Induction Motor Stray Flux Analysis Proposal for Machine Learning Targeted Applications

Stray flux analysis has proven an effective method for electrical machine, and especially induction motor, diagnosis due to its fully non-invasive, affordable approach. State-of-the-art has successfully combatted the most important shortcomings via signal processing and sensor spatial arrangement techniques. The resulting time-frequency domain spectrogram is an information rich, ideal candidate for feature extraction. This work proposes a complete, adaptable stray flux analysis setup for collection of the most common faults' pertinent information with respect to machine learning requirements, compliant with perceived industrial limitations. A representative experimental setup is utilized as proof of concept.