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: 15 May 2024 | Viewed by 1288

Special Issue 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

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Published Papers (1 paper)

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Research

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
Viewed by 900
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
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