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Fault Diagnosis and Fault-Tolerant Control of Power Machinery: Developments and...
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Fault Diagnosis and Fault-Tolerant Control of Power Machinery: Developments and Challenges

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 5453

Special Issue Editor

Dr. Sajad Saraygord Afshari

E-Mail Website
Guest Editor
Department of Mechanical Engineering University of Manitoba, Winnipeg, MB R3T 2N2, Canada
Interests: dynamics and control; vibration analysis and control; reliability structural health monitoring

Special Issue Information

Dear Colleagues,

The "Fault Diagnosis and Fault-Tolerant Control of Power Machinery: Developments and Challenges" Special Issue compiles cutting-edge research on fault detection, diagnosis, and control strategies for power machinery systems. Its goal is to meet the growing demand for the efficient, reliable, and safe operation of power machinery amidst technological advancements and increasing complexity. The issue consists of peer-reviewed articles that explore various aspects of fault diagnosis and fault-tolerant control. These include innovative methodologies and techniques for the early detection and identification of faults in machinery components, adaptive and robust control approaches, advanced data analytics for improved fault diagnosis, real-time monitoring and health management systems, and practical applications in diverse power machinery domains. In addition, the issue discusses the challenges faced by researchers and practitioners in implementing these advanced techniques, including scalability, computational complexity, and the need for reliable performance in uncertain environments. By showcasing the latest developments and addressing the challenges in fault diagnosis and control of power machinery, this Special Issue serves as a valuable resource for researchers, engineers, and policymakers in the field.

Dr. Sajad Saraygord Afshari
Guest Editor

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. Machines is an international peer-reviewed open access monthly 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.

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Published Papers (4 papers)

