Relevant bibliographies by topics / Fault diagnosis

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Fault diagnosis'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2025

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Journal articles on the topic "Fault diagnosis"

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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Dissertations / Theses on the topic "Fault diagnosis"

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Fan, Xiaoxin. "Fault diagnosis of VLSI designs: cell internal faults and volume diagnosis throughput." Diss., University of Iowa, 2012. https://ir.uiowa.edu/etd/3450.

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The modern VLSI circuit designs manufactured with advanced technology nodes of 65nm or below exhibit an increasing sensitivity to the variations of manufacturing process. New design-specific and feature-sensitive failure mechanisms are on the rise. Systematic yield issues can be severe due to the complex variability involved in process and layout features. Without improved yield analysis methods, time-to-market is delayed, mature yield is suboptimal, and product quality may suffer, thereby undermining the profitability of the semiconductor company. Diagnosis-driven yield improvement is a methodology that leverages production test results, diagnosis results, and statistical analysis to identify the root cause of yield loss and fix the yield limiters to improve the yield. To fully leverage fault diagnosis, the diagnosis-driven yield analysis requires that the diagnosis tool should provide high-quality diagnosis results in terms of accuracy and resolution. In other words, the diagnosis tool should report the real defect location without too much ambiguity. The second requirement for fast diagnosis-driven yield improvement is that the diagnosis tool should have the capability of processing a volume of failing dies within a reasonable time so that the statistical analysis can have enough information to identify the systematic yield issues. In this dissertation, we first propose a method to accurately diagnose the defects inside the library cells when multi-cycle test patterns are used. The methods to diagnose the interconnect defect have been well studied for many years and are successfully practiced in industry. However, for process technology at 90nm or 65nm or below, there is a significant number of manufacturing defects and systematic yield limiters lie inside library cells. The existing cell internal diagnosis methods work well when only combinational test patterns are used, while the accuracy drops dramatically with multi-cycle test patterns. A method to accurately identify the defective cell as well as the failing conditions is presented. The accuracy can be improved up to 94% compared with about 75% accuracy for previous proposed cell internal diagnosis methods. The next part of this dissertation addresses the throughput problem for diagnosing a volume of failing chips with high transistor counts. We first propose a static design partitioning method to reduce the memory footprint of volume diagnosis. A design is statically partitioned into several smaller sub-circuits, and then the diagnosis is performed only on the smaller sub-circuits. By doing this, the memory usage for processing the smaller sub-circuit can be reduced and the throughput can be improved. We next present a dynamic design partitioning method to improve the throughput and minimize the impact on diagnosis accuracy and resolution. The proposed dynamic design partitioning method is failure dependent, in other words, each failure file has its own design partition. Extensive experiments have been designed to demonstrate the efficiency of the proposed dynamic partitioning method.
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Hurdle, Emma Eileen. "System fault diagnosis using fault tree analysis." Thesis, Loughborough University, 2008. https://dspace.lboro.ac.uk/2134/34678.

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Fault tree analysis is a method that describes all possible causes of a specified system state in terms of the state of the components within the system. Fault trees are commonly developed to analyse the adequacy of systems, from a reliability or safety point of view during the stages of design. The aim of the research presented in this thesis was to develop a method for diagnosing faults in systems using a model-based fault tree analysis approach, taking into consideration the potential for use on aircraft systems. Initial investigations have been conducted by developing four schemes that use coherent and non-coherent fault trees, the concepts of which are illustrated by applying the techniques to a simple system. These were used to consider aspects of system performance for each scheme at specified points in time. The results obtained were analysed and a critical appraisal of the findings carried out to determine the individual effectiveness of each scheme. A number of issues were highlighted from the first part of research, including the need to consider dynamics of the system to improve the method. The most effective scheme from the initial investigations was extended to take into account system dynamics through the development of a pattern recognition technique. Transient effects, including time history of flows and rate of change of fluid level were considered. The established method was then applied to a theoretical version of the BAE Systems fuel rig to investigate how the method could be utilised on a larger system. The fault detection was adapted to work with an increased number of fuel tanks and other components adding to the system complexity. The implications of expanding the method to larger systems such as a full aircraft fuel system were identified for the Nimrod MRA4.
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Pavlidis, Antonios. "Analog Hardware Fault Diagnosis." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS452.

