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Advancement of Fault Detection/Diagnosis and Fault-Tolerant Control with Applications
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Advancement of Fault Detection/Diagnosis and Fault-Tolerant Control with Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: closed (20 November 2021) | Viewed by 45771

Special Issue Editors

Prof. Dr. Zehui Mao

E-Mail Website
Guest Editor
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing 211106, China
Interests: fault diagnosis; fault-tolerant control; nonlinear systems; adaptive control
Dr. Xinggang Yan

E-Mail Website
Guest Editor
School of Engineering and Digital Arts, Jennison Building, University of Kent, Canterbury CT2 7NT, Kent, UK
Interests: nonlinear control; sliding mode control; decentralized control; fault detection and isolation; observer design; time delay systems with applications in engineering systems
Dr. Hamed Badihi

E-Mail Website
Guest Editor
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing 210016, China
Interests: condition monitoring; fault detection and diagnosis; fault-tolerant control; fault-tolerant cooperative control; renewable energies; hybrid power systems; smart grids
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Fault Diagnosis and Fault-Tolerant Control has been the core technology which guarantees the high performance and high reliability of modern engineering systems in the presence of faults. Although it has attracted much attention during the past few decades, and the corresponding results have been obtained and reported in the literature, challenges and problems still remain. This Special Issue focuses on the design and implementation of new Fault Diagnosis and Fault-Tolerant Control methods for dynamic systems with their applications in aircraft/spacecraft control systems, formation and swarm systems, traffic systems, and underwater vehicles, etc. 

The main purpose of this Special Issue is to provide a platform for researchers and control engineers to publish their latest novel and original contribution to the area of fault detection/diagnosis and fault-tolerant control in order to satisfy the increasing demands for system reliability as well as safety. The considered system should include some of the characteristics such as faults, nonlinearities, disturbances, time delay, interconnections between subsystems, and stochastic processes, etc. All the results in both theoretical research and engineering applications with emphasis on novel techniques for system analysis and control design as well as various applications relating to faults are encouraged to submit. 

The topics of interest include, but are not limited to:Ÿ  

  • Fault detectability analysis
  • Model-based fault detection/diagnosis method and its performance analysis
  • Fault detection/diagnosis via artificial intelligent method
  • Fault detection/diagnosis for aircraft/spacecraft control systems, formation and swarm systems, traffic systems, and underwater vehicles, etc.
  • Fault-tolerant control via adaptive, sliding-mode, fuzzy technique
  • Fault-tolerant control for uncertain system, stochastic systems, multi-agent systems, etc.

Prof. Dr. Zehui Mao
Dr. Xinggang Yan
Assoc. Prof. Dr. Hamed Badihi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Fault detectability analysis
  • Model-based fault detection/diagnosis method and its performance analysis
  • Fault detection/diagnosis via artificial intelligent method
  • Fault detection/diagnosis for aircraft/spacecraft control systems, formation and swarm systems, traffic systems, and underwater vehicles, etc.
  • Fault-tolerant control via adaptive, sliding-mode, fuzzy technique
  • Fault-tolerant control for uncertain system, stochastic systems, multi-agent systems, etc.

Published Papers (14 papers)

