Abstract
Vibration out-of-tolerance is a common condition of unqualified engine inspection, and the cause of vibration out-of-tolerance is closely related to the quality of the engine assembly. When the engine is found to vibrate out of tolerance, it needs to be reassembled. This process is very time-consuming and labor-intensive. In order to improve the assembly quality and reduce the vibration value during the engine assembly process. An explainable optimization algorithm is proposed, which combines LightGBM, SHAP and PSO. First, a vibration value prediction model is trained using the LightGBM algorithm. Second, SHAP is used to explain the vibration value prediction model, and obtain the importance order of each assembly process parameter, which is used as the subsequent optimization order. Finally, according to the optimization order, PSO algorithm is used to iteratively optimize the assembly process parameters and it uses the vibration prediction model as the fitness function. It has been verified by experiments that the optimized assembly process parameters can effectively reduce the vibration value of the engine, which has positive guiding significance for the assembly of the engine.
National Defense Basic Research Program (JCKY2020204C009).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Shen, X., Fan, Q., Bai, S., Ai, Y.: Research on the influence of aero-engine assembly parameters on the vibration of the whole machine based on BP neural network. Aviation Maintenance & Engineering (01), 70–73 (2012)
Yang, A., et al.: Optimization of engine assembly process parameters based on neural network and PSO algorithm. Modern Manufacturing Engineering (02), 105–113 (2022)
Kim, J.Y., Lee, H.S., Oh, J.S.: Study on prediction of ship’s power using light GBM and XGBoost. J. Adv. Marine Eng. Technol. 44(2), 174–180 (2020)
Chen, X., Liu, Y., Li, Q., Li, W., Liu, Z., Guo, W.: Short-term photovoltaic power prediction based on LGBM-XGBoost. In: 2022 5th Asia Conference on Energy and Electrical Engineering (ACEEE), pp. 12–17. IEEE. (2022, July)
Liao, B., Wang, Z.,, Li, M., Sun, R.: XGBoost and SHAP model for football player value prediction and feature analysis. Computer Science (2022-04-13). Forthcoming
Xinnan, L., Lefan, L., Binqing, X., Honghai, Y.: Reexploring the default characteristics of small and micro enterprises: a machine learning model based on SHAP interpretation method. Chinese Journal of Management Science (2022-03-21). Forthcoming
Chen, X.K., Zuo, H.X., Liao, B., Sun, R.N.: Prediction and characteristic analysis model of coronary heart disease based on XGBoost and SHAP. Journal of Computer Applications (06) (2022)
Van den Broeck, G., Lykov, A., Schleich, M., Suciu, D.: On the tractability of SHAP explanations. Journal of Artificial Intelligence Research 74, 851–886 (2022)
Liu, Y., Liu, Z., Luo, X., Zhao, H.: Diagnosis of Parkinson’s disease based on SHAP value feature selection. Biocybernetics and Biomedical Engineering 42(3), 856–869 (2022)
Lee, Y.G., Oh, J.Y., Kim, D., et al.: SHAP value-based feature importance analysis for short-term load forecasting. J. Electr. Eng. Technol. (2022)
Huang, J., He, L.: Application of improved PSO-BP neural network in customer churn warning. Procedia Computer Scoence 131, 1238–1246 (2018)
Yand, W., Li, Q.: A survey of particle swarm optimization algorithms. Engineering Science 5, 87–94 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Peng, H., Yuan, W., Pu, Y., Yang, X., Guan, D., Guo, R. (2023). An Explainable Optimization Method for Assembly Process Parameter. In: Tian, Y., Ma, T., Jiang, Q., Liu, Q., Khan, M.K. (eds) Big Data and Security. ICBDS 2022. Communications in Computer and Information Science, vol 1796. Springer, Singapore. https://doi.org/10.1007/978-981-99-3300-6_28
Download citation
DOI: https://doi.org/10.1007/978-981-99-3300-6_28
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-3299-3
Online ISBN: 978-981-99-3300-6
eBook Packages: Computer ScienceComputer Science (R0)