Condition monitoring: the case for integrating data from independent sources

Abstract

A case study is presented in which data from two different sources are combined to create robust diagnostic systems. A diesel engine is used as an exemplar of the class of mechanical machines. Several commonly occurring faults symptomatic of early stages of fault development are induced in the engine. Data in the form of cylinder pressures and vibration are acquired. Orthogonal wavelet transforms, principal component analysis and time domain information are used to extract features from the data. Several artificial neural net classifiers are developed using these data. Statistical models are used to evaluate the diversity within the methodologies used to create the classifiers. The ‘diversity’ metrics are used to propose the most effective majority voting system.