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Research

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18 pages, 2405 KiB  
Article
Power Equipment Defects Prediction Based on the Joint Solution of Classification and Regression Problems Using Machine Learning Methods
by Ivan Shcherbatov, Evgeny Lisin, Andrey Rogalev, Grigory Tsurikov, Marek Dvořák and Wadim Strielkowski
Electronics 2021, 10(24), 3145; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10243145 - 17 Dec 2021
Cited by 5 | Viewed by 2362
Abstract
Our paper proposes a method for constructing a system for predicting defects and failures of power equipment and the time of their occurrence based on the joint solution of regression and classification problems using machine learning methods. A distinctive feature of this method [...] Read more.
Our paper proposes a method for constructing a system for predicting defects and failures of power equipment and the time of their occurrence based on the joint solution of regression and classification problems using machine learning methods. A distinctive feature of this method is the use of the equipment’s technical condition index as an informative parameter. The results of calculating and visualizing the technical condition index in relation to the electro-hydraulic automatic control system of hydropower turbine when predicting the defect “clogging of drainage channels” showed that its determination both for an equipment and for a group of its functional units allows one to quickly and with the required accuracy assess the arising technological disturbances in the operation of power equipment. In order to predict the behavior of the technical condition index of the automatic control system of the turbine, the optimal tuning of the LSTM model of the recurrent neural network was developed and carried out. The result of the application of the model was the forecast of the technical condition index achievement and the limiting characteristic according to the current time data on its values. The developed model accurately predicted the behavior of the technical condition index at time intervals of 3 and 10 h, which made it possible to draw a conclusion about its applicability for early identification of the investigated defect in the automatic control system of the turbine. Thus, we can conclude that the joint solution of regression and classification problems using an information parameter in the form of a technical condition index allows one to develop systems for predicting defects, one significant advantage of which is the ability to early determine the development of degradation phenomena in power equipment. Full article
(This article belongs to the Special Issue Advancement of Fault Detection/Diagnosis and Fault-Tolerant Control with Applications)
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19 pages, 843 KiB  
Article
Revisiting Symptom-Based Fault Tolerant Techniques against Soft Errors
by Hwisoo So, Moslem Didehban, Yohan Ko, Reiley Jeyapaul, Jongho Kim, Youngbin Kim, Kyoungwoo Lee and Aviral Shrivastava
Electronics 2021, 10(23), 3028; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10233028 - 04 Dec 2021
Cited by 2 | Viewed by 1608
Abstract
Aggressive technology scaling and near-threshold computing have made soft error reliability one of the leading design considerations in modern embedded microprocessors. Although traditional hardware/software redundancy-based schemes can provide a high level of protection, they incur significant overheads in terms of performance and hardware [...] Read more.
Aggressive technology scaling and near-threshold computing have made soft error reliability one of the leading design considerations in modern embedded microprocessors. Although traditional hardware/software redundancy-based schemes can provide a high level of protection, they incur significant overheads in terms of performance and hardware resources. The considerable overheads from such full redundancy-based techniques has motivated researchers to propose low-cost soft error protection schemes, such as symptom-based error protection schemes. The main idea behind a symptom-based error protection scheme is that soft errors in the system will quickly generate some symptoms, such as exceptions, branch mispredictions, cache or TLB misses, or unpredictable variable values. Therefore, monitoring such infrequent symptoms makes it possible to cover the manifestation of failures caused by soft errors. Symptom-based protection schemes have been suggested as shortcuts to achieve acceptable reliability with comparable overheads. Since the symptom-based protection schemes seem attractive due to their generality and simplicity, even state-of-the-art protection schemes exploit them as the baseline protections. However, our detailed analysis of the fault coverage and performance overheads of such schemes reveals that the user-visible failure coverage, particularly of ReStore, is limited (29% on average). By contrast, the runtime overheads are significant (40% on average) because the majority of the fault injection experiments, which were considered as detected/recovered failures by low-level symptoms, are actually benign faults by program-level masking effects. Full article
(This article belongs to the Special Issue Advancement of Fault Detection/Diagnosis and Fault-Tolerant Control with Applications)
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23 pages, 5832 KiB  
Article
A Remedial Control for Short-Circuit Fault in NPC/H-Bridge Inverters without Redundant Component