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Le nombre de circuits intégrés (CIs) utilisés dans les applications liées à des missions critiques et à la sûreté augmente sans cesse. Ces applications imposent aux CIs de présenter des propriétés de sûreté fonctionnelle. Cette thèse introduit un auto-test intégré (BIST) pour les CIs analogiques et à signaux mixtes, appelé autotest à symétrie (SymBIST) pour répondre à l’objectif de sûreté fonctionnelle. SymBIST repose sur le principe du BIST et sur l'existence de signaux invariants en fonctionnement nominal et variant en cas de fonctionnement erroné. Les invariants sont mesurés à l'aide de dispositifs intégrés spécifiques. SymBIST répond à trois objectifs de sûreté fonctionnelle : le test les défauts du CI, le test en ligne, et le diagnostic les défauts. SymBIST est démontré sur un convertisseur analogique-numérique à approximations successives (CAN SAR). Les résultats montent que la couverture de test et la précision de diagnostic sont plus élevées que l’état de l’art
The number of integrated circuits (ICs) used in safety- and mission-critical applications is ever increasing. These applications demand that ICs carry functional safety properties. In this thesis, we develop a Built-In Self Test (BIST) approach for Analog and Mixed-Signal (A/M-S) ICs, called Symmetry-Based Built-In Self Test (SymBIST), which achieves several objectives towards the functional safety goal. SymBIST is a generic BIST paradigm based on identifying inherent invariances that should hold true only in error-free operation, while their violation points to abnormal operation. The invariances are being checked using dedicated on-die checkers. SymBIST meets three functional safety objectives: post-manufacturing defect-oriented test, on-line testing, and fault diagnosis. SymBIST is demonstrated on a successive approximation analog-to-digital converter (SAR ADC). The results show that the test coverage and diagnostic accuracy are promising compared to the state of the art
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Frisk, Erik. "Residual generation for fault diagnosis." Doctoral thesis, Linköping : Univ, 2001. http://www.bibl.liu.se/liupubl/disp/disp2001/tek716s.pdf.

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Edwards, S. "Fault diagnosis of rotating machinery." Thesis, Swansea University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.636771.

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In this thesis, topics of importance to the fault diagnosis of rotating machinery in the power generation industry have been addressed, including a review of the relevant literature and an overview of the associated rotordynamics modelling and analysis techniques. For faults involving rotor-stator interaction it has been shown that the inclusion of torsion in mathematical models used for rotor-stator contract analyses can have a significant influence on the dynamic behaviour of the system. A 3 degrees-of-freedom model based on the Jeffcott rotor was developed and, for physically realistic systems, it was shown that very different results were obtained when including torsion, compared to when torsion was neglected, as has generally been the case in the past. An identification method for estimating both the excitation and flexible support parameters of a rotor-bearings-foundations system has been presented. Excitation due to both mass unbalance and a bent rotor were included in the analysis, which has been verified both in simulation and experimentally. The method has great practical potential, since it allows balancing to be performed using data obtained from just a single run-up or run-down, which has obvious benefits for field balancing. Using this single-shot balancing technique in experiment, vibration levels were successfully reduced by as much as 92% of their original levels. A bent rotor has been accurately identified in both simulation and experiment. It was also shown that including bend identification in those cases where only unbalance forcing was present in no way detracted from the accuracy of the estimated unbalance or foundation parameters. The identification of the flexible foundation parameters was generally successful, with measured and estimated parameters matching very closely in most cases. The identification method was tested for a wide range of conditions and proved suitably robust to changes in the system configuration, noisy data and modelling error.
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Adam, Johan D. "Failure diagnostic expert systems : a case study in fault diagnosis /." Master's thesis, This resource online, 1991. http://scholar.lib.vt.edu/theses/available/etd-01202010-020148/.

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ZHANG, XIAODONG. "FAULT DIAGNOSIS AND FAULT-TOLERANT CONTROL IN NONLINEAR SYSTEMS." University of Cincinnati / OhioLINK, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1021937028.

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Avram, Remus C. "Fault Diagnosis and Fault-Tolerant Control of Quadrotor UAVs." Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1464343320.

