Recognizing Commutator Motors Fault from Acoustics Signals Using Bayesian Functional Data Depth

Abstract

Monitoring and diagnostics of commutator motors are very important in order to ensure their reliability. They are often used in domestic and industrial devices. Algorithms for fault detection and isolation allow for the extension of system life, the reduction of system interruption, and can lead to significant savings. In this paper we discuss how Bayesian functional data depth can be used to detect faults.