by Sajjad Ahmadi, Philippe Poure, Davood Arab Khaburi and Shahrokh Saadate
Electronics 2021, 10(19), 2411; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10192411 - 02 Oct 2021
Cited by 2 | Viewed by 1875
Abstract
In this paper, a five-level neutral-point-clamped (NPC) inverter with short-circuit fault-tolerant capability is presented. Based on the proposed approach, in order to ensure service continuity subsequent to a short-circuit fault event in a switch, two steps are carried out. First of all, destructive [...] Read more.
In this paper, a five-level neutral-point-clamped (NPC) inverter with short-circuit fault-tolerant capability is presented. Based on the proposed approach, in order to ensure service continuity subsequent to a short-circuit fault event in a switch, two steps are carried out. First of all, destructive consequences arising from short-circuit fault in a power switch is prevented. Afterwards, according to the defected component, remedial actions are taken. The proposed strategy does not require any redundant component. The service continuity is acquired by applying a remedial control and modifying switching commands applied to the power switches. Using the proposed approach helps to restore the rated voltage and rated current at the terminal, and there is no limit for modulation index during fault-tolerant operation under remedial control. Furthermore, compared to healthy operation, harmonic content of the terminal voltage and current is not deteriorated during fault-tolerant operation. Moreover, additional components, such as bidirectional switches and contactors, are not employed in this strategy. Only some fast fuses are placed in the converter circuit for protection purposes which do not impose a noticeable cost compared to the bidirectional switches and contactors. Full article
(This article belongs to the Special Issue Advancement of Fault Detection/Diagnosis and Fault-Tolerant Control with Applications)
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16 pages, 446 KiB  
Article
Anti-Disturbance Fault-Tolerant Sliding Mode Control for Systems with Unknown Faults and Disturbances
by Xiaoli Zhang, Zhengyu Zhu and Yang Yi
Electronics 2021, 10(12), 1487; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10121487 - 20 Jun 2021
Cited by 2 | Viewed by 1776
Abstract
In this paper, a novel control algorithm with the capacity of fault tolerance and anti-disturbance is discussed for the systems subjected to actuator faults and mismatched disturbances. The fault diagnosis observer (FDO) and the disturbance observer (DO) are successively designed to estimate the [...] Read more.
In this paper, a novel control algorithm with the capacity of fault tolerance and anti-disturbance is discussed for the systems subjected to actuator faults and mismatched disturbances. The fault diagnosis observer (FDO) and the disturbance observer (DO) are successively designed to estimate the dynamics of unknown faults and disturbances. Furthermore, with the help of the observed information, a sliding surface and the corresponding sliding mode controller are proposed to compensate the actuator faults and eliminate the impact of mismatched disturbances simultaneously. Meanwhile, the convex optimization algorithm is discussed to guarantee the stability of the controlled system. The favorable anti-disturbance and fault-tolerant results can also be proved. Finally, the validity of the algorithm is certified by the simulation results for typical unmanned aerial vehicles (UAV) systems. Full article
(This article belongs to the Special Issue Advancement of Fault Detection/Diagnosis and Fault-Tolerant Control with Applications)
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23 pages, 1225 KiB  
Article
Multi-Fault Diagnosis in Three-Phase Induction Motors Using Data Optimization and Machine Learning Techniques
by Gustavo Henrique Bazan, Alessandro Goedtel, Oscar Duque-Perez and Daniel Morinigo-Sotelo
Electronics 2021, 10(12), 1462; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10121462 - 18 Jun 2021
Cited by 16 | Viewed by 3109
Abstract
Induction motors are very robust, with low operating and maintenance costs, and are therefore widely used in industry. They are, however, not fault-free, with bearings and rotor bars accounting for about 50% of the total failures. This work presents a two-stage approach for [...] Read more.
Induction motors are very robust, with low operating and maintenance costs, and are therefore widely used in industry. They are, however, not fault-free, with bearings and rotor bars accounting for about 50% of the total failures. This work presents a two-stage approach for three-phase induction motors diagnosis based on mutual information measures of the current signals, principal component analysis, and intelligent systems. In a first stage, the fault is identified, and, in a second stage, the severity of the defect is diagnosed. A case study is presented where different severities of bearing wear and bar breakage are analyzed. To test the robustness of the proposed method, voltage imbalances and load torque variations are considered. The results reveal the promising performance of the proposal with overall accuracies above 90% in all cases, and in many scenarios 100% of the cases are correctly classified. This work also evaluates different strategies for extracting the signals, showing the possibility of reducing the amount of information needed. Results show a satisfactory relation between efficiency and computational cost, with decreases in accuracy of less than 4% but reducing the amount of data by more than 90%, facilitating the efficient use of this method in embedded systems. Full article
