A Critical Overview of the Filterbank-Feature-Decision Methodology in Machine Condition Monitoring

The number of research papers dealing with vibration-based condition monitoring has been exponentially growing in recent decades. As a consequence, one may identify some trends that emerge from this vast literature. The present paper delineates a methodology that can be recognized in several research works, which is rooted in a succession of three stages. The first stage embodies a linear transform of the data, typically in the form of a filterbank, the second stage reduces the dimension of the data through a nonlinear functional, typically in the form of health indicators, and the last stage supplies a statistical decision. Although several variants of this methodology exist, its fundamental principles seem to have converged to a general consensus, at least implicitly. This paper provides a critical overview of this methodology. It discusses its working assumptions under some typical scenarios and formulates several caveats. It also provides a few prospects that may nourish future research.

Automated summary

This paper reviews the trends in vibration-based condition monitoring research and outlines a methodology that has been widely recognized in the field. Despite various variants, the fundamental principles of this methodology seem to have converged to a general consensus. The paper discusses working assumptions, caveats, and potential prospects for future research.