Research article Special Issues

Application of blind source separation to the health monitoring of electrical and mechanical faults in a linear actuator

  • Received: 27 June 2019 Accepted: 15 October 2019 Published: 01 November 2019
  • This paper proposes an automated fault isolation and diagnostic chain for the health monitoring of a linear actuator composed of a roller screw driven by a permanent magnet synchronous motor. Four health conditions are considered and diagnosed: the healthy condition, a short circuit in the stator windings, a mechanical backlash in the roller screw, and the combination of both faults. In order to separate the fault signatures, empirical mode decomposition is applied to the motor current, followed by independent component analysis, automatic isolation of the fault signatures, and a classification step for the diagnosis. The novelty proposed consists of an automatic processing of the independent components to isolate the effects of the short-circuit from the effects of the backlash. This isolation step, in contrast to earlier works, requires no human intervention to select signals of interest, making it suitable to real-time onboard diagnostics. Furthermore, results show that independent component analysis occupies an important role in the diagnosis: its omission leads to a reduction in the diagnostic performance of the classifier as well as a reduction in measures of class separability.

    Citation: Ryan Michaud, Romain Breuneval, Emmanuel Boutleux, Julien Huillery, Guy Clerc, Badr Mansouri. Application of blind source separation to the health monitoring of electrical and mechanical faults in a linear actuator[J]. AIMS Electronics and Electrical Engineering, 2019, 3(4): 328-346. doi: 10.3934/ElectrEng.2019.4.328

    Related Papers:

  • This paper proposes an automated fault isolation and diagnostic chain for the health monitoring of a linear actuator composed of a roller screw driven by a permanent magnet synchronous motor. Four health conditions are considered and diagnosed: the healthy condition, a short circuit in the stator windings, a mechanical backlash in the roller screw, and the combination of both faults. In order to separate the fault signatures, empirical mode decomposition is applied to the motor current, followed by independent component analysis, automatic isolation of the fault signatures, and a classification step for the diagnosis. The novelty proposed consists of an automatic processing of the independent components to isolate the effects of the short-circuit from the effects of the backlash. This isolation step, in contrast to earlier works, requires no human intervention to select signals of interest, making it suitable to real-time onboard diagnostics. Furthermore, results show that independent component analysis occupies an important role in the diagnosis: its omission leads to a reduction in the diagnostic performance of the classifier as well as a reduction in measures of class separability.


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