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S, Swetha, and Dr S. Venkatesh kumar. "Fault Detection and Prediction in Cloud Computing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (2018): 878–80. http://dx.doi.org/10.31142/ijtsrd18647.
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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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Biddle, Liam, and Saber Fallah. "A Novel Fault Detection, Identification and Prediction Approach for Autonomous Vehicle Controllers Using SVM." Automotive Innovation 4, no. 3 (2021): 301–14. http://dx.doi.org/10.1007/s42154-021-00138-0.
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AbstractFaults that develop in vehicle sensors have the potential to propagate unchecked throughout control systems if undetected. Automatic fault diagnosis and health monitoring algorithms will become necessary as automotive applications become more autonomous. The current fault diagnosis systems are not effective for complex systems such as autonomous cars where the case of simultaneous faults in different sensors is highly possible. Therefore, this paper proposes a novel fault detection, isolation and identification architecture for multi-fault in multi-sensor systems with an efficient computational burden for real-time implementation. Support Vector Machine techniques are used to detect and identify faults in sensors for autonomous vehicle control systems. In addition, to identify degrading performance in a sensor and predict the time at which a fault will occur, a novel predictive algorithm is proposed. The effectiveness and accuracy of the architecture in detecting and identifying multiple faults as well as the accuracy of the proposed predictive fault detection algorithm are verified through a MATLAB/IPG CarMaker co-simulation platform. The results present detection and identification accuracies of 94.94% and 97.01%, respectively, as well as a prediction accuracy of 75.35%.
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Patan, Krzysztof, and Józef Korbicz. "Nonlinear model predictive control of a boiler unit: A fault tolerant control study." International Journal of Applied Mathematics and Computer Science 22, no. 1 (2012): 225–37. http://dx.doi.org/10.2478/v10006-012-0017-6.
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Nonlinear model predictive control of a boiler unit: A fault tolerant control studyThis paper deals with a nonlinear model predictive control designed for a boiler unit. The predictive controller is realized by means of a recurrent neural network which acts as a one-step ahead predictor. Then, based on the neural predictor, the control law is derived solving an optimization problem. Fault tolerant properties of the proposed control system are also investigated. A set of eight faulty scenarios is prepared to verify the quality of the fault tolerant control. Based of different faulty situations, a fault compensation problem is also investigated. As the automatic control system can hide faults from being observed, the control system is equipped with a fault detection block. The fault detection module designed using the one-step ahead predictor and constant thresholds informs the user about any abnormal behaviour of the system even in the cases when faults are quickly and reliably compensated by the predictive controller.
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Wang, Shizhuang, Xingqun Zhan, Yawei Zhai, and Baoyu Liu. "Fault Detection and Exclusion for Tightly Coupled GNSS/INS System Considering Fault in State Prediction." Sensors 20, no. 3 (2020): 590. http://dx.doi.org/10.3390/s20030590.
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To ensure navigation integrity for safety-critical applications, this paper proposes an efficient Fault Detection and Exclusion (FDE) scheme for tightly coupled navigation system of Global Navigation Satellite Systems (GNSS) and Inertial Navigation System (INS). Special emphasis is placed on the potential faults in the Kalman Filter state prediction step (defined as “filter fault”), which could be caused by the undetected faults occurring previously or the Inertial Measurement Unit (IMU) failures. The integration model is derived first to capture the features and impacts of GNSS faults and filter fault. To accommodate various fault conditions, two independent detectors, which are respectively designated for GNSS fault and filter fault, are rigorously established based on hypothesis-test methods. Following a detection event, the newly-designed exclusion function enables (a) identifying and removing the faulty measurements and (b) eliminating the effect of filter fault through filter recovery. Moreover, we also attempt to avoid wrong exclusion events by analyzing the underlying causes and optimizing the decision strategy for GNSS fault exclusion accordingly. The FDE scheme is validated through multiple simulations, where high efficiency and effectiveness have been achieved in various fault scenarios.
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Al Qasem, Osama, and Mohammed Akour. "Software Fault Prediction Using Deep Learning Algorithms." International Journal of Open Source Software and Processes 10, no. 4 (2019): 1–19. http://dx.doi.org/10.4018/ijossp.2019100101.
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Software faults prediction (SFP) processes can be used for detecting faulty constructs at early stages of the development lifecycle, in addition to its being used in several phases of the development process. Machine learning (ML) is widely used in this area. One of the most promising subsets from ML is deep learning that achieves remarkable performance in various areas. Two deep learning algorithms are used in this paper, the Multi-layer perceptrons (MLPs) and Convolutional Neural Network (CNN). In order to evaluate the studied algorithms, four commonly used datasets from NASA are used i.e. (PC1, KC1, KC2 and CM1). The experiment results show how the CNN algorithm achieves prediction superiority of the MLP algorithm. The accuracy and detection rate measurements when using CNN has reached the standard ratio respectively as follows: PC1 97.7% - 73.9%, KC1 100% - 100%, KC2 99.3% - 99.2% and CM1 97.3% - 82.3%. This study provides promising results in using the deep learning for software fault prediction research.
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Li, Qiuying, and Hoang Pham. "Modeling Software Fault-Detection and Fault-Correction Processes by Considering the Dependencies between Fault Amounts." Applied Sciences 11, no. 15 (2021): 6998. http://dx.doi.org/10.3390/app11156998.
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Many NHPP software reliability growth models (SRGMs) have been proposed to assess software reliability during the past 40 years, but most of them have focused on modeling the fault detection process (FDP) in two ways: one is to ignore the fault correction process (FCP), i.e., faults are assumed to be instantaneously removed after the failure caused by the faults is detected. However, in real software development, it is not always reliable as fault removal usually needs time, i.e., the faults causing failures cannot always be removed at once and the detected failures will become more and more difficult to correct as testing progresses. Another way to model the fault correction process is to consider the time delay between the fault detection and fault correction. The time delay has been assumed to be constant and function dependent on time or random variables following some kind of distribution. In this paper, some useful approaches to the modeling of dual fault detection and correction processes are discussed. The dependencies between fault amounts of dual processes are considered instead of fault correction time-delay. A model aiming to integrate fault-detection processes and fault-correction processes, along with the incorporation of a fault introduction rate and testing coverage rate into the software reliability evaluation is proposed. The model parameters are estimated using the Least Squares Estimation (LSE) method. The descriptive and predictive performance of this proposed model and other existing NHPP SRGMs are investigated by using three real data-sets based on four criteria, respectively. The results show that the new model can be significantly effective in yielding better reliability estimation and prediction.
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Ma, Jie, and Jianan Xu. "Fault Prediction Algorithm for Multiple Mode Process Based on Reconstruction Technique." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/348729.
