Two General Architectures for Intelligent Machine Performance Degradation Assessment

Markov model is of good ability to infer random events whose likelihood depends on previous events. Based on this theory, hidden Markov model serves as an extension of Markov model to present an event from observations rather than states in Markov model. Moreover, due to successful applications in speech recognition, it attracts much attention in machine fault diagnosis. This paper presents two architectures for machine performance degradation assessment, which can be used to minimize machine downtime, reduce economic loss, and improve productivity. The major difference between the two architectures is whether historical data are available to build hidden Markov models. In case studies, bearing data as well as available historical data are used to demonstrate the effectiveness of the first architecture. Then, whole life gearbox data without historical data are employed to demonstrate the effectiveness of the second architecture. The results obtained from two case studies show that the presented architectures have good abilities for machine performance degradation assessment.