Effectiveness assessment of condition monitoring techniques for railway vehicles

Zhang, Dingcheng (2020). Effectiveness assessment of condition monitoring techniques for railway vehicles. University of Birmingham. Ph.D.

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Abstract

With the development of the global economy and increasing population mobility, the need for railway transportation is dramatically increasing. Key vehicle components, such as axle bearings, operate under harsh conditions making them prone to developing faults. Should these faults lead to failures, there exists the potential for economic loss (due to delays) or even casualties. Hence, condition monitoring (CM) is being adopted as a significant tool within the industry.

There are many CM techniques based on different physical principles, e.g. vibration, acoustic, thermal, etc, each with differing levels of effectiveness. Different CM techniques may also be suitable for application at different stages in the development of a fault. However, the exact effectiveness of specific techniques, and therefore the point in the lifecycle of an asset at which they can be effective, is not known. This results in difficulty and inefficiency when selecting a particular technique for monitoring an asset.

This PhD will consider mechanisms to make quantitative effectiveness assessments of different CM techniques throughout lifecycles of axle bearings. The work includes three main contributions: (1) Improving the effectiveness of wayside acoustic and on-board vibration techniques through using novel signal processing methods; (2) Constructing health indicators using deep learning and transfer learning methods (3) assessing effectiveness of each technique.

Type of Work:

Thesis (Doctorates > Ph.D.)

Award Type:

Doctorates > Ph.D.

Supervisor(s):