Systems Science & Control Engineering

As train loads and travel speeds have increased over time, railway axle bearings, one of the most critical components, are vulnerable to various faults due the extreme stresses which they encounter in operational use. Axle bearing damage can result in potentially catastrophic failures which can cause severe disruptions or even dangerous derailments, including significant costs for railway industries and even large-scale losses of human life.

The earlier that axle bearing failures can be detected and diagnosed, the more successful and cost-effective the maintenance regimes can be, and by reducing the incidence of urgent and costly unplanned interventions, the risks of significant impacts upon performance and safety are minimized.

Whilst there is a growing awareness that early fault diagnosis is a critical factor in bearing diagnostics, there is still a need for more effective sensing technology, and more powerful signal processing methods are still required to achieve more reliable and practical fault diagnosis of axle bearings which can be conducted dynamically under normal operating conditions.

This Collection aims to encourage original research and review articles related to advanced signal processing methods, accurate modelling, effective sensing technologies and systems for health monitoring and fault diagnosis of railway axle bearings in high-speed trains. Featuring original scientific research, it is expected that this Collection will have a broad impact, but will also showcase a number of relevant contemporary subtopics which might include, but are not necessarily limited to:

• Signal and image processing for fault diagnosis
• Root cause analysis and troubleshooting of faults/failures
• Modelling techniques for fault diagnosis
• Experimental/operational modal analysis for fault diagnosis
• Condition monitoring methods, technologies and systems
• Advanced and novel sensing techniques for fault diagnosis
• High performance computing and edge computing for fault diagnosis
• Non-destructive testing (NDT) methods for fault diagnosis
• Lubrication and tribology for fault diagnosis
• Machine learning and artificial intelligence for fault diagnosis
• Digital twins for fault diagnosis
• Fault/failure prognosis

All manuscripts submitted to this Article Collection will undergo a full peer-review; the Guest Advisors for this collection will not be handling manuscripts. Please review the journal scope and author submission instructions prior to submitting a manuscript.

The deadline for submitting manuscripts is 30 September 2024

Please contact Alex Johnson at [email protected] with any queries regarding this Article Collection.

Article Collection Guest Advisors

Professor Fengshou Gu is an expert in the fields of vibro-acoustics analysis and machinery diagnosis, with over 30 years of research experience. He is the author of over 500 technical and professional publications in machine dynamics, signal processing, tribology dynamic responses, condition monitoring and related fields. He has been involved in vibro-acoustics characterisation related to internal combustion engines, reciprocating compressors, centrifugal pumps, electric motors, industrial robots, hydraulic power systems, gearboxes, mechanical seals and rolling/journal bearings. He has experience in system modelling, various physical parameter measurements, and advanced signal processing techniques including time-frequency analysis, modulation signal bispectrum, wavelet transforms, machine learning algorithms and statistical analysis.

Professor Yuandong Xu a Professor in the Hunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment at Hunan University of Science and Technology. He has worked in the field of machinery condition monitoring for over nine years in developing advanced signal processing methods, effective and efficient detection and diagnosis approaches, system identification methods, numerical and analytic models, experiment systems, data acquisition software and hardware, and online condition monitoring systems.

Dr Yousif Muhamedsalih is a senior research fellow at the Institute of Railway Research (IRR). He has a wealth of experience working on projects related to inspection and condition monitoring, degradation modelling, wheel-rail interface studies, maintenance optimisation, measurements and signal analysis, railway engineering, multi-body engineering, data processing and data analysis, and control system and instrumentation.

Dr Yao Cheng is an assistant researcher at the State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University. He has many years of research experience in the dynamic model, fault feature extraction and diagnosis of axle box bearings in rail vehicles, and has published over 50 technical and professional publications in related fields. He has led or participated in multiple research projects on axle box bearing bench tests, dynamic characteristics, and fault diagnosis. He has extensive experience in the theory and modelling of axle box bearing dynamics, as well as advanced signal processing technologies.

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