LAPSE:2024.1302
Published Article
LAPSE:2024.1302
Enhancing LightGBM for Industrial Fault Warning: An Innovative Hybrid Algorithm
Shuai Li, Nan Jin, Azadeh Dogani, Yang Yang, Ming Zhang, Xiangyun Gu
June 21, 2024
The reliable operation of industrial equipment is imperative for ensuring both safety and enhanced production efficiency. Machine learning technology, particularly the Light Gradient Boosting Machine (LightGBM), has emerged as a valuable tool for achieving effective fault warning in industrial settings. Despite its success, the practical application of LightGBM encounters challenges in diverse scenarios, primarily stemming from the multitude of parameters that are intricate and challenging to ascertain, thus constraining computational efficiency and accuracy. In response to these challenges, we propose a novel innovative hybrid algorithm that integrates an Arithmetic Optimization Algorithm (AOA), Simulated Annealing (SA), and new search strategies. This amalgamation is designed to optimize LightGBM hyperparameters more effectively. Subsequently, we seamlessly integrate this hybrid algorithm with LightGBM to formulate a sophisticated fault warning system. Validation through industrial case studies demonstrates that our proposed algorithm consistently outperforms advanced methods in both prediction accuracy and generalization ability. In a real-world water pump application, the algorithm we proposed achieved a fault warning accuracy rate of 90%. Compared to three advanced algorithms, namely, Improved Social Engineering Optimizer-Backpropagation Network (ISEO-BP), Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN), and Grey Wolf Optimizer-Light Gradient Boosting Machine (GWO-LightGBM), its Root Mean Square Error (RMSE) decreased by 7.14%, 17.84%, and 13.16%, respectively. At the same time, its R-Squared value increased by 2.15%, 7.02%, and 3.73%, respectively. Lastly, the method we proposed also holds a leading position in the success rate of a water pump fault warning. This accomplishment provides robust support for the timely detection of issues, thereby mitigating the risk of production interruptions.
Keywords
Arithmetic Optimization Algorithm, fault warning, hybrid algorithm, hyperparameter optimization, LightGBM
Suggested Citation
Li S, Jin N, Dogani A, Yang Y, Zhang M, Gu X. Enhancing LightGBM for Industrial Fault Warning: An Innovative Hybrid Algorithm. (2024). LAPSE:2024.1302
Author Affiliations
Li S: Independent Researchers, No. 76 Chongmingdao East Road, Huangdao District, Qingdao 266000, China
Jin N: Independent Researchers, No. 76 Chongmingdao East Road, Huangdao District, Qingdao 266000, China
Dogani A: Department of Agricultural Economics, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran [ORCID]
Yang Y: Independent Researchers, No. 76 Chongmingdao East Road, Huangdao District, Qingdao 266000, China
Zhang M: Independent Researchers, No. 76 Chongmingdao East Road, Huangdao District, Qingdao 266000, China
Gu X: Independent Researchers, No. 76 Chongmingdao East Road, Huangdao District, Qingdao 266000, China
Journal Name
Processes
Volume
12
Issue
1
First Page
221
Year
2024
Publication Date
2024-01-19
Published Version
ISSN
2227-9717
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PII: pr12010221, Publication Type: Journal Article
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LAPSE:2024.1302
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doi:10.3390/pr12010221
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Jun 21, 2024
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