A constrained-time-based algorithm for vehicle maintain prediction

Qingping Wang; Haowen Wang; Haixia Pan

doi:10.1117/12.2689850

10 August 2023 A constrained-time-based algorithm for vehicle maintain prediction

Qingping Wang, Haowen Wang, Haixia Pan

Proceedings Volume 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023); 127481H (2023) https://doi.org/10.1117/12.2689850
Event: 5th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2023), 2023, Wuhan, China

Abstract

With the rapid development of big data and artificial intelligence, machine learning algorithms have become a popular topic in fault diagnosis and preventive maintenance. This paper focuses on predicting vehicle maintenance and proposes a constrained-time-based model using a Seq2Seq neural network structure based on GRU (gated recurrent unit) to extract the dependency relationship between vehicle maintenance time series data. Our proposed method effectively addresses the problem of the traditional method ignoring the temporal nature of vehicle maintenance data and enhancing the model's generalization ability. We also introduce an attention mechanism to automatically obtain key input time points significantly related to the current prediction output. Furthermore, we expand the model's input by concatenating other features of the vehicle attributes as constraints with the decoder's output to help the model better understand the input sequence. We conducted comparative experiments using real-world data. The results demonstrate that the constrained Seq2Seq model has better prediction performance than the traditional method, indicating that our proposed method has promising application prospects in vehicle maintenance prediction

Citation Download Citation

Qingping Wang, Haowen Wang, and Haixia Pan "A constrained-time-based algorithm for vehicle maintain prediction", Proc. SPIE 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023), 127481H (10 August 2023); https://doi.org/10.1117/12.2689850

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