(This article belongs to the Special Issue Advancement of Fault Detection/Diagnosis and Fault-Tolerant Control with Applications)
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18 pages, 1325 KiB  
Article
A Novel Bottom-Up/Top-Down Hybrid Strategy-Based Fast Sequential Fault Diagnosis Method
by Jingyuan Wang, Zhen Liu, Xiaowu Chen, Bing Long, Chenglin Yang and Xiuyun Zhou
Electronics 2021, 10(12), 1441; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10121441 - 15 Jun 2021
Cited by 2 | Viewed by 1424
Abstract
Sequential fault diagnosis is a kind of important fault diagnosis method for large scale complex systems, and generating an excellent fault diagnosis strategy is critical to ensuring the performance of sequential diagnosis. However, with the system complexity increasing, the complexity of fault diagnosis [...] Read more.
Sequential fault diagnosis is a kind of important fault diagnosis method for large scale complex systems, and generating an excellent fault diagnosis strategy is critical to ensuring the performance of sequential diagnosis. However, with the system complexity increasing, the complexity of fault diagnosis tree increases sharply, which makes it extremely difficult to generate an optimal diagnosis strategy. Especially, because the existing methods need massive redundancy iteration and repeated calculation for the state parameters of nodes, the resulting diagnosis strategy is often inefficient. To address this issue, a novel fast sequential fault diagnosis method is proposed. In this method, we present a new bottom-up search idea based on Karnaugh map, SVM and simulated annealing algorithm. It combines failure sources to generate states and a Karnaugh map is used to judge the logic of every state. Eigenvalues of SVM are obtained quickly through the simulated annealing algorithm, then SVM is used to eliminate the less useful state. At the same time, the bottom-up method and cost heuristic algorithms are combined to generate the optimal decision tree. The experiments show that the calculation time of the method is shorter than the time of previous algorithms, and a smaller test cost can be obtained when the number of samples is sufficient. Full article
(This article belongs to the Special Issue Advancement of Fault Detection/Diagnosis and Fault-Tolerant Control with Applications)
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30 pages, 13693 KiB  
Article
A CNN Prediction Method for Belt Grinding Tool Wear in a Polishing Process Utilizing 3-Axes Force and Vibration Data
by Wahyu Caesarendra, Triwiyanto Triwiyanto, Vigneashwara Pandiyan, Adam Glowacz, Silvester Dian Handy Permana and Tegoeh Tjahjowidodo
Electronics 2021, 10(12), 1429; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10121429 - 14 Jun 2021
Cited by 17 | Viewed by 3134
Abstract
This paper presents a tool wear monitoring methodology on the abrasive belt grinding process using vibration and force signatures on a convolutional neural network (CNN). A belt tool typically has a random orientation of abrasive grains and grit size variation for coarse or [...] Read more.
This paper presents a tool wear monitoring methodology on the abrasive belt grinding process using vibration and force signatures on a convolutional neural network (CNN). A belt tool typically has a random orientation of abrasive grains and grit size variation for coarse or fine material removal. Degradation of the belt condition is a critical phenomenon that affects the workpiece quality during grinding. This work focuses on the identifation and the study of force and vibrational signals taken from sensors along an axis or combination of axes that carry important information of the contact conditions, i.e., belt wear. Three axes of the two sensors are aligned and labelled as X-axis (parallel to the direction of the tool during the abrasive process), Y-axis (perpendicular to the direction of the tool during the abrasive process) and Z-axis (parallel to the direction of the tool during the retract movement). The grinding process was performed using a customized abrasive belt grinder attached to a multi-axis robot on a mild-steel workpiece. The vibration and force signals along three axes (X, Y and Z) were acquired for four discrete sequential belt wear conditions: brand-new, 5-min cycle time, 15-min cycle time, and worn-out. The raw signals that correspond to the sensor measurement along the different axes were used to supervisedly train a 10-Layer CNN architecture to distinguish the belt wear states. Different possible combinations within the three axes of the sensors (X, Y, Z, XY, XZ, YZ and XYZ) were fed as inputs to the CNN model to sort the axis (or combination of axes) in the order of distinct representation of the belt wear state. The CNN classification results revealed that the combination of the XZ-axes and YZ-axes of the accelerometer sensor provides more accurate predictions than other combinations, indicating that the information from the Z-axis of the accelerometer is significant compared to the other two axes. In addition, the CNN accuracy of the XY-axes combination of dynamometer outperformed that of other combinations. Full article
(This article belongs to the Special Issue Advancement of Fault Detection/Diagnosis and Fault-Tolerant Control with Applications)
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14 pages, 2853 KiB  
Article
Condition-Based Maintenance of HVAC on a High-Speed Train for Fault Detection
by Lorenzo Ciani, Giulia Guidi, Gabriele Patrizi and Diego Galar
