A Squeezed Modulation Signal Bispectrum Method for Motor Current Signals Based Gear Fault Diagnosis

Yuandong Xu, Xiaoli Tang, Xiuquan Sun, Fengshou Gu, Andrew D. Ball

Research output: Contribution to journalArticlepeer-review


Electromechanical systems as the prime power source are widely employed in industry. To ensure the high productivity and safety of the motor-gear system, motor current signature analysis (MCSA) becomes a cost-effective and effective approach to health condition monitoring of motors and gears simultaneously. In general, working conditions of the motor-gear system can be separated into stationary and nonstationary working conditions. The nonstationary working conditions usually refer to varying speeds, which have been broadly investigated in the angular domain analysis and time-frequency analysis. The stationary working conditions are assumed to have a constant rotating speed which is an ideal scenario, but practically, the rotating speed varies slightly with randomness. The random speed variation seems neglectable but it actually spreads the energy into adjacent frequency bins, which attenuates the amplitude of fault signatures and leads to inaccurate fault diagnosis. To address this issue, a squeezed modulation signal bispectrum (MSB) approach is developed to concentrate the leaked energy for accurately diagnosing gear faults with motor current signals. The squeezed MSB concentrates the energy in the frequency domain along the time axis to overcome the random speed oscillation induced energy leakage and then demodulates and aligns the modulation fault signatures from the squeezed spectra for ensemble averaging to further enhance fault signatures. The simulation study shows the performance of the proposed method under different levels of random speed variation, and the experimental studies demonstrate the effectiveness of the squeezed MSB for diagnosing gear tooth breakage faults under a wide range of working conditions.

Original languageEnglish
Article number3521508
Number of pages8
JournalIEEE Transactions on Instrumentation and Measurement
Early online date25 Aug 2022
Publication statusPublished - 9 Sept 2022

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  • Employee welfare
  • Frequency modulation
  • Gears
  • Induction motors
  • Modulation
  • Oscillators
  • Random speed variation
  • Squeezed MSB
  • Torque
  • fault diagnosis
  • gears
  • motor current signature analysis


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