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In the framework of fault reconstruction technique, this paper studies the problems of multiple mode process fault detection, fault estimation, and fault prediction systematically based on multi-PCA model. First, a multi-PCA model is used for fault detection in steady state process under different conditions, while a weighted algorithm is applied to transition process. Then, describe the faults quantitatively and use the optimization method to derive the fault amplitude under the sense of fault reconstruction. Fault amplitude drifts under different conditions even if the same fault occurs. To solve the above problem, consistent estimation algorithm of fault amplitude under different conditions has been studied. Last, employ the support vector machine (SVM) to predict the trend of the fault amplitude. Effectiveness of the algorithms proposed in this paper has been verified using Tennessee Eastman process as the study object.
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Shin, Donghoon, Kang-moon Park, and Manbok Park. "Development of Fail-Safe Algorithm for Exteroceptive Sensors of Autonomous Vehicles." Electronics 9, no. 11 (2020): 1774. http://dx.doi.org/10.3390/electronics9111774.
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This paper presents a fail-safe algorithm for the exteroceptive sensors of autonomous vehicles. The proposed fault diagnosis mechanism consists of three parts: (1) fault detecting by a duplication-comparison method, (2) fault isolating by possible area prediction and (3) in-vehicle sensor fail-safes. The main ideas are the usage of redundant external sensor pairs, which estimate the same target, whose results are compared to detect the fault by a modified duplication-comparison method and the novel fault isolation method using target predictions. By comparing the estimations of surrounding vehicles and the raw measurement data, the location of faults can be determined whether they are from sensors themselves or a software error. In addition, faults were isolated by defining possible areas where existing sensor coordinates could be measured, which can be predicted by using previous estimation results. The performance of the algorithm has been tested by using offline vehicle data analysis via MATLAB. Various fault injection experiments were conducted and the performance of the suggested algorithm was evaluated based on the time interval between injection and the detection of faults.
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Encalada-Dávila, Á., C. Tutivén, B. Puruncajas, and Y. Vidal. "Wind Turbine Multi-Fault Detection based on SCADA Data via an AutoEncoder." Renewable Energy and Power Quality Journal 19 (September 2021): 487–92. http://dx.doi.org/10.24084/repqj19.325.
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Nowadays, wind turbine fault detection strategies are settled as a meaningful pipeline to achieve required levels of efficiency, availability, and reliability, considering there is an increasing installation of this kind of machinery, both in onshore and offshore configuration. In this work, it has been applied a strategy that makes use of SCADA data with an increased sampling rate. The employed wind turbine in this study is based on an advanced benchmark, established by the National Renewable Energy Laboratory (NREL) of USA. Different types of faults on several actuators and sensed by certain installed sensors have been studied. The proposed strategy is based on a normality model by means of an autoencoder. As of this, faulty data are used for testing from which prediction errors were computed to detect if those raise a fault alert according to a defined metric which establishes a threshold on which a wind turbine works securely. The obtained results determine that the proposed strategy is successful since the model detects the considered three types of faults. Finally, even when prediction errors are small, the model is able to detect the faults without problems.
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Guo, Ruijun, Guobin Zhang, Qian Zhang, et al. "An Adaptive Early Fault Detection Model of Induced Draft Fans Based on Multivariate State Estimation Technique." Energies 14, no. 16 (2021): 4787. http://dx.doi.org/10.3390/en14164787.
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The induced draft (ID) fan is an important piece of auxiliary equipment in coal-fired power plants. Early fault detection of the ID fan can provide predictive maintenance and reduce unscheduled shutdowns, thus improving the reliability of the power generation. In this study, an adaptive model was developed to achieve the early fault detection of ID fans. First, a non-parametric monitoring model was constructed to describe the normal operating characteristics with the multivariate state estimation technique (MSET). A similarity index representing operation status was defined according to the prediction deviations to produce warnings of early faults. To deal with the model accuracy degradation because of variant condition operation of the ID fan, an adaptive strategy was proposed by using the samples with a high data quality index (DQI) to manage the memory matrix and update the MSET model, thereby improving the fault detection results. The proposed method was applied to a 300 MW coal-fired power plant to achieve the early fault detection of an ID fan. In addition, fault detection by using the model without an update was also compared. Results show that the update strategy can greatly improve the MSET model accuracy when predicting normal operations of the ID fan; accordingly, the fault can be detected more than 4 h earlier by using the strategy with the adaptive update when compared to the model without an update.
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Betti, Alessandro, Mauro Tucci, Emanuele Crisostomi, Antonio Piazzi, Sami Barmada, and Dimitri Thomopulos. "Fault Prediction and Early-Detection in Large PV Power Plants Based on Self-Organizing Maps." Sensors 21, no. 5 (2021): 1687. http://dx.doi.org/10.3390/s21051687.
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In this paper, a novel and flexible solution for fault prediction based on data collected from Supervisory Control and Data Acquisition (SCADA) system is presented. Generic fault/status prediction is offered by means of a data driven approach based on a self-organizing map (SOM) and the definition of an original Key Performance Indicator (KPI). The model has been assessed on a park of three photovoltaic (PV) plants with installed capacity up to 10 MW, and on more than sixty inverter modules of three different technology brands. The results indicate that the proposed method is effective in predicting incipient generic faults in average up to 7 days in advance with true positives rate up to 95%. The model is easily deployable for on-line monitoring of anomalies on new PV plants and technologies, requiring only the availability of historical SCADA data, fault taxonomy and inverter electrical datasheet.
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Baek, Sujeong. "System integration for predictive process adjustment and cloud computing-based real-time condition monitoring of vibration sensor signals in automated storage and retrieval systems." International Journal of Advanced Manufacturing Technology 113, no. 3-4 (2021): 955–66. http://dx.doi.org/10.1007/s00170-021-06652-z.
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AbstractAs automation and digitalization are being increasingly implemented in industrial applications, manufacturing systems comprising several functions are becoming more complex. Consequently, fault analysis (e.g., fault detection, diagnosis, and prediction) has attracted increased research attention. Investigations involving fault analysis are usually performed using real-time, online, or automated techniques for fault detection or alarming. Conversely, recovery of faulty states to their healthy forms is usually performed manually under offline conditions. However, the development of intelligent systems requires that appropriate feedback be provided automatically, to facilitate faulty-state recovery without the need for manual operator intervention and/or decision-making. To this end, this paper proposes a system integration technique for predictive process adjustment that determines appropriate recovery actions and performs them automatically by analyzing relevant sensor signals pertaining to the current situation of a manufacturing unit via cloud computing and machine learning. The proposed system corresponds to an automated predictive process adjustment module of an automated storage and retrieval system (ASRS). The said integrated module collects and analyzes the temperature and vibration signals of a product transporter using an internet-of-things-based programmable logic controller and cloud computing to identify the current states of the ASRS system. Upon detection of faulty states, the control program identifies corresponding process control variables and controls them to recover the system to its previous no-fault state. The proposed system will facilitate automatic prognostics and health management in complex manufacturing systems by providing automatic fault diagnosis and predictive recovery feedback.