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Pernestål, Anna. "Probabilistic Fault Diagnosis with Automotive Applications." Doctoral thesis, Linköpings universitet, Fordonssystem, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-51931.

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The aim of this thesis is to contribute to improved diagnosis of automotive vehicles. The work is driven by case studies, where problems and challenges are identified. To solve these problems, theoretically sound and general methods are developed. The methods are then applied to the real world systems. To fulfill performance requirements automotive vehicles are becoming increasingly complex products. This makes them more difficult to diagnose. At the same time, the requirements on the diagnosis itself are steadily increasing. Environmental legislation requires that smaller deviations from specified operation must be detected earlier. More accurate diagnostic methods can be used to reduce maintenance costs and increase uptime. Improved diagnosis can also reduce safety risks related to vehicle operation. Fault diagnosis is the task of identifying possible faults given current observations from the systems. To do this, the internal relations between observations and faults must be identified. In complex systems, such as automotive vehicles, finding these relations is a most challenging problem due to several sources of uncertainty. Observations from the system are often hidden in considerable levels of noise. The systems are complicated to model both since they are complex and since they are operated in continuously changing surroundings. Furthermore, since faults typically are rare, and sometimes never described, it is often difficult to get hold of enough data to learn the relations from. Due to the several sources of uncertainty in fault diagnosis of automotive systems, a probabilistic approach is used, both to find the internal relations, and to identify the faults possibly present in the system given the current observations. To do this successfully, all available information is integrated in the computations. Both on-board and off-board diagnosis are considered. The two tasks may seem different in nature: on-board diagnosis is performed without human integration, while the off-board diagnosis is mainly based on the interactivity with a mechanic. On the other hand, both tasks regard the same vehicle, and information from the on-board diagnosis system may be useful also for off-board diagnosis. The probabilistic methods are general, and it is natural to consider both tasks. The thesis contributes in three main areas. First, in Paper 1 and 2, methods are developed for combining training data and expert knowledge of different kinds to compute probabilities for faults. These methods are primarily developed with on-board diagnosis in mind, but are also applicable to off-board diagnosis. The methods are general, and can be used not only in diagnosis of technical system, but also in many other applications, including medical diagnosis and econometrics, where both data and expert knowledge are present. The second area concerns inference in off-board diagnosis and troubleshooting, and the contribution consists in the methods developed in Paper 3 and 4. The methods handle probability computations in systems subject to external interventions, and in particular systems that include both instantaneous and non-instantaneous dependencies. They are based on the theory of Bayesian networks, and include event-driven non-stationary dynamic Bayesian networks (nsDBN) and an efficient inference algorithm for troubleshooting based on static Bayesian networks. The framework of nsDBN event-driven nsDBN is applicable to all kinds of problems concerning inference under external interventions. The third contribution area is Bayesian learning from data in the diagnosis application. The contribution is the comparison and evaluation of five Bayesian methods for learning in fault diagnosis in Paper 5. The special challenges in diagnosis related to learning from data are considered. It is shown how the five methods should be tailored to be applicable to fault diagnosis problems. To summarize, the five papers in the thesis have shown how several challenges in automotive diagnosis can be handled by using probabilistic methods. Handling such challenges with probabilistic methods has a great potential. The probabilistic methods provide a framework for utilizing all information available, also if it is in different forms and. The probabilities computed can be combined with decision theoretic methods to determine the appropriate action after the discovery of reduced system functionality due to faults.
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Brunson, Christopher M. "Matrix converter fault detection and diagnosis." Thesis, University of Nottingham, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.718994.

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With the increased use of power electronics in aerospace, automotive, industrial, and energy generation sectors, the demand for highly reliable and power dense solutions has increased. Taking into account the demands for high reliability and high power density, matrix converters become attractive. With their lack of large bulky DC- Link capacitors, high power densities are possible with capability to operate with high ambient temperatures [7]. Demand for high reliability under tight weight and volume constrains, often it is not possible to have an entirely redundant system. Under these conditions it is desirable that the system continue to operate even under faulty conditions, albeit with diminished performance in some regard. Research has been carried out on the continued operation of a matrix converter during an open- circuit switch failure[8][9]. These methods however assume that a fault detection and diagnosis system was already in place. The behavior of matrix converters under fault conditions are more complex than traditional inverter drive systems, as there is no decoupling through the DC-Link and the matrix converter's clamp circuit also complicates matters. This thesis describes the operation of a matrix converter and the clamp circuit during a open-circuit fault condition and presents a number of methods for fault detection and diagnosis in matrix converters.
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Books on the topic "Fault diagnosis"

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Korbicz, Józef, Zdzisław Kowalczuk, Jan M. Kościelny, and Wojciech Cholewa, eds. Fault Diagnosis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18615-8.

