期刊
APPLIED SCIENCES-BASEL
卷 11, 期 21, 页码 -出版社
MDPI
DOI: 10.3390/app112110187
关键词
fault detection; anomaly detection; hybrid electric vehicle; transmission mounted electric drive; engine clutch engagement/disengagement; machine learning; multi-layer perceptron (MLP); long short-term memory (LSTM); convolutional neural network (CNN); one-class SVM
类别
资金
- Industrial Strategic Technology Development Program - Ministry of Trade, Industry & Energy (MOTIE, Korea) [20010132]
This study focused on using machine learning methods to detect anomalies in the engine clutch engagement/disengagement process of hybrid electric vehicles. The one-class SVM-based models were found to have the highest anomaly detection performance. It was also concluded that configuring the training architecture to determine normal/anomaly by data instance and conducting one-class classification is effective for anomaly detection in the target data.
Transmission mounted electric drive type hybrid electric vehicles (HEVs) engage/disengage an engine clutch when EV & LRARR;HEV mode transitions occur. If this engine clutch is not adequately engaged or disengaged, driving power is not transmitted correctly. Therefore, it is required to verify whether engine clutch engagement/disengagement operates normally in the vehicle development process. This paper studied machine learning-based methods for detecting anomalies in the engine clutch engagement/disengagement process. We trained the various models based on multi-layer perceptron (MLP), long short-term memory (LSTM), convolutional neural network (CNN), and one-class support vector machine (one-class SVM) with the actual vehicle test data and compared their results. The test results showed the one-class SVM-based models have the highest anomaly detection performance. Additionally, we found that configuring the training architecture to determine normal/anomaly by data instance and conducting one-class classification is proper for detecting anomalies in the target data.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据