Operational industrial fault detection and diagnosis: railway actuator case studies

Silmon, Joseph A. (2009). Operational industrial fault detection and diagnosis: railway actuator case studies. University of Birmingham. Ph.D.

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Abstract

Modern railways are required to operate with a high level of safety and reliability. The weakest components are those which have the highest safety requirements and the lowest inherent reliability. Single-throw mechanical actuators, such as powered train doors, trainstops, level crossing barriers and switch actuators (point machines) are a group of components which have these properties. Preventative maintenance is carried out periodically in order to mitigate the risks of these actuators failing. This is inefficient: a condition-based maintenance approach would reduce costs and the risks to staff. However, this kind of maintenance requires very accurate automatic condition monitoring. Currently, the threshold-based condition monitoring systems installed in pilot schemes around the country do not have enough insight into actuator performance to detect incipient faults. These are hard to spot because their symptoms develop over a long period of time. It is uneconomical to carry out detailed analysis or modelling, or collect a large amount of training data, for each instance of a large group of assets. Therefore, the solution needed to establish diagnosis rules based on offline analysis, or training data from only one actuator. This thesis draws on previous work in qualitative trend analysis to build a diagnosis system which uses a combined approach of qualitative and quantitative analysis to transfer the knowledge gathered from one actuator to its fellows in service. The method used has been designed to use straightforward components, so that it can be more easily explained to users. Two case studies were carried out in order to verify the system's functions. Data were collected from real-life actuators, under simulation of incipient faults. The diagnosis system then operated on the data. The system's performance was almost as good with real-world data as it was with synthetic data. The system has been a success when operating on the data gathered under laboratory conditions. In the real world, a system such as this could be used to post-process data gathered around the railway network from actuators with local data acquisition equipment. Incipient faults could be detected in the early stages of their development and accurately diagnosed, allowing maintenance effort to be targeted very specifically, saving money, time and exposing staff to fewer hazards.

Type of Work:

Thesis (Doctorates > Ph.D.)

Award Type:

Doctorates > Ph.D.

Supervisor(s):