Fault diagnosis using time domain measurements

Abstract

A voting technique is used to diagnose fault conditions down to component level in a feedback control system using only the input-output cross-correlation function measured at suitable time delays. These time delays are chosen using a new formula based on Bayes's theorem, which ranks the time delays in order of usefulness in fault diagnosis; it can be readily applied at the design stage, thus assisting the integration of system design and test functions. The fault conditions necessary to set up the scheme may be obtained by direct fault generation in an actual system, or by simulation of the system mathematical model. A learning approach to the design of a fault diagnosis scheme is described which makes full use of any available failure data, together with the ranking formula for time delay selection, in the creation of an optimum scheme.Results obtained on a complex electro-hydraulic servo are presented which show that the scheme works satisfactorily in the presence of measurement noise and parameter drift, two factors which often cause a breakdown in conventional pattern recognition techniques. The computational requirements are extremely modest, and are well within the capacity of present day mini-computers proposed for use in automatic test equipment. In many instances, the scheme is suitable for manual and partially automated test sets, and can be used for fault diagnosis of a wide range of circuits and systems.