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Treetrong, Juggrapong. "Fault Prediction of Induction Motor Based on Time-Frequency Analysis." Applied Mechanics and Materials 52-54 (March 2011): 115–20. http://dx.doi.org/10.4028/www.scientific.net/amm.52-54.115.
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Because the faults happening in the motor (such as the stator and the rotor faults) can distort the sinusoidal response of the motor RPM and the main frequency, hence the spectrum method has previously been introduced which it relates to both amplitudes and phases among harmonics in a signal. The method popularly applied for fault detection is based on frequency analysis by observing the side band, its harmonics around main frequencies or its other harmonics. Based on the present experiments, the spectrum method by FFT function has ability to distinguish the motor condition. But, the fault severity levels seem to not able to analyze. Hence the time-frequency Analysis (or spectrogram) of the stator phase currents is proposed here. The method is expected to show relation between the phase current signals and the fault levels which make it can detect the faults and also indicate the fault levels. The experiments show that the proposed method can provide good accuracy for fault prediction and fault level quantification. Hence it can conclude that the propose method can be an effective tool for motor fault prediction.
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Yang, Jun Gang, Jie Zhang, Jian Xiong Yang, and Ying Huang. "A Principal Component Analysis Based Fault Detection Method in Etch Process of Semiconductor Manufacturing." Key Engineering Materials 522 (August 2012): 793–98. http://dx.doi.org/10.4028/www.scientific.net/kem.522.793.
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A Principal Component Analysis based Fault Detection method is proposed here to detect faults in etch process of semiconductor manufacturing. The main idea of this method is to calculate the loading vector and build the fault detection model according to training data. Using this model, the main information of fault data can be obtained immediately and easily. Also the principal component subspace and residual subspace can be constructed. Then, faults are detected by calculating Squared Prediction Error. Finally, an industrial example of Lam 9600 TCP metal etcher at Texas Instruments is used to demonstrate the performance of the proposed PCA-based method in fault detection, and the results show that it has such advantages as simple algorithm and low time cost, thus especially adapts to the real time fault detection of semiconductor manufacturing.
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Xiao, Sa, Jiajie Yao, Yanhu Chen, Dejun Li, Feng Zhang, and Yong Wu. "Fault Detection and Isolation Methods in Subsea Observation Networks." Sensors 20, no. 18 (2020): 5273. http://dx.doi.org/10.3390/s20185273.
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Subsea observation networks have gradually become the main means of deep-sea exploration. The reliability of the observation network is greatly affected by the severe undersea conditions. This study mainly focuses on theoretical research and the experimental platform verification of high-impedance and open-circuit fault detection for an underwater observation network. With the aid of deep learning, we perform the fault detection and prediction of the network operation. For the high-impedance and open-circuit fault detection of submarine cables, the entire system is modeled and simulated, and the voltage and current values of the operating nodes under different fault types are collected. Numerous calibrated data samples are supervised by a deep learning algorithm, and a fault location system model is built in the laboratory to verify the feasibility and superiority of the scheme. This paper also studies the fault isolation of the observation network, focusing on the communication protocol and the design of the fault isolation system. Experimental results verify the effectiveness of the proposed algorithm for the location and prediction of high-impedance and open-circuit faults, and the feasibility of the fault isolation system has also been verified. Moreover, the proposed methods greatly improve the reliability of undersea observation network systems.
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Liu, Jingjing, Chuanyang Liu, Yiquan Wu, Huajie Xu, and Zuo Sun. "An Improved Method Based on Deep Learning for Insulator Fault Detection in Diverse Aerial Images." Energies 14, no. 14 (2021): 4365. http://dx.doi.org/10.3390/en14144365.
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Insulators play a significant role in high-voltage transmission lines, and detecting insulator faults timely and accurately is important for the safe and stable operation of power grids. Since insulator faults are extremely small and the backgrounds of aerial images are complex, insulator fault detection is a challenging task for automatically inspecting transmission lines. In this paper, a method based on deep learning is proposed for insulator fault detection in diverse aerial images. Firstly, to provide sufficient insulator fault images for training, a novel insulator fault dataset named “InSF-detection” is constructed. Secondly, an improved YOLOv3 model is proposed to reuse features and prevent feature loss. To improve the accuracy of insulator fault detection, SPP-networks and a multi-scale prediction network are employed for the improved YOLOv3 model. Finally, the improved YOLOv3 model and the compared models are trained and tested on the “InSF-detection”. The average precision (AP) of the improved YOLOv3 model is superior to YOLOv3 and YOLOv3-dense models, and just a little (1.2%) lower than that of CSPD-YOLO model; more importantly, the memory usage of the improved YOLOv3 model is 225 MB, which is the smallest between the four compared models. The experimental results and analysis validate that the improved YOLOv3 model achieves good performance for insulator fault detection in aerial images with diverse backgrounds.
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Xiong, Wei, Xu Ji, Yue Ma, et al. "Seismic fault detection with convolutional neural network." GEOPHYSICS 83, no. 5 (2018): O97—O103. http://dx.doi.org/10.1190/geo2017-0666.1.
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Mapping fault planes using seismic images is a crucial and time-consuming step in hydrocarbon prospecting. Conventionally, this requires significant manual efforts that normally go through several iterations to optimize how the different fault planes connect with each other. Many techniques have been developed to automate this process, such as seismic coherence estimation, edge detection, and ant-tracking, to name a few. However, these techniques do not take advantage of the valuable experience accumulated by the interpreters. We have developed a method that uses the convolutional neural network (CNN) to automatically detect and map fault zones using 3D seismic images in a similar fashion to the way done by interpreters. This new technique is implemented in two steps: training and prediction. In the training step, a CNN model is trained with annotated seismic image cubes of field data, where every point in the seismic image is labeled as fault or nonfault. In the prediction step, the trained model is applied to compute fault probabilities at every location in other seismic image cubes. Unlike reported methods in the literature, our technique does not require precomputed attributes to predict the faults. We verified our approach on the synthetic and field data sets. We clearly determined that the CNN-computed fault probability outperformed that obtained using the coherence technique in terms of exhibiting clearer discontinuities. With the capability of emulating human experience and evolving through training using new field data sets, deep-learning tools manifest huge potential in automating and advancing seismic fault mapping.
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Karimi, Parvaneh, Sergey Fomel, Lesli Wood, and Dallas Dunlap. "Predictive coherence." Interpretation 3, no. 4 (2015): SAE1—SAE7. http://dx.doi.org/10.1190/int-2015-0030.1.