Electronics 2021, 10(12), 1418; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10121418 - 12 Jun 2021
Cited by 6 | Viewed by 3942
Abstract
Reliability-centered maintenance (RCM) is a well-established method for preventive maintenance planning. This paper focuses on the optimization of a maintenance plan for an HVAC (heating, ventilation and air conditioning) system located on high-speed trains. The first steps of the RCM procedure help in [...] Read more.
Reliability-centered maintenance (RCM) is a well-established method for preventive maintenance planning. This paper focuses on the optimization of a maintenance plan for an HVAC (heating, ventilation and air conditioning) system located on high-speed trains. The first steps of the RCM procedure help in identifying the most critical items of the system in terms of safety and availability by means of a failure modes and effects analysis. Then, RMC proposes the optimal maintenance tasks for each item making up the system. However, the decision-making diagram that leads to the maintenance choice is extremely generic, with a consequent high subjectivity in the task selection. This paper proposes a new fuzzy-based decision-making diagram to minimize the subjectivity of the task choice and preserve the cost-efficiency of the procedure. It uses a case from the railway industry to illustrate the suggested approach, but the procedure could be easily applied to different industrial and technological fields. The results of the proposed fuzzy approach highlight the importance of an accurate diagnostics (with an overall 86% of the task as diagnostic-based maintenance) and condition monitoring strategy (covering 54% of the tasks) to optimize the maintenance plan and to minimize the system availability. The findings show that the framework strongly mitigates the issues related to the classical RCM procedure, notably the high subjectivity of experts. It lays the groundwork for a general fuzzy-based reliability-centered maintenance method. Full article
(This article belongs to the Special Issue Advancement of Fault Detection/Diagnosis and Fault-Tolerant Control with Applications)
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16 pages, 5965 KiB  
Article
State of Health Prediction of Power Connectors by Analyzing the Degradation Trajectory of the Electrical Resistance
by Jimmy Martínez, Jordi-Roger Riba and Manuel Moreno-Eguilaz
Electronics 2021, 10(12), 1409; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10121409 - 11 Jun 2021
Cited by 9 | Viewed by 2011
Abstract
Estimating the remaining useful life (RUL) or the state of health (SoH) of electrical components such as power connectors is still a challenging and complex task. Power connectors play a critical role in medium- and high-voltage power networks, their failure leading to important [...] Read more.
Estimating the remaining useful life (RUL) or the state of health (SoH) of electrical components such as power connectors is still a challenging and complex task. Power connectors play a critical role in medium- and high-voltage power networks, their failure leading to important consequences such as power outages, unscheduled downtimes, safety hazards or important economic losses. Online condition monitoring strategies allow developing improved predictive maintenance plans. Due to the development of low-cost sensors and electronic communication systems compatible with Internet of Things (IoT) applications, several methods for online and offline SoH determination of diverse power devices are emerging. This paper presents, analyzes and compares the performance of three simple and effective methods for online determination of the SoH of power connectors with low computational requirements. The proposed approaches are based on monitoring the evolution of the connectors’ electrical resistance, which defines the degradation trajectory because the electrical resistance is a reliable indicator or signature of the SoH of the connectors. The methods analyzed in this paper are validated by means of experimental ageing tests emulating real degradation conditions. Laboratory results prove the suitability and feasibility of the proposed approach, which could be applied to other power products and apparatus. Full article
(This article belongs to the Special Issue Advancement of Fault Detection/Diagnosis and Fault-Tolerant Control with Applications)
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23 pages, 3761 KiB  
Article
Real-Time Fault Diagnosis and Fault-Tolerant Control Strategy for Hall Sensors in Permanent Magnet Brushless DC Motor Drives
by Xi Zhang, Yiyun Zhao, Hui Lin, Saleem Riaz and Hassan Elahi
Electronics 2021, 10(11), 1268; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10111268 - 25 May 2021
Cited by 10 | Viewed by 3238
Abstract
The Hall sensor is the most commonly used position sensor of the permanent magnet brushless direct current (PMBLDC) motor. Its failure may lead to a decrease in system reliability. Hence, this article proposes a novel methodology for the Hall sensors fault diagnosis and [...] Read more.
