A Review of Electric Machine Stator Winding Insulation Diagnostic Signal Processing Methods and Metrics

Stator winding insulation failure is a leading cause of electric machine failure. Early detection of winding insulation deterioration is essential to preventing catastrophic damage and ultimate electric machine failure. Various condition monitoring and diagnostic methods have been developed to assess insulation health while the machine is in operation. These diagnostic methods depend on different signal processing techniques that are used to extract insulation-sensitive information from measured signals. This paper presents a review of the diagnostic signal processing techniques that have been applied to stator winding insulation condition monitoring, spanning time-domain, frequency-domain, time–frequency-domain and data-driven approaches. Where appropriate, the underlying mathematical formulation of the reviewed technique is presented, the physical basis for its sensitivity to insulation condition monitoring is discussed, and the key strengths and limitations are identified. A comparative analysis with summary tables is provided to highlight the trade-offs between detection sensitivity, computational cost, hardware requirements and practical deployment considerations. The review shows that time- and frequency-domain methods are simple to implement, while time–frequency and data-driven methods generally offer higher performance, but require greater computation and validation. Also, the comparison shows that turn-to-turn and groundwall insulation monitoring have received more research attention, while phase-to-phase remains less developed. This review concludes by identifying the challenges and future research directions needed to advance this field from laboratory demonstrations toward industrial adoption.