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Detection and interpretation of fault systems and stratigraphic features and the relationship between them are crucial for seismic interpretation and reservoir characterization. To provide better interpretation insight and to be able to extract overlooked features out of seismic data volumes, we have developed a new attribute that detects faults and other discontinuities while handling local nonstationary variations across them. First, we used predictive painting to form a structural prediction of seismic events from neighboring traces (left and right neighboring traces in 2D and neighboring traces in all directions around a reference trace in 3D) according to the local structural slopes. Then, we computed prediction residuals by subtracting each prediction from the original data, and we found the smallest prediction-error interval for each point that best represented discontinuity information at that point. The extracted fault information changed with location (spatially and temporally), and it was nonstationary. Conventional coherence measures operate on a spatial window of neighboring traces and a temporal (vertical) analysis window of samples above and below the analysis point, and they can hardly cope with nonstationarity in fault information. In contrast, in our method, neither temporal nor spatial windows were involved in coherence computation, which allowed us to honor nonstationary changes of fault information and to achieve high resolution in the vertical and lateral directions. To assess the performance of the proposed attribute, we compared it with the conventional coherence attribute over the same data set. The comparison demonstrated the effectiveness of discontinuity detection using predictive coherence and showed its value in extracting additional information from seismic data.
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Mizuno, Osamu, and Michi Nakai. "Can Faulty Modules Be Predicted by Warning Messages of Static Code Analyzer?" Advances in Software Engineering 2012 (May 10, 2012): 1–8. http://dx.doi.org/10.1155/2012/924923.
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We have proposed a detection method of fault-prone modules based on the spam filtering technique, “Fault-prone filtering.” Fault-prone filtering is a method which uses the text classifier (spam filter) to classify source code modules in software. In this study, we propose an extension to use warning messages of a static code analyzer instead of raw source code. Since such warnings include useful information to detect faults, it is expected to improve the accuracy of fault-prone module prediction. From the result of experiment, it is found that warning messages of a static code analyzer are a good source of fault-prone filtering as the original source code. Moreover, it is discovered that it is more effective than the conventional method (that is, without static code analyzer) to raise the coverage rate of actual faulty modules.
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Mezentsev, Oleg A., Richard E. DeVor, and Shiv G. Kapoor. "Prediction of Thread Quality by Detection and Estimation of Tapping Faults." Journal of Manufacturing Science and Engineering 124, no. 3 (2002): 643–50. http://dx.doi.org/10.1115/1.1475319.
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A method is proposed for predicting the internal thread height as a function of tapping process faults and relating it to the standardized thread quality tolerances. Based on the mechanistic tapping model [6], a method of estimating the magnitudes of the faults has been proposed. The effects of tap-hole axes misalignment and tap runout on thread height have been revealed by the mechanistic tapping process model. Based on this model and data obtained from the process estimated process fault values have been used to predict thread height and then predict thread pitch diameter. Using standard tolerances for pitch diameter, an assessment of thread quality can then be made. Validation experiments have been run on several materials for different combinations of process faults. Results of thread measurements showed excellent correspondence with the model predictions.
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Fontes Godoy, Wagner, Daniel Morinigo-Sotelo, Oscar Duque-Perez, Ivan Nunes da Silva, Alessandro Goedtel, and Rodrigo Henrique Cunha Palácios. "Estimation of Bearing Fault Severity in Line-Connected and Inverter-Fed Three-Phase Induction Motors." Energies 13, no. 13 (2020): 3481. http://dx.doi.org/10.3390/en13133481.
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This paper addresses a comprehensive evaluation of a bearing fault evolution and its consequent prediction concerning the remaining useful life. The proper prediction of bearing faults in their early stage is a crucial factor for predictive maintenance and mainly for the production management schedule. The detection and estimation of the progressive evolution of a bearing fault are performed by monitoring the amplitude of the current signals at the time domain. Data gathered from line-fed and inverter-fed three-phase induction motors were used to validate the proposed approach. To assess classification accuracy and fault estimation, the models described in this paper are investigated by using Artificial Neural Networks models. The paper also provides process flowcharts and classification tables to present the prognostic models used to estimate the remaining useful life of a defective bearing. Experimental results confirmed the method robustness and provide an accurate diagnosis regardless of the bearing fault stage, motor speed, load level, and type of supply.
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Chouiref, Houda, Boumedyen Boussaid, Mohamed Naceur Abdelkrim, Vicenç Puig, and Christophe Aubrun. "Integrated FDI/FTC approach for wind turbines using a LPV interval predictor subspace approach and virtual sensors/actuators." Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy 235, no. 6 (2021): 1527–43. http://dx.doi.org/10.1177/09576509211002080.
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In order to keep wind turbines connected and in operation at all times despite the occurrence of some faults, advanced fault detection and accommodation schemes are required. To achieve this goal, this paper proposes to use the Linear Parameter Varying approach to design an Active Fault Tolerant Control for wind turbines. This Active Fault Tolerant Control is integrated with a Fault Detection and Isolation approach. Fault detection is based on a Linear Parameter Varying interval predictor approach while fault isolation is based on analysing the residual fault signatures. To include fault-tolerance in the control system (already available in the considered wind turbine case study based on the well known SAFEPROCESS benchmark), the information of the Fault Detection and Isolation approach block is exploited and it is used in the implementation of a virtual actuator and sensor scheme. The proposed Active Fault Tolerant Control is evaluated using fault scenarios which are proposed in the wind turbine benchmark to assess its performance. Results show the effectiveness of the proposed Active Fault Tolerant Control approach in faulty situation.
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Poddar, Surojit, and Naresh Tandon. "Classification and detection of cavitation, particle contamination and oil starvation in journal bearing through machine learning approach using acoustic emission signals." Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology 235, no. 10 (2021): 2137–43. http://dx.doi.org/10.1177/1350650121991316.
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The ability to classify condition-monitoring data and make a decision can be imparted to a computer through the machine learning approach. In this article, the acoustic emission signals emerging from journal bearings under normal operating conditions and faulty states, namely cavitation, particle contamination and oil starvation, have been classified to develop fault-prediction model using the machine learning approach. Furthermore, an application has been developed that takes acoustic emission data as input and diagnoses the category of faults besides triggering an alarm under faulty states.
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Li, N., R. Zhou, and X. Z. Zhao. "Mechanical faulty signal denoising using a redundant non-linear second-generation wavelet transform." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 225, no. 4 (2011): 799–808. http://dx.doi.org/10.1243/09544062jmes2410.
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Denoising and extraction of the weak signals are crucial to mechanical equipment fault diagnostics, especially for early fault detection, in which cases fault features are very weak and masked by the noise. The wavelet transform has been widely used in mechanical faulty signal denoising due to its extraordinary timefrequency representation capability. However, the mechanical faulty signals are often non-stationary, with the structure varying significantly within each scale. Because a single wavelet filter cannot mimic the signal structure of an entire scale, the traditional wavelet-based signal denoising method cannot achieve an ideal effect, and even worse some faulty information of the raw signal may be lost in the denoising process. To overcome this deficiency, a novel mechanical faulty signal denoising method using a redundant non-linear second generation wavelet transform is proposed. In this method, an optimal prediction operator is selected for each transforming sample according to the selection criterion of minimizing each individual prediction error. Consequently, the selected predictor can always fit the local characteristics of the signals. The signal denoising results from both simulated signals and experimental data are presented and both support the proposed method.
