Industrial fault diagnosis: Pneumatic train door case study

Abstract

A practical, robust method of fault detection and diagnosis of a class of pneumatic train door commonly found in rapid transit systems is presented. The methodology followed is intended to be applied within a practical system where computation is distributed across a local data network for economic reasons. The health of the system is ascertained by extracting features from the trajectory profiles of the train door. This is incorporated into a low-level fault detection scheme, which relies upon using simple parity equations. Detailed diagnostics are carried out once a fault has been detected; for this purpose neural network models are utilized. This method of detection and diagnosis is implemented in a distributed architecture resulting in a practical, low-cost industrial solution. It is feasible to integrate the results of the diagnosis process directly into an operator's maintenance information system (MIS), thus producing a proactive maintenance regime.