The Hall sensor is the most commonly used position sensor of the permanent magnet brushless direct current (PMBLDC) motor. Its failure may lead to a decrease in system reliability. Hence, this article proposes a novel methodology for the Hall sensors fault diagnosis and fault-tolerant control in PMBLDC motor drives. Initially, the Hall sensor faults are analyzed and classified into three fault types. Taking the Hall signal as the system state and the conducted MOSFETs as the system event, the extended finite state machine (EFSM) of the motor in operation is established. Meanwhile, a motor speed observer based on the super twisting algorithm (STA) is designed to obtain the speed signal of the proposed strategy. On this basis, a real-time Hall sensor fault diagnosis strategy is established by combining the EFSM and the STA speed observer. Moreover, this article proposes a Hall signal reconstruction strategy, which can generate compensated Hall signal to realize fault-tolerant control under single or double Hall sensor faults. Finally, theoretical analysis and experimental results validate the superior effectiveness of the proposed real-time fault diagnosis and fault-tolerant control strategy. Full article
(This article belongs to the Special Issue Advancement of Fault Detection/Diagnosis and Fault-Tolerant Control with Applications)
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20 pages, 5508 KiB  
Article
Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network
by Rafia Nishat Toma, Cheol-Hong Kim and Jong-Myon Kim
Electronics 2021, 10(11), 1248; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10111248 - 24 May 2021
Cited by 34 | Viewed by 3799
Abstract
Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify [...] Read more.
Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings. Full article
(This article belongs to the Special Issue Advancement of Fault Detection/Diagnosis and Fault-Tolerant Control with Applications)
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11 pages, 1755 KiB  
Article
Application of Logistic Regression Algorithm in the Interpretation of Dissolved Gas Analysis for Power Transformers
by Yousuf D. Almoallem, Ibrahim B. M. Taha, Mohamed I. Mosaad, Lara Nahma and Ahmed Abu-Siada
Electronics 2021, 10(10), 1206; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10101206 - 19 May 2021
Cited by 13 | Viewed by 2634
Abstract
Dissolved gas analysis (DGA) is one of the regular routine tests accepted by worldwide utilities to detect power transformer incipient faults. While the DGA measurement has fully matured since the development of offline and online sensors, interpretation of the DGA results still calls [...] Read more.
Dissolved gas analysis (DGA) is one of the regular routine tests accepted by worldwide utilities to detect power transformer incipient faults. While the DGA measurement has fully matured since the development of offline and online sensors, interpretation of the DGA results still calls for advanced approaches to automate and standardize the process. Current industry practice relies on various interpretation techniques that are reported to be inconsistent and, in some cases, unreliable. This paper presents a new application for the advanced logistic regression algorithm to improve the reliability of the DGA interpretation process. In this regard, regularized logistic regression is used to improve the accuracy of the DGA interpretation process. Results reveal the superior features of the proposed logistic regression approach over the conventional and artificial intelligence techniques presented in the literature. Full article
(This article belongs to the Special Issue Advancement of Fault Detection/Diagnosis and Fault-Tolerant Control with Applications)
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20 pages, 844 KiB  
Article
F_Radish: Enhancing Silent Data Corruption Detection for Aerospace-Based Computing
by Na Yang and Yun Wang
Electronics 2021, 10(1), 61; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10010061 - 31 Dec 2020
Cited by 3 | Viewed by 2107
Abstract
Radiation-induced soft errors degrade the reliability of aerospace-based computing. Silent data corruption (SDC) is the most dangerous and insidious type of soft error result. To detect SDC, program invariant assertions are used to harden programs. However, there exist redundant assertions in hardened programs, [...] Read more.
Radiation-induced soft errors degrade the reliability of aerospace-based computing. Silent data corruption (SDC) is the most dangerous and insidious type of soft error result. To detect SDC, program invariant assertions are used to harden programs. However, there exist redundant assertions in hardened programs, which impairs the detection efficiency. Benign errors are another type of soft error result. An assertion may detect benign errors, incurring unnecessary recovery overhead. The detection degree of an assertion represents the detection capability, and an assertion with a high detection degree can detect severe errors. To improve the detection efficiency and detection degree while reducing the benign detection ratio, F_Radish is proposed in the present work to screen redundant assertions in a novel way. At a program point, the detection degree and benign detection ratio are considered to evaluate the importance of the assertions in the program point. As a result, only the most important assertion remains in the program point. Moreover, the redundancy degree is considered to screen redundant assertions for neighbouring program points. Experimental results show that in comparison with the Radish approach, the detection efficiency of F_Radish is about two times greater. Moreover, F_Radish reduces the benign detection ratio and improves the detection degree. It can avoid more unnecessary recovery overheads and detect more serious SDC than can Radish. Full article
(This article belongs to the Special Issue Advancement of Fault Detection/Diagnosis and Fault-Tolerant Control with Applications)
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Review