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Li, Xiaochuan, Xiaoyu Yang, Yingjie Yang, Ian Bennett, and David Mba. "An intelligent diagnostic and prognostic framework for large-scale rotating machinery in the presence of scarce failure data." Structural Health Monitoring 19, no. 5 (2019): 1375–90. http://dx.doi.org/10.1177/1475921719884019.
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In this work, a novel diagnostic and prognostic framework is proposed to detect faults and predict remaining service life of large-scale rotating machinery in the presence of scarce failure data. In the proposed framework, a canonical variate residuals–based diagnostic method is developed to facilitate remaining service life prediction by continuously implementing detection of the prediction start time. A novel two-step prognostic feature exploring approach that involves fault identification, feature extraction, feature selection and multi-feature fusion is put forward. Most existing prognostic methods lack a fault-identification module to automatically identify the fault root-cause variables required in the subsequent prognostic analysis and decision-making process. The proposed prognostic feature exploring method overcomes this challenge by introducing a canonical variate residuals–based fault-identification method. With this method, the most representative degradation features are extracted from only the fault root-cause variables, thereby facilitating machinery prognostics by ensuring accurate estimates. Its effectiveness is demonstrated for slow involving faults in two case studies of an operational industrial centrifugal pump. Moreover, an enhanced grey model approach is developed for remaining useful life prediction. In particular, the empirical Bayesian algorithm is employed to improve the traditional grey forecasting model in terms of quantifying the uncertainty of remaining service life in a probabilistic form and improving its prediction accuracy. To demonstrate the superiority of empirical Bayesian–grey model, existing prognostic methods such as grey model, particle filter–grey model and empirical Bayesian–exponential regression are also utilized to realize machinery remaining service life prediction, and the results are compared with that of the proposed method. The achieved predictive accuracy shows that the proposed approach outperforms its counterparts and is highly applicable in fault prognostics of industrial rotating machinery. The use of in-service data in a practical scenario shows that the proposed prognostic approach is a promising tool for online health monitoring.
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Suresh, Yeresime, Lov Kumar, and Santanu Ku Rath. "Statistical and Machine Learning Methods for Software Fault Prediction Using CK Metric Suite: A Comparative Analysis." ISRN Software Engineering 2014 (March 4, 2014): 1–15. http://dx.doi.org/10.1155/2014/251083.
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Experimental validation of software metrics in fault prediction for object-oriented methods using statistical and machine learning methods is necessary. By the process of validation the quality of software product in a software organization is ensured. Object-oriented metrics play a crucial role in predicting faults. This paper examines the application of linear regression, logistic regression, and artificial neural network methods for software fault prediction using Chidamber and Kemerer (CK) metrics. Here, fault is considered as dependent variable and CK metric suite as independent variables. Statistical methods such as linear regression, logistic regression, and machine learning methods such as neural network (and its different forms) are being applied for detecting faults associated with the classes. The comparison approach was applied for a case study, that is, Apache integration framework (AIF) version 1.6. The analysis highlights the significance of weighted method per class (WMC) metric for fault classification, and also the analysis shows that the hybrid approach of radial basis function network obtained better fault prediction rate when compared with other three neural network models.
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Grimaldi, Reginaldo B. G., Talita S. A. Chagas, Jugurta Montalvão, Núbia S. D. Brito, Wellinsílvio C. dos Santos, and Tarso V. Ferreira. "High impedance fault detection based on linear prediction." Electric Power Systems Research 190 (January 2021): 106846. http://dx.doi.org/10.1016/j.epsr.2020.106846.
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Li, Jian Jun, Zhi Yi Wang, and Dong Zheng. "Fault Prediction in Air-Conditioning Refrigeration System by Wavelet Transform." Advanced Materials Research 614-615 (December 2012): 428–31. http://dx.doi.org/10.4028/www.scientific.net/amr.614-615.428.
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A good deal of electricity consumption can be attributed to air-conditioning refrigeration systems. The percentage can be significantly higher if a cooling system is operating at low performance levels due to the presence of faults. The wavelet transform moves data from a time domain to a frequency domain with the wavelet as the basic function giving the localized features of the original signal in the fault detection. It is well known for its capability of treating the transient or time-related varying signals. The fault of heat load increase of the air-conditioning room can be predicted by wavelet transform through the test. Fault prediction in air-conditioning refrigeration system by wavelet transform can avoid defects of the conventional methods.
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Safavi, Saeid, Mohammad Amin Safavi, Hossein Hamid, and Saber Fallah. "Multi-Sensor Fault Detection, Identification, Isolation and Health Forecasting for Autonomous Vehicles." Sensors 21, no. 7 (2021): 2547. http://dx.doi.org/10.3390/s21072547.
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The primary focus of autonomous driving research is to improve driving accuracy and reliability. While great progress has been made, state-of-the-art algorithms still fail at times and some of these failures are due to the faults in sensors. Such failures may have fatal consequences. It therefore is important that automated cars foresee problems ahead as early as possible. By using real-world data and artificial injection of different types of sensor faults to the healthy signals, data models can be trained using machine learning techniques. This paper proposes a novel fault detection, isolation, identification and prediction (based on detection) architecture for multi-fault in multi-sensor systems, such as autonomous vehicles.Our detection, identification and isolation platform uses two distinct and efficient deep neural network architectures and obtained very impressive performance. Utilizing the sensor fault detection system’s output, we then introduce our health index measure and use it to train the health index forecasting network.
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Parzinger, Michael, Ulrich Wellisch, Lucia Hanfstaengl, Ferdinand Sigg, Markus Wirnsberger, and Uli Spindler. "Identifying faults in the building system based on model prediction and residuum analysis." E3S Web of Conferences 172 (2020): 22001. http://dx.doi.org/10.1051/e3sconf/202017222001.
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The energy efficiency of the building HVAC systems can be improved when faults in the running system are known. To this day, there are no cost-efficient, automatic methods that detect faults of the building HVAC systems to a satisfactory degree. This study induces a new method for fault detection that can replace a graphical, user-subjective evaluation of a building data measured on site with an automatic, data-based approach. This method can be a step towards cost-effective monitoring. For this research, the data from a detailed simulation of a residential case study house was used to compare a faultless operation of a building with a faulty operation. We argue that one can detect faults by analysing the properties of residuals of the prediction to the actual data. A machine learning model and an ARX model predict the building operation, and the method employs various statistical tests such as the Sign Test, the Turning Point Test, the Box-Pierce Test and the Bartels-Rank Test. The results show that the amount of data, the type and density of system faults significantly affect the accuracy of the prediction of faults. It became apparent that the challenge is to find a decision rule for the best combination of statistical tests on residuals to predict a fault.
