An efficient acoustic-based diagnosis of inter-turn fault in interior mount LSPMSM

This paper investigates the use of acoustic signals in diagnosing stator inter-turn faults in Line Start Permanent Magnet Synchronous Motors (LSPMSMs). Singular spectrum analysis (SSA) is proposed to analyze the acoustic signal for fault detection. The proposed methodology involves collection of experimental acoustic data using a smartphone from an interior mount LSPMSM under different loading levels, healthy as well as faulty motor cases where different number of turns are shorted to emulate different fault levels. Acoustic signal for each case is analyzed using SSA, which results in decomposition of the signal into periodic components and noise. Fast Fourier Transform (FFT) of each component is carried out to find the unique frequency representative for each case. The results with the proposed methodology show that it is capable to detect the occurrence of inter-turn fault under different loading levels with the capability of distinguishing fault severity with readily available acoustic sensor of a smart phone. In addition, it is possible to differentiate between the load and no-load modes of operation.