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Autodata, ed. Fault diagnosis. Maidenhead: Autodata, 1993.

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Chen, Tinghuai. Fault Diagnosis and Fault Tolerance. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77179-8.

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Isermann, Rolf. Fault-Diagnosis Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-12767-0.

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Isermann, Rolf. Fault-Diagnosis Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-30368-5.

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Fong, Rebecca Pui Shan. Probabilistic fault diagnosis. Ottawa: National Library of Canada, 2003.

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7

Loveday, George. Electronic fault diagnosis. 2nd ed. Harlow: Longman Scientific & Technical, 1986.

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8

Denton, Tom. Advanced Automotive Fault Diagnosis. Fifth edition. | Abingdon, Oxon; New York, NY: Routledge, 2021.: Routledge, 2020. http://dx.doi.org/10.1201/9780429317781.

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Varga, Andreas. Solving Fault Diagnosis Problems. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51559-5.

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Karmakar, Subrata, Surajit Chattopadhyay, Madhuchhanda Mitra, and Samarjit Sengupta. Induction Motor Fault Diagnosis. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0624-1.

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Book chapters on the topic "Fault diagnosis"

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Chen, Tinghuai. "Computer System Diagnosis and Society Diagnosis." In Fault Diagnosis and Fault Tolerance, 65–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77179-8_2.

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Cholewa, Wojciech, and Jan Maciej Kościelny. "Introduction." In Fault Diagnosis, 3–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18615-8_1.

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Janczak, Andrzej. "Parametric and Neural Network Wiener and Hammerstein Models in Fault Detection and Isolation." In Fault Diagnosis, 381–410. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18615-8_10.

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Kościelny, Jan Maciej, and Michał Syfert. "Application of Fuzzy Logic to Diagnostics." In Fault Diagnosis, 411–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18615-8_11.

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Witczak, Marcin, and Józef Korbicz. "Observers and Genetic Programming in the Identification and Fault Diagnosis of Non-Linear Dynamic Systems." In Fault Diagnosis, 457–509. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18615-8_12.

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Kowalczuk, Zdzisław, and Tomasz Białaszewski. "Genetic Algorithms in the Multi-Objective Optimisation of Fault Detection Observers." In Fault Diagnosis, 511–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18615-8_13.

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Marciniak, Andrzej, and Józef Korbicz. "Pattern Recognition Approach to Fault Diagnostics." In Fault Diagnosis, 557–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18615-8_14.

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Cholewa, Wojciech. "Expert Systems in Technical Diagnostics." In Fault Diagnosis, 591–631. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18615-8_15.

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Ligęza, Antoni. "Selected Methods of Knowledge Engineering in Systems Diagnosis." In Fault Diagnosis, 633–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18615-8_16.

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Moczulski, Wojciech. "Methods of Acquisition of Diagnostic Knowledge." In Fault Diagnosis, 675–718. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18615-8_17.

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Conference papers on the topic "Fault diagnosis"

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He, David, Eric Bechhoefer, Yongzhi Qu, and Brandon Hecke. "Bearing Fault Diagnosis Using A Spectral Average Based Approach." In Vertical Flight Society 70th Annual Forum & Technology Display, 1–9. The Vertical Flight Society, 2014. http://dx.doi.org/10.4050/f-0070-2014-9527.