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Xiao, Cheng, Zuojun Liu, Tieling Zhang, and Xu Zhang. "Deep Learning Method for Fault Detection of Wind Turbine Converter." Applied Sciences 11, no. 3 (2021): 1280. http://dx.doi.org/10.3390/app11031280.
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The converter is an important component in wind turbine power drive-train systems, and usually, it has a higher failure rate. Therefore, detecting the potential faults for prediction of its failure has become indispensable for condition-based maintenance and operation of wind turbines. This paper presents an approach to wind turbine converter fault detection using convolutional neural network models which are developed by using wind turbine Supervisory Control and Data Acquisition (SCADA) system data. The approach starts with the selection of fault indicator variables, and then the fault indicator variables data are extracted from a wind turbine SCADA system. Using the data, radar charts are generated, and the convolutional neural network models are applied to feature extraction from the radar charts and characteristic analysis of the feature for fault detection. Based on the analysis of the Octave Convolution (OctConv) network structure, an improved AOctConv (Attention Octave Convolution) structure is proposed in this paper, and it is applied to the ResNet50 backbone network (named as AOC–ResNet50). It is found that the algorithm based on AOC–ResNet50 overcomes the issues of information asymmetry caused by the asymmetry of the sampling method and the damage to the original features in the high and low frequency domains by the OctConv structure. Finally, the AOC–ResNet50 network is employed for fault detection of the wind turbine converter using 10 min SCADA system data. It is verified that the fault detection accuracy using the AOC–ResNet50 network is up to 98.0%, which is higher than the fault detection accuracy using the ResNet50 and Oct–ResNet50 networks. Therefore, the effectiveness of the AOC–ResNet50 network model in wind turbine converter fault detection is identified. The novelty of this paper lies in a novel AOC–ResNet50 network proposed and its effectiveness in wind turbine fault detection. This was verified through a comparative study on wind turbine power converter fault detection with other competitive convolutional neural network models for deep learning.
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Li, Yao. "A Fault Prediction and Cause Identification Approach in Complex Industrial Processes Based on Deep Learning." Computational Intelligence and Neuroscience 2021 (March 5, 2021): 1–13. http://dx.doi.org/10.1155/2021/6612342.
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Faults occurring in the production line can cause many losses. Predicting the fault events before they occur or identifying the causes can effectively reduce such losses. A modern production line can provide enough data to solve the problem. However, in the face of complex industrial processes, this problem will become very difficult depending on traditional methods. In this paper, we propose a new approach based on a deep learning (DL) algorithm to solve the problem. First, we regard these process data as a spatial sequence according to the production process, which is different from traditional time series data. Second, we improve the long short-term memory (LSTM) neural network in an encoder-decoder model to adapt to the branch structure, corresponding to the spatial sequence. Meanwhile, an attention mechanism (AM) algorithm is used in fault detection and cause identification. Third, instead of traditional biclassification, the output is defined as a sequence of fault types. The approach proposed in this article has two advantages. On the one hand, treating data as a spatial sequence rather than a time sequence can overcome multidimensional problems and improve prediction accuracy. On the other hand, in the trained neural network, the weight vectors generated by the AM algorithm can represent the correlation between faults and the input data. This correlation can help engineers identify the cause of faults. The proposed approach is compared with some well-developed fault diagnosing methods in the Tennessee Eastman process. Experimental results show that the approach has higher prediction accuracy, and the weight vector can accurately label the factors that cause faults.
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Minh, Vu Trieu, Nitin Afzulpurkar, and W. M. Wan Muhamad. "Fault Detection and Control of Process Systems." Mathematical Problems in Engineering 2007 (2007): 1–20. http://dx.doi.org/10.1155/2007/80321.
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This paper develops a stochastic hybrid model-based control system that can determine online the optimal control actions, detect faults quickly in the control process, and reconfigure the controller accordingly using interacting multiple-model (IMM) estimator and generalized predictive control (GPC) algorithm. A fault detection and control system consists of two main parts: the first is the fault detector and the second is the controller reconfiguration. This work deals with three main challenging issues: design of fault model set, estimation of stochastic hybrid multiple models, and stochastic model predictive control of hybrid multiple models. For the first issue, we propose a simple scheme for designing faults for discrete and continuous random variables. For the second issue, we consider and select a fast and reliable fault detection system applied to the stochastic hybrid system. Finally, we develop a stochastic GPC algorithm for hybrid multiple-models controller reconfiguration with soft switching signals based on weighted probabilities. Simulations for the proposed system are illustrated and analyzed.
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Sun, Jian Ping, Ming Gao, and Ya Lun Li. "Fault Prediction Method Research of the Power Plant Fan." Advanced Materials Research 580 (October 2012): 99–104. http://dx.doi.org/10.4028/www.scientific.net/amr.580.99.
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To solve the problem of the power plant fan fault prediction, proposed that combining with neural network method and nonparametric density function estimation methods based on parzen window the estimation to achieve fault detection. To improve the prediction performance of neural network, used PSO method, which can realize weights optimization of the neural network prediction, avoid falling into local optimum. Using sliding time window achieve the multi-step prediction of the neural network, and ensure the prediction accuracy. Then, fault is predicted by prediction residuals through density function estimation and hypothesis test. Finally, by using the vibration fault prediction of the air feeder of a power plant in Shanxi as research object to test this method, the simulation result illustrate this fault prediction algorithm can predict the fault of fan timely and effectively.
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Zhong, Lina, Jianye Liu, Rongbing Li, and Rong Wang. "Approach for Detecting Soft Faults in GPS/INS Integrated Navigation based on LS-SVM and AIME." Journal of Navigation 70, no. 3 (2017): 561–79. http://dx.doi.org/10.1017/s037346331600076x.
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In life-critical applications, the real-time detection of faults is very important in Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation systems. A new fault detection method for soft fault detection is developed in this paper with the purpose of improving real-time performance. In general, the innovation information obtained from a Kalman filter is used for test statistic calculations in Autonomous Integrity Monitored Extrapolation (AIME). However, the innovation of the Kalman filter is degraded by error tracking and closed-loop correction effects, leading to time delays in soft fault detection. Therefore, the key issue of improving real-time performance is providing accurate innovation to AIME. In this paper, the proposed algorithm incorporates Least Squares-Support Vector Machine (LS-SVM) regression theory into AIME. Because the LS-SVM has a good regression and prediction performance, the proposed method provides replaced innovation obtained from the LS-SVM driven by real-time observation data. Based on the replaced innovation, the test statistics can follow fault amplitudes more accurately; finally, the real-time performance of soft fault detection can be improved. Theoretical analysis and physical simulations demonstrate that the proposed method can effectively improve the detection instantaneity.
