Synergising an Advanced Optimisation Technique with Deep Learning: A Novel Method in Fault Warning Systems

Jia Tian (), Xingqin Zhang, Shuangqing Zheng, Zhiyong Liu and Changshu Zhan ()
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Jia Tian: Cangdong Power Plant, Cangzhou 061113, China
Xingqin Zhang: Transportation College, Northeast Forestry University, Harbin 150040, China
Shuangqing Zheng: Cangdong Power Plant, Cangzhou 061113, China
Zhiyong Liu: Cangdong Power Plant, Cangzhou 061113, China
Changshu Zhan: Transportation College, Northeast Forestry University, Harbin 150040, China

Mathematics, 2024, vol. 12, issue 9, 1-25

Abstract: In the realm of automated industry and smart production, the deployment of fault warning systems is crucial for ensuring equipment reliability and enhancing operational efficiency. Although there are a multitude of existing methodologies for fault warning, the proficiency of these systems in processing and analysing data is increasingly challenged by the progression of industrial apparatus and the escalating magnitude and intricacy of the data involved. To address these challenges, this research outlines an innovative fault warning methodology that combines a bi-directional long short-term memory (Bi-LSTM) network with an enhanced hunter–prey optimisation (EHPO) algorithm. The Bi-LSTM network is strategically utilised to outline complex temporal patterns in machinery operational data, while the EHPO algorithm is employed to meticulously fine-tune the hyperparameters of the Bi-LSTM, aiming to enhance the accuracy and generalisability of fault warning. The EHPO algorithm, building upon the foundational hunter–prey optimisation (HPO) framework, introduces an advanced population initialisation process, integrates a range of strategic exploration methodologies, and strengthens its search paradigms through the incorporation of the differential evolution (DE) algorithm. This comprehensive enhancement aims to boost the global search efficiency and accelerate the convergence speed of the algorithm. Empirical analyses, conducted using datasets from real-world industrial scenarios, have validated the improved warning performance of this proposed methodology against some benchmark techniques, as evidenced by superior metrics such as root mean square error (RMSE) and mean absolute error (MAE), albeit with a slight increase in computational resource requirements. This study not only proposes a novel paradigm for fault warning within complex industrial frameworks but also contributes to the discourse on hyperparameter optimisation within the field of machine learning algorithms.

Keywords: industrial anomaly warning; deep learning architecture; enhanced optimisation approach; hyperparameter adjustment; synergising algorithms (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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