Fast–ICA for Mechanical Fault Detection and Identification in Electromechanical Systems for Wind Turbine Applications

Recently, the approaches based on source separation are increasingly adopted for the fault diagnosis in several industrial applications. In particular, Independent Component Analysis (ICA) method is attractive, thanks to its simplicity of implementation. In the context of electrical rotating machinery with a variable speed, namely the wind turbine type, the interaction between the electrical and mechanical parts along with the fault is complex. Therefore, the essential system variables are affected and it thereby requires to be analyzed in order to detect the presence of certain faults. In this paper, the target system is the classical association of a doubly-fed induction motor to a two stage gearbox for wind energy application system. The investigated mechanical fault is a uniform wear of two gear wheels for the same stage. The idea behind the proposed technique is to consider the fault detection and identification as a source separation problem. Based on the analysis into independent components, Fast–ICA algorithm is adopted to separate and identify the sources of the gear faults. Afterwards, a spectral analysis is applied on the signals resulting from the separation in order to identify the fault components related to the damaged wheels. The efficiency of the proposed technique for the separation and identification of the fault components is evaluated by numerical simulations.

@article{Farhat2017,
  title     = {Fast–ICA for Mechanical Fault Detection and Identification in Electromechanical Systems for Wind Turbine Applications},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {8},
  number    = {7},
  year      = {2017},
  publisher = {The Science and Information Organization},
  author    = {Mohamed Farhat and Yasser Gritli and Mohamed Benrejeb},
  doi       = {10.14569/IJACSA.2017.080759},
  url       = {https://doi.org/10.14569/IJACSA.2017.080759}
}