In-depth fault diagnosis of small universal motors based on acoustic analysis

The traditional techniques for assessing end quality of electrical motors rely on techniques such as vibration analysis, analysis of electrical current and parity relations between electrical and mechanical quantities. However, for certain types of small universal motors these techniques fail to provide reliable diagnosis for a set of incipient mechanical faults (impact between rotating surfaces). The aim of this paper is to present a complementary approach based on acoustic analysis, which results in unambiguous and reliable diagnostic classification. The underlying case study refers to the fault detection and isolation of vacuum cleaner motors in the framework of quality assessment at the end of the production line. The main contributions of the paper are as follows. First, a detailed analysis of sound sources is performed and a set of appropriate features is suggested. Second, for the purpose of fault detection efficient signal processing algorithms are presented, which enable detection and isolation of faults with mechanical origin and faults with aerodynamic origin. Third, a prototype version of a test rig is designed and applied on a set of test motors. Finally an excerpt from diagnostic system performance evaluation obtained from a vast experimental study is provided.