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The diagnosis of bearing health through the quantification of accelerometer data has been an area of interest for many years and has resulted in numerous signal processing methods and algorithms. In this paper, a new bearing fault diagnostic approach that combines envelope analysis, time synchronous resampling, and spectral averaging of vibration signals is presented. In this method, the accelerometer signal is first digitized simultaneously with tachometer signal acquisition. Then, the digitized vibration signal is band pass filtered to retain the information associated with the bearing defects. Finally, the tachometer signal is used to time synchronously resample the vibration data to compute spectral average and extract condition indicators for bearing fault diagnosis. The method is validated using the vibration output of seeded fault steel bearings on a bearing test rig. The result is an effective approach validated to diagnose all four bearing fault types: inner race, outer race, ball, and cage.
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Loboda, Igor, Sergey Yepifanov, and Yakov Feldshteyn. "An Integrated Approach to Gas Turbine Monitoring and Diagnostics." In ASME Turbo Expo 2008: Power for Land, Sea, and Air. ASMEDC, 2008. http://dx.doi.org/10.1115/gt2008-51449.

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This paper presents an investigation of a conventional gas turbine diagnostic process and its generalization. A usual sequence of diagnostic actions consists of two stages: monitoring (fault detection) and subsequent proper diagnosis (fault identification). Such an approach neither implies fault identification nor uses the information about incipient faults unless the engine is recognized as faulty. In previous investigations for engine steady state operation conditions we addressed diagnostics problems without their relation with the monitoring process. Fault classes were given by samples of patterns generated by a static gas turbine performance model. This fault simulation took into account faults of varying severity including incipient ones. A diagnostic algorithm employed artificial neural networks to identify an actual fault. In the present paper we consider the monitoring and diagnosis as joint processes extending our previous approach over both of them. It is proposed to form two classes for the monitoring using the above-mentioned classes constructed for the diagnosis. A two-shaft industrial gas turbine has been chosen to test the proposed integrated approach to monitoring and diagnosis. A general recommendation following from the presented investigation is to identify faults simultaneously with fault detection. This permits accumulating preliminary diagnoses before the engine faulty condition is detected and a rapid final diagnosis after the fault detection.
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Davison, Craig R., and A. M. Birk. "Automated Fault Diagnosis for Small Gas Turbine Engines." In ASME Turbo Expo 2002: Power for Land, Sea, and Air. ASMEDC, 2002. http://dx.doi.org/10.1115/gt2002-30029.

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In one possible model of distributed power generation a large number of users will operate individual, gas turbine powered, cogeneration systems. These systems will be small, relatively inexpensive, and installed in locations without ready access to gas turbine maintenance experts. Consequently an automated method to monitor the engine and diagnose its health is required. To remain compatible with the low cost of the power system the diagnostics must also be relatively inexpensive to install and operate. Accordingly a minimum number of extra sensors should be used and the analysis performed by a common personal computer system. The current work automates the diagnosis of component faults by comparing the engine’s operating trends to the trends for known faults. This allows the relative percentage chance of each fault occurring to be determined. The likelihood of each fault is then compared, to determine which component is degrading. The technique can be adapted to compare the engines historic operating trend or a single operating point. In this initial work a computer model was used as a test bed and 5 faults were introduced individually. The technique successfully diagnosed the faulty component using either the operating trend or a single operating point.
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Jiang, Hao, Xinyu Ren, and Xiaojian Fang. "Aeroengine Multi-Fault Diagnosis Based on Hierarchical Multi-mode Filtering." In GPPS Xi'an21. GPPS, 2022. http://dx.doi.org/10.33737/gpps21-tc-136.

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Carry out model-based online fault diagnosis for aero-engine sensors, actuators and components. Traditional methods use a set of filters to estimate the current state of the engine, and then process the estimated residuals of each filter to obtain the diagnosis result. For single engine faults, this method has a good diagnostic effect, but when double engine faults are considered, the amount of calculation will greatly increase, and the accuracy and real-time performance of fault diagnosis cannot meet the requirements. In this regard, this paper proposes a fault diagnosis algorithm based on hierarchical multi-mode filtering, which combines the advantages of hybrid Kalman filtering and multi-mode adaptive filtering algorithms, and uses a hierarchical diagnosis architecture for fault diagnosis. First, establish a hybrid Kalman filter bank of sensors, actuators and components, and then layer them. The first layer diagnoses the normal state of the engine and single fault conditions, and the second layer diagnoses the double fault conditions on the basis of the first layer fault diagnosis, and finally outputs the diagnosis results comprehensively. This method can meet the real-time and accuracy requirements for single and double fault diagnosis of the engine.
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Yu, Rui, Xianling Li, Mo Tao, and Zhiwu Ke. "Fault Diagnosis of Feedwater Pump in Nuclear Power Plants Using Parameter-Optimized Support Vector Machine." In 2016 24th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/icone24-60334.

