Data-driven Fault Diagnosis Method for Transmission Sensors
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School of Automotive Studies, Tongji University, Shanghai 201804, China

Clc Number:

U461.91

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    Abstract:

    Aiming at the limitations of model-based and rule-based fault diagnosis methods, a data-driven fault diagnosis method for transmission sensors was proposed. First, a residual sequence was obtained between the output of actual sensor and the output of sensor model established by step-wise regression. Then, the residual sequence was decomposed by wavelet packet transform(WPT), and the Shannon entropy of each node was calculated as the feature values. Finally, a probabilistic neural network(PNN) was adopted to identify the feature values of different sensor faults. This method is verified by transmission signals from hardware-in-the-loop platform. Results indicate that the method has a diagnostic accuracy of 98.50%, and the diagnostic accuracy varies little under different sample divisions. In addition, the fault diagnoses of two speed sensors were also performed, and the diagnostic accuracy is at a relatively high value, which proves the applicability of the method.

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    Fig.1 Flow chart of data-driven diagnosis method for transmission sensors
    Fig.2 Structure of WPT-PNN
    Fig.3 Structure of PNN
    Fig.4 Structure of hardware-in-the-loop platform
    Fig.5 Typical signal modes of sensor faults
    Fig.6 Comparison of actual sensor output and sensor model output
    Fig.7 Residual sequence of normal and faulty sensors
    Fig.8 Curve of diagnostic accuracy as a function of smoothing factor
    Fig.9 Comparison of diagnosis results between different methods
    Fig.10 Comparison of diagnosis results for speed sensors
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WU Guangqiang, TAO Yichao, ZENG Xiang. Data-driven Fault Diagnosis Method for Transmission Sensors[J].同济大学学报(自然科学版),2021,49(2):272~279

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  • Received:June 11,2020
  • Online: March 18,2021
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