hall sensor data. (2) The proposed fault-diagnosis system is equipped with a sensitive and accurate fault-diagnosis system that can achieve an accuracy exceeding 98%. (3) The proposed fault-recovery system is capable of recovering the position in the sequence states of the Hall sensors, achieving an accuracy of 95% or higher.
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Basnet, Barun, Hyunjun Chun, and Junho Bang. "An Intelligent Fault Detection Model for Fault Detection in Photovoltaic Systems." Journal of Sensors 2020 (June 9, 2020): 1–11. http://dx.doi.org/10.1155/2020/6960328.
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Effective fault diagnosis in a PV system requires understanding the behavior of the current/voltage (I/V) parameters in different environmental conditions. Especially during the winter season, I/V characters of certain faulty states in a PV system closely resemble that of a normal state. Therefore, a normal fault detection model can falsely predict a well-operating PV system as a faulty state and vice versa. In this paper, an intelligent fault diagnosis model is proposed for the fault detection and classification in PV systems. For the experimental verification, various fault state and normal state datasets are collected during the winter season under wide environmental conditions. The collected datasets are normalized and preprocessed using several data-mining techniques and then fed into a probabilistic neural network (PNN). The PNN model will be trained with the historical data to predict and classify faults when new data is fetched in it. The trained model showed better performance in prediction accuracy when compared with other classification methods in machine learning.
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Shen, Yifei, Tianzhen Wang, Yassine Amirat, and Guodong Chen. "IGBT Open-Circuit Fault Diagnosis for MMC Submodules Based on Weighted-Amplitude Permutation Entropy and DS Evidence Fusion Theory." Machines 9, no. 12 (November 26, 2021): 317. http://dx.doi.org/10.3390/machines9120317.
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Modular multilevel converters (MMCs) have a complex structure and a large number of submodules (SMs). If there is a fault in one of the SMs, it will affect the reliable operation of the system. Therefore, rapid fault diagnosis and accurate fault positioning are crucial to ensuring the continuous operation of the system. However, the IGBT open-circuit faults in different submodules of MMCs have similar fault features, and the traditional fault feature extraction method cannot effectively extract the key features of the fault so as to accurately locate the faulty submodules. In response to this problem, this paper proposes a fault diagnosis method based on weighted-amplitude permutation entropy (WAPE) and DS evidence fusion theory. The simulation results show that WAPE has better feature extraction ability than basic permutation entropy, and the fused multiscale feature decision output has better diagnostic accuracy than the single-scale feature. Compared with traditional fault diagnosis methods, this method achieves the diagnosis of multiple fault types by collecting a single signal, which greatly reduces the number of samples and leads to higher diagnostic accuracy and faster diagnostic speed.
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Li, Zhi Chun. "A Simple SOM Neural Network Based Fault Detection Model for Fault Diagnosis of Rolling Bearings." Applied Mechanics and Materials 397-400 (September 2013): 1321–25. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.1321.
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Rolling bearings are common parts in the transmission systems and have been widely used in various kinds of applications. The normal operation of the rolling bearings hence plays an important role on the efficiency of the system performance. However, due to hostile working environment the rolling bearings are prone to failures. The transmission systems may break down when there occurs faults in the rolling bearings. As a result, it is essential to detect the faults of rolling bearings. However, when use artificial intelligence method to diagnose the rolling bearings faults the signal processing is extensively complex while very few works have been done on the simplification of the artificial neural network (ANN) models for the rolling bearings fault detection. To deal with this problem, a simple self-organized map (SOM) neural network method together with a principal component analysis (PCA) based feature reduction procedure is proposed to diagnosis rolling bearings faults in this work. The vibration data of the normal and faulty rolling bearings was acquired from an experimental test bed. The PCA was firstly used to extract distinct fault features. Then the SOM was employed to train and learn the fault features to identify the fault patterns. The fault detection results show that the proposed method is feasible and effective for the fault diagnosis of rolling bearings. The fault detection rate is beyond 89.0%.
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Kouachi, Sabah, Nacerdine Bourouba, Kamel Mebarkia, and Imad Laidani. "Analog Circuits Fault Diagnosis Using ISM Technique and a GA-SVM Classifier Approach." Electronics ETF 28, no. 2 (December 15, 2024): 54–67. https://doi.org/10.53314/els2428054k.
Full textAbstract:
This present work aims to contribute to the solution of the problems encountered in electronic circuits fault diagnosis. One of these troubleshoots faced is the lack of effective features that help to optimize fault classifier and hence improve circuit fault detection and identification. Thus, our feature extraction approach is based on the CUT’s transfer function. This is deduced from the Matlab identification system IS model (ISM), namely the OE model belonging to the ARMA model’s family. These features are the transfer function polynomial coefficients playing a crucial role in the fault free and faulty circuits construction models and feeding the classifier for the fault diagnosis purpose. The faults we are dealing with are of single parametric type. This is done from PSPICE time domain analysis on the CUT output response under theses circuit conditions and followed by extracting the IS model (ISM) orders (p,q) polynomials. The coefficient values of the latter were considered as efficient comparison elements between faulty and healthy circuit responses. As a result, the OE model has achieved 100% fault coverage and its construction reached high accuracy level exceeding 98% for faulty circuits. This accuracy level ambition us to use its coefficients as input features for our Hybrid proposal fault classifier. This is built with GA and SVM algorithms combination targeting both data reduction and fault classification accuracy respectively. The results achieved are conclusive since the classifier accuracy level reached 100% and a 70% of feature data volume reduction was scored.