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The condition monitoring of the feedwater pump in secondary circuit is critical to the safe operation of the nuclear power plant. This article presents a fault diagnosis method of feedwater pump by using parameter-optimized support vector machine (SVM). While the fault features of feedwater pump are reflected from the power spectrum of the vibration signals, we trained and diagnosed the fault feature table with support vector machine. The optimal penalty factor C and kernel parameter γ of support vector machine are selected by grid search and k-fold cross validation. Then the faults are diagnosed by the SVM model under the optimal parameters. Diagnostic results show that the parameter-optimized SVM method achieves higher diagnostic accuracy than the PNN method, exhibiting superior performance to effectively diagnose the faults of feedwater pump.
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Aretakis, N., I. Roumeliotis, and K. Mathioudakis. "Performance Model “Zooming” for In-Depth Component Fault Diagnosis." In ASME Turbo Expo 2010: Power for Land, Sea, and Air. ASMEDC, 2010. http://dx.doi.org/10.1115/gt2010-23262.

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A method giving the possibility for a more detailed gas path component fault diagnosis, by exploiting the “zooming” feature of current performance modelling techniques, is presented. A diagnostic engine performance model is the main tool that points to the faulty engine component. A diagnostic component model is then used to identify the fault. The method is demonstrated on the case of compressor faults. A 1-D model based on the “stage stacking” approach is used to “zoom” into the compressors, supporting a 0-D engine model. A first level diagnosis determines the deviation of overall compressor performance parameters, while “zooming” calculations allow a localization of the faulty stages of a multistage compressor. The possibility to derive more detailed information with no additional measurement data is established, by incorporation of empirical knowledge on the type of faults that are usually encountered in practice. Although the approach is based on known individual diagnostic methods, it is demonstrated that the integrated formulation provides not only higher effectiveness but also additional fault identification capabilities.
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Shao, Jiye, Rixin Wang, Jingbo Gao, and Minqiang Xu. "Probabilistic Model-Based Fault Diagnosis of the Rotor System." In ASME 2007 Power Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/power2007-22072.

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The rotor is one of the most core components of the rotating machinery and its working states directly influence the working states of the whole rotating machinery. There exists much uncertainty in the field of fault diagnosis in the rotor system. This paper analyses the familiar faults of the rotor system and the corresponding faulty symptoms, then establishes the rotor’s Bayesian network model based on above information. A fault diagnosis system based on the Bayesian network model is developed. Using this model, the conditional probability of the fault happening is computed when the observation of the rotor is presented. Thus, the fault reason can be determined by these probabilities. The diagnosis system developed is used to diagnose the actual three faults of the rotor of the rotating machinery and the results prove the efficiency of the method proposed.
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Wang, Zhao-Hui, Lai-Bin Zhang, Wei Liang, and Lixiang Duan. "Research of Method to Diagnosing Complex Fault of Compressor in Pipeline Station." In 2006 International Pipeline Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/ipc2006-10050.

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The compressor is dynamical equipment in pipeline station to deliver oil gas, it’s fault can result big accident such as stopping delivery and producing economic losing, and some fault of compressor are very complex due to the compressor’s complicated structure. Many compressor have carried simple diagnostic system, which can only diagnose normal fault, are not effective for diagnosing complex fault because these fault attributes are not obvious. This paper has researched the method to diagnose complex fault, by collecting the compressor’s vibration signals, using wavelet noise reduction technique and the fractal dimension method to process the vibration signal, which can abstract the non-obvious characteristics of complex fault effectively. The basic principle of fractal method applied in fault diagnosis is described. The result implies that the fractal dimension of good compressor is 4.4, and the fractal dimension of faulty compressor is 5.36, and fractal dimension of compressor with complex faults is 5.42. It is illustrated that this method is very effective for describing the fault features and diagnosing the complex fault of complex. This method can diagnose and predict the complex fault with a high correctness, and has been used in the Shanxi-Beijing pipeline station successfully, Which provide a good tool for pipeline’s Safety and Integrity Management.
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Mendonça, Luis, S. M. Vieira, and J. M. C. Sousa. "INTELLIGENT FAULT DIAGNOSIS OF MARINE EQUIPMENTS." In Maritime Transport Conference. Universitat Politècnica de Catalunya. Iniciativa Digital Politècnica, 2024. http://dx.doi.org/10.5821/mt.12834.

