A Study on the Anomaly Detection of Engine Clutch Engagement/Disengagement Using Machine Learning for Transmission Mounted Electric Drive Type Hybrid Electric Vehicles
| Autor: | Jihwan Kim, Yonghyeok Ji, Yeongjin Cho, Jaesung Bang, Hyeongcheol Lee, Howon Seo, Seongyong Jeong |
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| Rok vydání: | 2021 |
| Předmět: |
Technology
QH301-705.5 Computer science QC1-999 one-class SVM Machine learning computer.software_genre Convolutional neural network Fault detection and isolation multi-layer perceptron (MLP) General Materials Science Clutch Biology (General) hybrid electric vehicle QD1-999 Instrumentation Fluid Flow and Transfer Processes convolutional neural network (CNN) business.industry long short-term memory (LSTM) Physics Process Chemistry and Technology General Engineering Process (computing) engine clutch engagement/disengagement transmission mounted electric drive Engineering (General). Civil engineering (General) Perceptron fault detection anomaly detection Computer Science Applications Support vector machine Chemistry machine learning Anomaly detection Artificial intelligence TA1-2040 business computer Test data |
| Zdroj: | Applied Sciences, Vol 11, Iss 10187, p 10187 (2021) Applied Sciences Volume 11 Issue 21 |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app112110187 |
| Popis: | Transmission mounted electric drive type hybrid electric vehicles (HEVs) engage/disengage an engine clutch when EV↔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. |
| Databáze: | OpenAIRE |
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