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28 February 2024 Prediction of remaining useful life of lubricating oil based on optimal BP neural network
Zhongxin Liu, Huaiguang Wang, Dinghai Wu, Liqiang Song, Baojian Yang
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Proceedings Volume 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023); 130710Q (2024) https://doi.org/10.1117/12.3025457
Event: International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 2023, Shenyang, China
Abstract
Choosing an appropriate lubricating oil replacement strategy is crucial for the machine’s operation and maintenance. Based on the concept of condition-based maintenance (CBM), this article proposes a method for predicting the remaining useful life (RUL) of lubricating oil using lubrication condition monitoring (LCM) data and machine learning (ML) theory. Firstly, obtain lubricating oil samples through engine bench tests and quantitatively analyze the elemental content of the lubricating oil in use using atomic emission spectroscopy (AES). Then, a method for finding the optimal back propagation (BP) neural network was proposed to construct a lubricating oil RUL prediction model. The content of 12 elements in lubricating oil is used as the input variable, and the three states of lubricating oil are used as the output variable. Finally, by comparing with the lubricating oil RUL prediction model based on support vector machine (SVM), it is shown that the proposed optimal BP neural network model has better accuracy and robustness.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Zhongxin Liu, Huaiguang Wang, Dinghai Wu, Liqiang Song, and Baojian Yang "Prediction of remaining useful life of lubricating oil based on optimal BP neural network", Proc. SPIE 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 130710Q (28 February 2024); https://doi.org/10.1117/12.3025457
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KEYWORDS
Neural networks
Neurons
Artificial neural networks
Data modeling
Statistical analysis
Metals
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