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The automatic and intelligent diagnosis of faults in marine equipment is a task that is considered to be of great importance considering the numerous tasks that are associated with professionals working on ships. The possibility of including automatic and intelligent processes on a ship makes it possible to monitor equipment more effectively and make more informed decisions. This approach has received a lot of attention in the academic and industrial fields as it can offer considerable economic and safety advantages. Some fault diagnosis approaches can be found in the literature, where mathematical and control theory models are taken into account. However, in complex processes not all their characteristics are always known exactly, so mathematical modelling of processes is an extremely difficult task. Fault diagnosis can therefore be based mainly on data or heuristic information. The inherent characteristics of fuzzy logic theory make it suitable for processing this type of information, which is why it will be used to model processes and diagnose faults in a marine equipment valve. The fault diagnosis architecture proposed in this paper is based on analysing the discrepancy signals obtained between the outputs of the fuzzy models and the process data under study. These discrepancies, the residuals, are indicative of equipment fault. The proposed fault diagnosis architecture uses an intelligent decision-making approach to indicate the occurrence of faults. In this paper, this architecture will be used to diagnose abrupt faults in a marine equipment valve.
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Romessis, C., and K. Mathioudakis. "Implementation of Stochastic Methods for Industrial Gas Turbine Fault Diagnosis." In ASME Turbo Expo 2005: Power for Land, Sea, and Air. ASMEDC, 2005. http://dx.doi.org/10.1115/gt2005-68739.

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Implementation of stochastic diagnostic methods for diagnosis of sensor or component faults is presented. Two industrial gas turbines are considered as test cases, one twin and one single shaft arrangement. Methods based on Probabilistic Neural Networks (PNN) and Bayesian Belief Networks (BBN), are implemented. The ability for successful diagnosis is demonstrated on specific cases of sensor malfunctions, as well as on two types of compressor deterioration, fouling and variable vane mistuning. The examined diagnostic problem and the methods of PNN for sensor fault diagnosis and BBN for the diagnosis of component faults are first described. For each gas turbine case, the implementation of the diagnostic methods is shown and application to fault cases that occurred is presented. The effectiveness of the stochastic diagnostic methods demonstrates that they offer a powerful alternative diagnostic tool.
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Reports on the topic "Fault diagnosis"

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Graham, Bryan P. Multiple Fault Diagnosis System. Fort Belvoir, VA: Defense Technical Information Center, June 1988. http://dx.doi.org/10.21236/ada200406.

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Seginer, Ido, Louis D. Albright, and Robert W. Langhans. On-line Fault Detection and Diagnosis for Greenhouse Environmental Control. United States Department of Agriculture, February 2001. http://dx.doi.org/10.32747/2001.7575271.bard.

