On-line machine condition monitoring using artificial neural networks.

Abstract

Modern predictive maintenance strategies often require continuous monitoring of rotating machines condition. By detecting incipient failures, expensive unscheduled downtime can be eliminated, unnecessary periodic maintenance procedures avoided, and potentially dangerous machine failures prevented. Vibration signatures have long been recognized as an effective indicator of machine condition. By observing vibration wave forms in the time, frequency, and cepstral domains, human experts are able to assess accurately the condition of bearings, gears, and other machine components. The expense and limited availability of human experts make it highly desirable to automate the vibration interpretation process. This has proven difficult; human pattern recognition ability has exceeded the most sophisticated computer programs. This paper describes a study where artificial neural networks were trained to emulate the ability of a human expert to detect defective rolling element bearings from their vibration signatures. A cascade of probabilistic and backpropagation neural networks learned to agree with the human expert on 100% of the bearing vibration samples, where these were drawn from data outside of the training set. [Work supported by the Electrical Power Research Institute.]