A Hybrid Algorithm Based on Social Engineering and Artificial Neural Network for Fault Warning Detection in Hydraulic Turbines

Tan, Yun; Zhan, Changshu; Pi, Yanxia; Zhang, Chunhui; Song, Jinghui; Yan, Cheng; Golmohammadi, Amir-Mohammad

doi:10.3390/math11102274

Cited by 6 publications

(12 citation statements)

References 44 publications

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“…The method enabled early warning and maintenance planning for distribution network faults. In the wind turbine field, Tan et al [15] improved the social engineering optimizer method for optimizing BP networks in hydraulic turbine fault warning systems. Experimental results showed superior performance compared to other methods.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing LightGBM for Industrial Fault Warning: An Innovative Hybrid Algorithm

Li,

Jin,

Dogani

et al. 2024

Processes

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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.

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Section: Literature Reviewmentioning

confidence: 99%

Enhancing LightGBM for Industrial Fault Warning: An Innovative Hybrid Algorithm

Li,

Jin,

Dogani

et al. 2024

Processes

View full text Add to dashboard Cite

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“…where N(0,1) represents a random number following the ecological distribution between 0 and 1. If p < 0.5, we choose the random walk strategy for optimization, as shown in Equations ( 14) and (15).…”

Section: Proposed Icoa 221 Population Initializationmentioning

confidence: 99%

“…Here, to validate the effectiveness of the algorithm, we compare the proposed algorithm with ISEO-BP [15], SSAPSO-LightGBM [4], and MSBOA-Bi-LSTM [25]. These algorithms have demonstrated excellent performance in the field of fault warning and have been verified by real-world industrial cases.…”

Section: Algorithm Performance Analysismentioning

confidence: 99%

“…Zhao et al [14] studied sensor data using a deep learning strategy, specifically a deep autoencoder network, to provide early warnings about potential faults in wind turbine components. Tan et al [15] enhanced the SEO method in the field of wind turbines to optimize the BPNN, the performance of which surpasses other methods according to experimental results.…”

Section: Introductionmentioning

confidence: 99%

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Integrating Improved Coati Optimization Algorithm and Bidirectional Long Short-Term Memory Network for Advanced Fault Warning in Industrial Systems

Ji,

Dogani,

Jin

et al. 2024

Processes

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In today’s industrial landscape, the imperative of fault warning for equipment and systems underscores its critical significance in research. The deployment of fault warning systems not only facilitates the early detection and identification of potential equipment failures, minimizing downtime and maintenance costs, but also bolsters equipment reliability and safety. However, the intricacies and non-linearity inherent in industrial data often pose challenges to traditional fault warning methods, resulting in diminished performance, especially with complex datasets. To address this challenge, we introduce a pioneering fault warning approach that integrates an enhanced Coati Optimization Algorithm (ICOA) with a Bidirectional Long Short-Term Memory (Bi-LSTM) network. Our strategy involves a triple approach incorporating chaos mapping, Gaussian walk, and random walk to mitigate the randomness of the initial solution in the conventional Coati Optimization Algorithm (COA). We augment its search capabilities through a dual population strategy, adaptive factors, and a stochastic differential variation strategy. The ICOA is employed for the optimal selection of Bi-LSTM parameters, effectively accomplishing the fault prediction task. Our method harnesses the global search capabilities of the COA and the sophisticated data analysis capabilities of the Bi-LSTM to enhance the accuracy and efficiency of fault warnings. In a practical application to a real-world case of induced draft fan fault warning, our results indicate that our method anticipates faults approximately two hours in advance. Furthermore, in comparison with other advanced methods, namely, the Improved Social Engineering Optimizer Optimized Backpropagation Network (ISEO-BP), the Sparrow Particle Swarm Hybrid Algorithm Optimized Light Gradient Boosting Machine (SSAPSO-LightGBM), and the Improved Butterfly Optimization Algorithm Optimized Bi-LSTM (MSBOA-Bi-LSTM), our proposed approach exhibits distinct advantages and robust prediction effects.

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“…The construction of engineering systems will inevitably be affected by complex uncertain factors [1][2][3]. Especially with the increasing complexity of engineering systems, various uncertainties will be mixed and difficult to distinguish [4]. If these factors cannot be analyzed accurately, the reliability and security of the engineering system cannot be guaranteed [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A Coupled Simulated Annealing and Particle Swarm Optimization Reliability-Based Design Optimization Strategy under Hybrid Uncertainties

Yang,

Wang,

et al. 2023

Mathematics

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As engineering systems become increasingly complex, reliability-based design optimization (RBDO) has been extensively studied in recent years and has made great progress. In order to achieve better optimization results, the mathematical model used needs to consider a large number of uncertain factors. Especially when considering mixed uncertainty factors, the contradiction between the large computational cost and the efficiency of the optimization algorithm becomes increasingly fierce. How to quickly find the optimal most probable point (MPP) will be an important research direction of RBDO. To solve this problem, this paper constructs a new RBDO method framework by combining an improved particle swarm algorithm (PSO) with excellent global optimization capabilities and a decoupling strategy using a simulated annealing algorithm (SA). This study improves the efficiency of the RBDO solution by quickly solving MPP points and decoupling optimization strategies. At the same time, the accuracy of RBDO results is ensured by enhancing global optimization capabilities. Finally, this article illustrates the superiority and feasibility of this method through three calculation examples.

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A Hybrid Algorithm Based on Social Engineering and Artificial Neural Network for Fault Warning Detection in Hydraulic Turbines

Cited by 6 publications

References 44 publications

Enhancing LightGBM for Industrial Fault Warning: An Innovative Hybrid Algorithm

Enhancing LightGBM for Industrial Fault Warning: An Innovative Hybrid Algorithm

Integrating Improved Coati Optimization Algorithm and Bidirectional Long Short-Term Memory Network for Advanced Fault Warning in Industrial Systems

A Coupled Simulated Annealing and Particle Swarm Optimization Reliability-Based Design Optimization Strategy under Hybrid Uncertainties

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