Research on Fault Indicator for Integrated Fault Diagnosis System of PMSM Based on Stator Tooth Flux
Ke Lv, Dong Wang, Wen Huang, Jinghua Hu
Abstract
Interturn short-circuit fault (ISF), insulation fault (INF), eccentricity fault (ECF), and demagnetization fault (DMF) are the most common faults in permanent magnet synchronous motors (PMSMs). As these faults degrade their reliability, an integrated fault diagnosis method that can diagnose multiple faults is necessary. Fault indicator (FI) is the basis of fault diagnosis. In current methods, a single FI makes it difficult to diagnose the above four faults. In this article, an FI composed of stator tooth flux (STF) with multiple teeth is proposed. The fault features extracted by deep learning networks from the proposed FI can diagnose the four investigated faults. First, the STF of multiple teeth under four faults is analyzed to understand the effects of these faults on flux at different positions. Then, the FI is constructed based on analyzed characteristics. The proposed FI exhibits excellent performance in relatively few STFs (different teeth). Finally, the effectiveness and generality of the proposed FI are verified by a deep learning network based on simulation data and experimental data. The proposed FI provides a significant reference for the diagnosis of multiple faults in PMSM.