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Kazemi, Pezhman, Jaume Giralt, Christophe Bengoa, and Jean-Philippe Steyer. "Data-driven fault detection methods for detecting small-magnitude faults in anaerobic digestion process." Water Science and Technology 81, no. 8 (2020): 1740–48. http://dx.doi.org/10.2166/wst.2020.026.
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Abstract Early detection of small-magnitude faults in anaerobic digestion (AD) processes is a mandatory step for preventing serious consequence in the future. Since volatile fatty acids (VFA) accumulation is widely suggested as a process health indicator, a VFA soft-sensor was developed based on support vector machine (SVM) and used for generating the residuals by comparing real and predicted VFA. The estimated residual signal was applied to univariate statistical control charts such as cumulative sum (CUSUM) and square prediction error (SPE) to detect the faults. A principal component analysis (PCA) model was also developed for comparison with the aforementioned approach. The proposed framework showed excellent performance for detecting small-magnitude faults in the state parameters of AD processes.
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Alsukhni, Emad, Ahmad A. Saifan, and Hanadi Alawneh. "A New Data Mining-Based Framework to Test Case Prioritization Using Software Defect Prediction." International Journal of Open Source Software and Processes 8, no. 1 (2017): 21–41. http://dx.doi.org/10.4018/ijossp.2017010102.
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Test cases do not have the same importance when used to detect faults in software; therefore, it is more efficient to test the system with the test cases that have the ability to detect the faults. This research proposes a new framework that combines data mining techniques to prioritize the test cases. It enhances fault prediction and detection using two different techniques: 1) the data mining regression classifier that depends on software metrics to predict defective modules, and 2) the k-means clustering technique that is used to select and prioritize test cases to identify the fault early. Our approach of test case prioritization yields good results in comparison with other studies. The authors used the Average Percentage of Faults Detection (APFD) metric to evaluate the proposed framework, which results in 19.9% for all system modules and 25.7% for defective ones. Our results give us an indication that it is effective to start the testing process with the most defective modules instead of testing all modules arbitrary arbitrarily.
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Wang, Guozhong. "Substation DC system grounding fault prediction method." E3S Web of Conferences 252 (2021): 01036. http://dx.doi.org/10.1051/e3sconf/202125201036.
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There may be disturbance and uncertainty in the collection of leakage current in DC system of substation, which leads to the decrease of accuracy and increase of prediction error. Based on this, an improved grey prediction method is proposed to predict DC system branch grounding fault. Firstly, the characteristics of DC system ground fault parameters are collected. Secondly, the improved grey prediction algorithm is used to predict and estimate whether the detection reaches the fault threshold in the future. Finally, the validity of the proposed method is verified by MATLAB modeling.
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Alkharabsheh, Abdel Rahman, Lina Momani, Waleed Al-Nuaimy, Jafar Ababneh, Tariq Alwada’n, and Abeer Hawatmeh. "Early fault prediction and detection of hydrocephalus shunting system." Journal of Biomedical Science and Engineering 06, no. 03 (2013): 280–90. http://dx.doi.org/10.4236/jbise.2013.63036.
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Mechbal, N., and M. Vergé. "H 2 Polynomial Filtering and Prediction for Fault Detection." IFAC Proceedings Volumes 30, no. 18 (1997): 863–68. http://dx.doi.org/10.1016/s1474-6670(17)42508-0.
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Xu, Feng, Vicenç Puig, Carlos Ocampo-Martinez, Sorin Olaru, and Silviu-Iulian Niculescu. "Robust Mpc for Actuator–Fault Tolerance Using Set–Based Passive Fault Detection and Active Fault Isolation." International Journal of Applied Mathematics and Computer Science 27, no. 1 (2017): 43–61. http://dx.doi.org/10.1515/amcs-2017-0004.
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Abstract In this paper, a fault-tolerant control (FTC) scheme is proposed for actuator faults, which is built upon tube-based model predictive control (MPC) as well as set-based fault detection and isolation (FDI). In the class of MPC techniques, tubebased MPC can effectively deal with system constraints and uncertainties with relatively low computational complexity compared with other robust MPC techniques such as min-max MPC. Set-based FDI, generally considering the worst case of uncertainties, can robustly detect and isolate actuator faults. In the proposed FTC scheme, fault detection (FD) is passive by using invariant sets, while fault isolation (FI) is active by means of MPC and tubes. The active FI method proposed in this paper is implemented by making use of the constraint-handling ability of MPC to manipulate the bounds of inputs. After the system has been detected to become faulty, the input-constraint set of the MPC controller is adjusted to actively excite the system for achieving FI guarantees on-line, where an active FI-oriented input set is designed off-line. In this way, the system can be excited in order to obtain more extra system-operating information for FI than passive FI approaches. Overall, the objective of this paper is to propose an actuator MPC scheme with as little as possible of FI conservatism and computational complexity by combining tube-based MPC and set theory within the framework of MPC, respectively. Finally, a case study is used to show the effectiveness of the proposed FTC scheme.
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Munirathinam, Sathyan, and Balakrishnan Ramadoss. "Predictive Models for Equipment Fault Detection in the Semiconductor Manufacturing Process." International Journal of Engineering and Technology 8, no. 4 (2016): 273–85. http://dx.doi.org/10.7763/ijet.2016.v6.898.
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Munirathinam, Sathyan, and Balakrishnan Ramadoss. "Predictive Models for Equipment Fault Detection in the Semiconductor Manufacturing Process." International Journal of Engineering and Technology 8, no. 4 (2016): 273–85. http://dx.doi.org/10.7763/ijet.2016.v8.898.
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Zhou, Funa, Jiayu Wang, and Yulin Gao. "DCA-Based Real-Time Residual Useful Life Prediction for Critical Faulty Component." Journal of Control Science and Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/8492139.
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Residual useful life (RUL) prediction is significant for condition-based maintenance. Traditional data-driven RUL prediction method can only predict fault trend of the system rather than RUL of a specific system component. Thus it cannot tell the operator which component should be maintained. The innovation of this paper is as follows: (1) Wavelet filtering based method is developed for early detection of slowly varying fault. (2) Designated component analysis is introduced as a feature extraction tool to define the fault precursor of a specific component. (3) Exponential life prediction model is established by nonlinear fitting of the historical RUL and the fault size characterized by the statistics used. Once online detection statistics is obtained, real-time RUL of the critical component can be predicted online. Simulation shows the effectiveness of this algorithm.
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Vidal, Yolanda, Francesc Pozo, and Christian Tutivén. "Wind Turbine Multi-Fault Detection and Classification Based on SCADA Data." Energies 11, no. 11 (2018): 3018. http://dx.doi.org/10.3390/en11113018.
