Extracting Broken-Rotor-Bar Fault Signature of Varying-Speed Induction Motors


An effective way to detect broken-bar faults of squirrel-cage induction motors is to extract the characteristic frequency component in the stator current as a fault signature, or so- called motor current signature analysis (MCSA). However, for inverter-fed motor drive systems, the motor is typically operating under varying-speed, varying-load, and noisy environments, which makes the fault signature extraction a very challenging problem. In this paper, we propose a sparsity- driven and graph-based method to extract the fault signature effectively, where the fault signature is modeled as a sparse component in the frequency domain for each short-time window measurement while gradually changing from window to window in the time-domain. Compared to the conventional short-time Fourier transform-based method, our method is more robust to noise and varying speed operations. Experiments are carried out to demonstrate the effectiveness of the proposed method.


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    •  AWARD    Best paper award at PHMAP 2023
      Date: September 14, 2023
      Awarded to: Dehong Liu, Anantaram Varatharajan, and Abraham Goldsmith
      MERL Contacts: Abraham Goldsmith; Dehong Liu
      Research Areas: Electric Systems, Signal Processing
      • MERL researchers Dehong Liu, Anantaram Varatharajan, and Abraham Goldsmith were awarded one of three best paper awards at Asia Pacific Conference of the Prognostics and Health Management Society 2023 (PHMAP23) held in Tokyo from September 11th to 14th, 2023, for their co-authored paper titled 'Extracting Broken-Rotor-Bar Fault Signature of Varying-Speed Induction Motors.'

        PHMAP is a biennial international conference specialized in prognostics and health management. PHMAP23 attracted more than 300 attendees from worldwide and published more than 160 regular papers from academia and industry including aerospace, production, civil engineering, electronics, and so on.