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JRM Vol.12 No.6 pp. 702-705
doi: 10.20965/jrm.2000.p0702
(2000)

Paper:

Fault Detection of Automobile Transmission Gears Using General Parameter Methods

Yasuhiko Dote*, Seppo J. Ovaska**, and Xiao-Zhi Gao**

*Department of Computer Science and Systems Engineering, Muroran Institute of Technology, 27-1, Mizumoto-cho, Muroran 050-8585, Japan

**Institute of Intelligent Power Electronics, Helsinki University of Technology, Otakaari 5 A, FIN-02150 Espoo, Finland

Received:
September 26, 2000
Accepted:
October 8, 2000
Published:
December 20, 2000
Keywords:
Fault detection, Fault diagnosis, Modeling, Neural networks, Autoregressive process
Abstract

This paper compares the performance of nonlinear Radial Basis Function Network-based (RBFN) and linear AutoRegressive (AR) model-based General Parameter (GP) methods in a fault detection application. We use the efficient GP approach for initializing the weights of the RBFN model in the beginning of the off-line system identification phase, as well as for fine-tuning the modeling accuracy of RBFN and AR models on-line. Our fault detection scheme is based on monitoring the expectation value of the scalar general parameter. This provides improved robustness and detection sensitivity over such methods where the on-line prediction error is used directly in the decision making process. In order to illustrate the performance of the proposed nonlinear and linear schemes, they are applied to fault detection of automobile transmission gears. As the acoustic sound level time-series, providing the necessary basis information for fault detection, is slightly nonlinear, the GPRBFN outperformed the linear methods: the GP-AR method and conventional AR inverse filtering. Both of the GP-based methods provide competitive solutions for real-world fault detection and diagnosis applications.

Cite this article as:
Yasuhiko Dote, Seppo J. Ovaska, and Xiao-Zhi Gao, “Fault Detection of Automobile Transmission Gears Using General Parameter Methods,” J. Robot. Mechatron., Vol.12, No.6, pp. 702-705, 2000.
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Last updated on Oct. 27, 2021