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17 pages, 1450 KiB  
Review
Machine Learning-Based Data-Driven Fault Detection/Diagnosis of Lithium-Ion Battery: A Critical Review
by Akash Samanta, Sumana Chowdhuri and Sheldon S. Williamson
Electronics 2021, 10(11), 1309; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10111309 - 30 May 2021
Cited by 77 | Viewed by 10746
Abstract
Fault detection/diagnosis has become a crucial function of the battery management system (BMS) due to the increasing application of lithium-ion batteries (LIBs) in highly sophisticated and high-power applications to ensure the safe and reliable operation of the system. The application of Machine Learning [...] Read more.
Fault detection/diagnosis has become a crucial function of the battery management system (BMS) due to the increasing application of lithium-ion batteries (LIBs) in highly sophisticated and high-power applications to ensure the safe and reliable operation of the system. The application of Machine Learning (ML) in the BMS of LIB has long been adopted for efficient, reliable, accurate prediction of several important states of LIB such as state of charge, state of health and remaining useful life. Inspired by some of the promising features of ML-based techniques over the conventional LIB fault detection/diagnosis methods such as model-based, knowledge-based and signal processing-based techniques, ML-based data-driven methods have been a prime research focus in the last few years. This paper provides a comprehensive review exclusively on the state-of-the-art ML-based data-driven fault detection/diagnosis techniques to provide a ready reference and direction to the research community aiming towards developing an accurate, reliable, adaptive and easy to implement fault diagnosis strategy for the LIB system. Current issues of existing strategies and future challenges of LIB fault diagnosis are also explained for better understanding and guidance. Full article
(This article belongs to the Special Issue Advancement of Fault Detection/Diagnosis and Fault-Tolerant Control with Applications)
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