An approach to fault diagnosis of vacuum cleaner motors based on sound analysis
This paper addresses the problem of the detailed quality end-test of vacuum cleaner motors at the end of the manufacturing cycle. For the prototyping purposes a test rig has been constructed and is presented in short. The diagnostic system built hereto takes advantage of vibration, sound and commutation analysis as well as parity relation checks. The paper focuses on the sound analysis module and provides two main contributions. First, an analysis of sound sources is performed and a set of appropriate features is suggested. Second, efficient signal processing algorithms are developed in order to detect and localise bearing faults, defects in fan impeller, improper brushcommutator contacts and rubbing of rotating surfaces. A thorough laboratory study shows that the underlying diagnostic modules provide accurate diagnosis, high sensitivity with respect to faults, and good diagnostic resolution.
sound analysis signal processing fault detection electrical motors hilbert transform