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Due to the increasing installation of wind turbines in remote locations, both onshore and offshore, advanced fault detection and classification strategies have become crucial to accomplish the required levels of reliability and availability. In this work, without using specific tailored devices for condition monitoring but only increasing the sampling frequency in the already available (in all commercial wind turbines) sensors of the Supervisory Control and Data Acquisition (SCADA) system, a data-driven multi-fault detection and classification strategy is developed. An advanced wind turbine benchmark is used. The wind turbine we consider is subject to different types of faults on actuators and sensors. The main challenges of the wind turbine fault detection lie in their non-linearity, unknown disturbances, and significant measurement noise at each sensor. First, the SCADA measurements are pre-processed by group scaling and feature transformation (from the original high-dimensional feature space to a new space with reduced dimensionality) based on multiway principal component analysis through sample-wise unfolding. Then, 10-fold cross-validation support vector machines-based classification is applied. In this work, support vector machines were used as a first choice for fault detection as they have proven their robustness for some particular faults, but at the same time have never accomplished the detection and classification of all the proposed faults considered in this work. To this end, the choice of the features as well as the selection of data are of primary importance. Simulation results showed that all studied faults were detected and classified with an overall accuracy of 98.2%. Finally, it is noteworthy that the prediction speed allows this strategy to be deployed for online (real-time) condition monitoring in wind turbines.
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Song, Junho, Woojin Ahn, Sangkyoo Park, and Myotaeg Lim. "Failure Detection for Semantic Segmentation on Road Scenes Using Deep Learning." Applied Sciences 11, no. 4 (2021): 1870. http://dx.doi.org/10.3390/app11041870.
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Detecting failure cases is an essential element for ensuring the safety self-driving system. Any fault in the system directly leads to an accident. In this paper, we analyze the failure of semantic segmentation, which is crucial for autonomous driving system, and detect the failure cases of the predicted segmentation map by predicting mean intersection of union (mIoU). Furthermore, we design a deep neural network for predicting mIoU of segmentation map without the ground truth and introduce a new loss function for training imbalance data. The proposed method not only predicts the mIoU, but also detects failure cases using the predicted mIoU value. The experimental results on Cityscapes data show our network gives prediction accuracy of 93.21% and failure detection accuracy of 84.8%. It also performs well on a challenging dataset generated from the vertical vehicle camera of the Hyundai Motor Group with 90.51% mIoU prediction accuracy and 83.33% failure detection accuracy.
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Wang, Xiaoqian, Dali Sheng, Jinlian Deng, et al. "Kernel Regression Residual Decomposition Method to Detect Rolling Element Bearing Faults." Mathematical Problems in Engineering 2021 (April 28, 2021): 1–10. http://dx.doi.org/10.1155/2021/5523098.
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The raw vibration signal carries a great deal of information representing the mechanical equipment's health conditions. However, in the working condition, the vibration response signals of faulty components are often characterized by the presence of different kinds of impulses, and the corresponding fault features are always immersed in heavy noises. Therefore, signal denoising is one of the most important tasks in the fault detection of mechanical components. As a time-frequency signal processing technique without the support of the strictly mathematical theory, empirical mode decomposition (EMD) has been widely applied to detect faults in mechanical systems. Kernel regression (KR) is a well-known nonparametric mathematical tool to construct a prediction model with good performance. Inspired by the basic idea of EMD, a new kernel regression residual decomposition (KRRD) method is proposed. Nonparametric Nadaraya–Watson KR and a standard deviation (SD) criterion are employed to generate a deep cascading framework including a series of high-frequency terms denoted by residual signals and a final low-frequency term represented by kernel regression signal. The soft thresholding technique is then applied to each residual signal to suppress noises. To illustrate the feasibility and the performance of the KRRD method, a numerical simulation and the faulty rolling element bearings of well-known open access data as well as the experimental investigations of the machinery simulator are performed. The fault detection results show that the proposed method enables the recognition of faults in mechanical systems. It is expected that the KRRD method might have a similar application prospect of EMD.
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Calabrese, Francesca, Alberto Regattieri, Lucia Botti, Cristina Mora, and Francesco Gabriele Galizia. "Unsupervised Fault Detection and Prediction of Remaining Useful Life for Online Prognostic Health Management of Mechanical Systems." Applied Sciences 10, no. 12 (2020): 4120. http://dx.doi.org/10.3390/app10124120.
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Predictive maintenance allows industries to keep their production systems available as much as possible. Reducing unforeseen shutdowns to a level that is close to zero has numerous advantages, including production cost savings, a high quality level of both products and processes, and a high safety level. Studies in this field have focused on a novel approach, prognostic health management (PHM), which relies on condition monitoring (CM) for predicting the remaining useful life (RUL) of a system. However, several issues remain in its application to real industrial contexts, e.g., the difficulties in conducting tests simulating each fault condition, the dynamic nature of industrial environments, and the need to handle large amounts of data collected from machinery. In this paper, a data-driven methodology for PHM implementation is proposed, which has the following characteristics: it is unsupervised, i.e., it does not require any prior knowledge regarding fault behaviors and it does not rely on pre-trained classification models, i.e., it can be applied “from scratch”; it can be applied online due to its low computational effort, which makes it suitable for edge computing; and, it includes all of the steps that are involved in a prognostic program, i.e., feature extraction, health indicator (HI) construction, health stage (HS) division, degradation modelling, and RUL prediction. Finally, the proposed methodology is applied in this study to a rotating component. The study results, in terms of the ability of the proposed approach to make a timely prediction of component fault conditions, are promising.
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Yuan, Tongke, Zhifeng Sun, and Shihao Ma. "Gearbox Fault Prediction of Wind Turbines Based on a Stacking Model and Change-Point Detection." Energies 12, no. 22 (2019): 4224. http://dx.doi.org/10.3390/en12224224.
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The fault diagnosis and prediction technology of wind turbines are of great significance for increasing the power generation and reducing the downtime of wind turbines. However, most of the current fault detection approaches are realized by setting a single alarm threshold. Considering the complicated working conditions of wind farms, such methods are prone to ignore the fault, send out a false alarm, or leave insufficient troubleshooting time. In this work, we propose a gearbox fault prediction approach of wind turbines based on the supervisory control and data acquisition (SCADA) data. A stacking model composed of Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Extreme Gradient Boosting (XGBOOST) was constructed as the normal behavior model to describe the normal conditions of the wind turbines. We used the Mahalanobis distance (MD) instead of the residual to measure the deviation of the current state from the normal conditions of the turbines. By inputting the MD series into the proposed change-point detection algorithm, we can obtain the change point at which the fault symptom begins to appear, and thus achieving the fault prediction of the gearbox. The proposed approach is validated on the historical data of 5 wind turbines in a wind farm, which proves its effectiveness to detect the fault in advance.