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Background Early detection and identification of faulty greenhouse operation is essential, if losses are to be minimized by taking immediate corrective actions. Automatic detection and identification would also free the greenhouse manager to tend to his other business. Original objectives The general objective was to develop a method, or methods, for the detection, identification and accommodation of faults in the greenhouse. More specific objectives were as follows: 1. Develop accurate systems models, which will enable the detection of small deviations from normal behavior (of sensors, control, structure and crop). 2. Using these models, develop algorithms for an early detection of deviations from the normal. 3. Develop identifying procedures for the most important faults. 4. Develop accommodation procedures while awaiting a repair. The Technion team focused on the shoot environment and the Cornell University team focused on the root environment. Achievements Models: Accurate models were developed for both shoot and root environment in the greenhouse, utilizing neural networks, sometimes combined with robust physical models (hybrid models). Suitable adaptation methods were also successfully developed. The accuracy was sufficient to allow detection of frequently occurring sensor and equipment faults from common measurements. A large data base, covering a wide range of weather conditions, is required for best results. This data base can be created from in-situ routine measurements. Detection and isolation: A robust detection and isolation (formerly referred to as 'identification') method has been developed, which is capable of separating the effect of faults from model inaccuracies and disturbance effects. Sensor and equipment faults: Good detection capabilities have been demonstrated for sensor and equipment failures in both the shoot and root environment. Water stress detection: An excitation method of the shoot environment has been developed, which successfully detected water stress, as soon as the transpiration rate dropped from its normal level. Due to unavailability of suitable monitoring equipment for the root environment, crop faults could not be detected from measurements in the root zone. Dust: The effect of screen clogging by dust has been quantified. Implications Sensor and equipment fault detection and isolation is at a stage where it could be introduced into well equipped and maintained commercial greenhouses on a trial basis. Detection of crop problems requires further work. Dr. Peleg was primarily responsible for developing and implementing the innovative data analysis tools. The cooperation was particularly enhanced by Dr. Peleg's three summer sabbaticals at the ARS, Northem Plains Agricultural Research Laboratory, in Sidney, Montana. Switching from multi-band to hyperspectral remote sensing technology during the last 2 years of the project was advantageous by expanding the scope of detected plant growth attributes e.g. Yield, Leaf Nitrate, Biomass and Sugar Content of sugar beets. However, it disrupted the continuity of the project which was originally planned on a 2 year crop rotation cycle of sugar beets and multiple crops (com and wheat), as commonly planted in eastern Montana. Consequently, at the end of the second year we submitted a continuation BARD proposal which was turned down for funding. This severely hampered our ability to validate our findings as originally planned in a 4-year crop rotation cycle. Thankfully, BARD consented to our request for a one year extension of the project without additional funding. This enabled us to develop most of the methodology for implementing and running the hyperspectral remote sensing system and develop the new analytical tools for solving the non-repeatability problem and analyzing the huge hyperspectral image cube datasets. However, without validation of these tools over a ful14-year crop rotation cycle this project shall remain essentially unfinished. Should the findings of this report prompt the BARD management to encourage us to resubmit our continuation research proposal, we shall be happy to do so.
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Allison, Kenneth R. Use of a Working Model in Fault Diagnosis. Fort Belvoir, VA: Defense Technical Information Center, January 1988. http://dx.doi.org/10.21236/ada195621.

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Church, K. G., R. R. Kolesar, M. E. Phillips, and R. C. Garrido. Air Vehicle Diagnostic System: CH-46 Aft Main Transmission Fault Diagnosis-Final Report. Fort Belvoir, VA: Defense Technical Information Center, June 1997. http://dx.doi.org/10.21236/ada329362.

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Springer, David. Expert Meeting Report: HVAC Fault Detection, DIagnosis, and Repair/Replacement. Office of Scientific and Technical Information (OSTI), May 2016. http://dx.doi.org/10.2172/1254918.

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Springer, David. Expert Meeting Report: HVAC Fault Detection, Diagnosis, and Repair/Replacement. Office of Scientific and Technical Information (OSTI), May 2016. http://dx.doi.org/10.2172/1255641.

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Frank, Stephen M., Guanjing Lin, Xin Jin, Rupam Singla, Amanda Farthing, Liang Zhang, and Jessica Granderson. Metrics and Methods to Assess Building Fault Detection and Diagnosis Tools. Office of Scientific and Technical Information (OSTI), March 2019. http://dx.doi.org/10.2172/1503166.

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Kim, Minsung, Seok Ho Yoon, W. Vance Payne, and Piotr A. Domanski. Cooling mode fault detection and diagnosis method for a residential heat pump. Gaithersburg, MD: National Institute of Standards and Technology, 2008. http://dx.doi.org/10.6028/nist.sp.1087.

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Reifman, J., T. Y. C. Wei, and J. E. Vitela. PRODIAG: Combined expert system/neural network for process fault diagnosis. Volume 1, Theory. Office of Scientific and Technical Information (OSTI), September 1995. http://dx.doi.org/10.2172/219266.

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Bae, Yeonjin, Borui Cui, Jaewan Joe, Piljae Im, Veonica Adetola, Liang Zhang, Matt Leach, and Teja Kuruganti. Review: Sensor Impact on Building Controls and Automatic Fault Detection and Diagnosis (AFDD). Office of Scientific and Technical Information (OSTI), January 2020. http://dx.doi.org/10.2172/1671427.

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