E3S Web Conf.
Volume 252, 20212021 International Conference on Power Grid System and Green Energy (PGSGE 2021)
|Number of page(s)||5|
|Section||Power Control Technology and Smart Grid Application|
|Published online||23 April 2021|
A Novel Vehicle Gearbox Fault Diagnosis Approach Based on Collective Anomaly Detection
School of Economic and Management, Hefei University, Hefei, 230601, China
* Corresponding author’s e-mail: firstname.lastname@example.org
Targeting the problem of gearbox fault diagnosis, we proposed a novel semi-supervised approach based on collective anomaly detection. Based on the limited sample data, the principle of the approach is to detect whether a test dataset contains abnormal patterns by using data distribution as the metric. The sequence obeying unexpected distribution will be identified as collective anomaly, which may be generated by fault patterns. The approach consists of three steps. First, the mixture of multivariate Gaussian distribution is used to fit the structure of sample dataset and test dataset. Then, based on maximum likelihood estimate algorithm, we hope to search the optimal parameters which can fit the data distribution with the highest degree. Finally, the fixed point iteration algorithm is used to solve likelihood estimate functions. Experimental results demonstrate that the proposed approach can be used to find fault patterns of gearbox without the prior knowledge of their generated mechanisms.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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