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Research

14 pages, 4344 KiB  
Article
Investigation of Transfer Learning Method for Motor Fault Detection
by Prashant Kumar, Saurabh Singh and Doug Young Song
Machines 2025, 13(4), 329; https://doi.org/10.3390/machines13040329 - 17 Apr 2025
Viewed by 149
Abstract
Industry 4.0 is propelling modern industries forward due to its reliability, stability, and performance. Electric motors (EMs) are utilized in multiple industries for their efficiency, precise speed and torque control, and robustness. Detecting faults in motors at an early stage is crucial to [...] Read more.
Industry 4.0 is propelling modern industries forward due to its reliability, stability, and performance. Electric motors (EMs) are utilized in multiple industries for their efficiency, precise speed and torque control, and robustness. Detecting faults in motors at an early stage is crucial to ensure maximum productivity. Recently, DL has been implemented as a data-driven approach for detecting faults in motors. However, due to the limited availability of labeled fault data, the performance of the DL model is constrained. This issue is addressed by leveraging transfer learning (TL), which uses knowledge from a larger source domain to improve performance in a smaller target domain. In this paper, a multiple fault detection (FD) model for EMs is proposed by combining the ideas of deep convolutional neural networks (CNNs) and TL. A one-dimensional signal-to-image conversion technique is suggested for converting the vibration signal to images, and an Inception-ResNet-v2-inspired FD model is proposed for detecting bearing faults in the motor. The proposed method achieved a mean accuracy of more than 99%. Full article
(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control of Power Machinery: Developments and Challenges)
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25 pages, 4153 KiB  
Article
Enhanced Fault Detection in Satellite Attitude Control Systems Using LSTM-Based Deep Learning and Redundant Reaction Wheels
by Sajad Saraygord Afshari
Machines 2024, 12(12), 856; https://doi.org/10.3390/machines12120856 - 27 Nov 2024
Cited by 1 | Viewed by 1034
Abstract
Reliable fault detection in satellite attitude control systems stands as a critical aspect of ensuring the safety and success of space missions. Central to these systems, reaction wheels (RWs), despite being the most frequently used actuators, present a vulnerability given their susceptibility to [...] Read more.
Reliable fault detection in satellite attitude control systems stands as a critical aspect of ensuring the safety and success of space missions. Central to these systems, reaction wheels (RWs), despite being the most frequently used actuators, present a vulnerability given their susceptibility to faults—a factor with the potential to precipitate catastrophic failures such as total satellite loss. In light of this, we introduce a fault detection methodology grounded in deep learning techniques specifically designed for satellite attitude control systems. Our proposed method utilizes a Long Short-Term Memory (LSTM) model adept at learning temporal patterns inherent to both healthy and faulty system behaviors. Incorporated into our model is a torque allocation algorithm designed to circumvent specific velocities known to induce torque disturbances, a factor known to influence LSTM performance adversely. To bolster the robustness of our fault detection technique, we also incorporated denoising autoencoders within the LSTM framework, thereby enabling the model to identify temporal patterns in healthy and faulty system behavior, even amidst the noise. The method was evaluated using cross-validation on simulated satellite data comprising 1000 time series samples and across different fault scenarios, such as stiction and resonance at varying intensities (90%, 50%, and 30%). The results confirm achieving performance metrics such as Mean Squared Error for accurate fault identification. This research underscores a stride in the evolution of fault detection and control strategies for satellite attitude control systems, holding promise to boost the reliability and efficiency of future space missions. Full article
(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control of Power Machinery: Developments and Challenges)
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22 pages, 1749 KiB  
Article
Assessing the Critical Factors Leading to the Failure of the Industrial Pressure Relief Valve Through a Hybrid MCDM-FMEA Approach
by Pradnya Kuchekar, Ajay S. Bhongade, Ateekh Ur Rehman and Syed Hammad Mian
Machines 2024, 12(11), 820; https://doi.org/10.3390/machines12110820 - 17 Nov 2024
Cited by 3 | Viewed by 1261
Abstract
Industrial pressure relief valves must function reliably and effectively to protect pressurized systems from excessive pressure conditions. These valves are essential safety devices that act as cushions to protect piping systems, equipment, and vessels from the risks of high pressure, which can cause [...] Read more.
Industrial pressure relief valves must function reliably and effectively to protect pressurized systems from excessive pressure conditions. These valves are essential safety devices that act as cushions to protect piping systems, equipment, and vessels from the risks of high pressure, which can cause damage or even explosions. The objectives of this study were to minimize valve failures, decrease the number of rejected valves in the production line, and enhance the overall quality of pressure relief valves. This work introduces an integrated quality improvement methodology known as the hybrid multi-criteria decision-making (MCDM)—failure mode and effects analysis (FMEA) approach. This approach is based on prioritizing crucial factors for any failure modes in the industrial setting. The presented case study demonstrates the application of a hybrid approach for identifying the fundamental causes of industrial pressure relief valve failure modes and malfunctions. This investigation highlights the applicability of FMEA as a methodology for determining causes and executing remedial actions to keep failures from happening again. FMEA helps uncover the underlying causes of industrial pressure relief valve failures, while the integration of the hybrid MCDM methodology enables the application of four integrated MCDM methods to identify crucial factors. The adopted model addresses the shortcomings of the conventional FMEA by accurately analyzing the relationships between the risk factors and by utilizing several MCDM methods to rank failure modes. Following the application of the adopted methodology, it was discovered that the high-risk failure modes for the pressure relief valve included misalignment of wire, normal wear/aging, rejection of machined parts, mismatch of mating parts, and corrosion. Therefore, risk managers should prioritize developing improvement strategies for these five failure modes. Similarly, failures comprising debris, delayed valve opening, internal leakage, premature valve opening, and burr foreign particles were determined as second essential groups for improvement. Full article
(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control of Power Machinery: Developments and Challenges)
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17 pages, 4560 KiB  
Article
A Generalised Intelligent Bearing Fault Diagnosis Model Based on a Two-Stage Approach
by Amirmasoud Kiakojouri, Zudi Lu, Patrick Mirring, Honor Powrie and Ling Wang
Machines 2024, 12(1), 77; https://doi.org/10.3390/machines12010077 - 19 Jan 2024
Cited by 3 | Viewed by 2064
Abstract
This paper introduces a two-stage intelligent fault diagnosis model for rolling element bearings (REBs) aimed at overcoming the challenge of limited real-world vibration training data. In this study, bearing characteristic frequencies (BCFs) extracted from a novel hybrid method combining cepstrum pre-whitening (CPW) and [...] Read more.
This paper introduces a two-stage intelligent fault diagnosis model for rolling element bearings (REBs) aimed at overcoming the challenge of limited real-world vibration training data. In this study, bearing characteristic frequencies (BCFs) extracted from a novel hybrid method combining cepstrum pre-whitening (CPW) and high-pass filtering developed by the authors’ group are used as input features, and a two-stage approach is taken to develop an intelligent REB fault detect and diagnosis model. In the first stage, various machine learning (ML) methods, including support vector machine (SVM), multinomial logistic regressions (MLR), and artificial neural networks (ANN), are evaluated to identify faulty bearings from healthy ones. The best-performing ML model is selected for this stage. In the second stage, a similar evaluation is conducted to find the most suitable ML technique for bearing fault classification. The model is trained and validated using vibration data from an EU Clean Sky2 I2BS project (An EU Clean Sky 2 project ‘Integrated Intelligent Bearing Systems’ collaborated between Schaeffler Technologies and the University of Southampton. Safran Aero Engines was the topic manager for this project) and tested on datasets from Case Western Reserve University (CWRU) and the US Society for Machinery Failure Prevention Technology (MFPT). The results show that the two-stage model, using an SVM with a polynomial kernel function in Stage-1 and an ANN with one hidden layer and 0.05 dropout rate in Stage-2, can successfully detect bearing conditions in both test datasets and perform better than the results in literature without the requirement of further training. Compared with a single-stage model, the two-stage model also shows improved performance. Full article
(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control of Power Machinery: Developments and Challenges)
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