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Detection method of integrated transmission system anomaly based on improved CNN-LSTM
Published:2024-11-21 author:YAO Haiyang, CHEN Tao, JIA Ran, et al. Browse: 117 Check PDF documents
Detection method of integrated transmission system 
anomaly based on improved CNNLSTM


YAO Haiyang1, CHEN Tao1, JIA Ran1, XING Peixin1, SUN Guangxin2


(1.Key Laboratory of Modern Measurement and Control Technology, Ministry of Education, Beijing Information Science & 

Technology University, Beijing 100192, China; 2.Assembly and Commissioning Room, Beijing Machinery and Equipment 
Research Institute, Beijing 100854, China)


Abstract: To address the significant challenges posed by the harsh and unpredictable operating environments of tracked armored vehicles, leading to the anomaly detection of data in complex comprehensive transmission systems, an innovative anomaly detection method based on an enhanced convolutional neural network (CNN) and long short-term memory (LSTM) hybrid model was proposed. Firstly, the advanced principal component analysis (PCA) and precise sliding window techniques were skillfully utilized to effectively reduce dimensionality and accurately segment the comprehensive transmission monitoring data, thereby substantially enhancing the quality of the data used for critical anomaly detection. Then, an optimized CNNLSTM model was employed to meticulously extract spatial features from the sequence data, with the data categories outputted through a robust fully connected layer. The CNN and LSTM in parallel were ingeniously combined by the sophisticated approach, and a novel residual connection structure was introduced to significantly enhance the network-s learning capability for intricate comprehensive transmission data. Finally, a meticulously constructed experimental platform for comprehensive transmission system anomaly detection was established, and various sensors were strategically arranged to collect the intricate state data of the transmission system, validating the effectiveness of the improved CNN-LSTM method. The research results impressively shows that the refined CNN-LSTM composite model with innovative residual connections achieves an outstanding anomaly detection accuracy of 92.7% on the test set of comprehensive transmission system oil leakage experimental data, with the area under the receiver operating characteristic (ROC) curve (AUC) reaching 0.982, marking a notable improvement of 0.034 compared to the conventional CNN-LSTM. The proposed enhanced CNN-LSTM model demonstrates exceptional robustness and generalization ability, offering a highly viable new approach for data anomaly detection in sophisticated comprehensive transmission devices.

Key words: mechanical transmission device; convolutional neural network(CNN); long short-term memory networks(LSTM); residual connections; principal component analysis(PCA); receiver operating characteristic (ROC); area under curve (AUC)


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