A Three-Step Framework for Multimodal Industrial Process Monitoring Based on DLAN, TSQTA, and FSBN

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.

Section: Literature Reviewmentioning

confidence: 99%

Enhancing LightGBM for Industrial Fault Warning: An Innovative Hybrid Algorithm

Li,

Jin,

Dogani

et al. 2024

“…For instance, Wu et al formulated a combined approach that integrated a deep local adap-tive network, dual-phase qualitative trend analysis, and a five-state Bayesian network. This method turned aberrant variable continuous data into trend state data, serving fault detection, identification, and diagnosis [8]. Luo et al introduced an online monitoring technology based on SCADA data to enhance prediction reliability.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As the tent mapping may have small cycles and unstable periodic points, we introduce a random variable rand(0, 1) × 1/N into the Tent mapping function to improve it. The updated expression is described in Equation (8).…”

Section: Population Initializationmentioning

confidence: 99%

“…where y i is the chaotic sequence generated by Equation ( 8), x i is the mapped ESCSO individual, and ub and lb represent the upper and lower bounds of the search space, respectively. Generally, the value of σ is set to be between (0, 1), so we also choose its value range to be (0, 1), and we first randomly generate y old(i) in the (0, 1) search space, and then chaotically map it according to Equation (8) to generate a new mapped particle y new(i) , and then. Finally, we generate the final initial solution xi according to Equation (9).…”

Section: Population Initializationmentioning

confidence: 99%

See 1 more Smart Citation

A Fault Warning Approach Using an Enhanced Sand Cat Swarm Optimization Algorithm and a Generalized Neural Network

Pi,

Tan,

Golmohammadi

et al. 2023

With the continuous development and complexity of industrial systems, various types of industrial equipment and systems face increasing risks of failure during operation. Important to these systems is fault warning technology, which can timely detect anomalies before failures and take corresponding preventive measures, thereby reducing production interruptions and maintenance costs, improving production efficiency, and enhancing equipment reliability. Machine learning techniques have proven highly effective for fault detection in modern production processes. Among numerous machine learning algorithms, the generalized neural network stands out due to its simplicity, effectiveness, and applicability to various fault warning scenarios. However, the increasing complexity of systems and equipment presents significant challenges to the generalized neural network. In real-world scenarios, it suffers from drawbacks such as difficulties in determining parameters and getting trapped in local optima, which affect its ability to meet the requirements of high efficiency and accuracy. To overcome these issues, this paper proposes a fault warning method based on an enhanced sand cat swarm optimization algorithm combined with a generalized neural network. First, we develop an enhanced sand cat swarm optimization algorithm that incorporates an improved chaotic mapping initialization strategy, as well as Cauchy mutation and reverse elite strategies based on adaptive selection. Subsequently, we utilize this algorithm to optimize the generalized neural network and determine its optimal parameters, effectively improving the accuracy and reliability of system fault warnings. The proposed method is validated using actual industrial system data, specifically for generator fault warning, and is demonstrated to outperform other advanced fault warning techniques. This research provides valuable insights and promising directions for enhancing industrial fault warning capabilities.

“…They developed a model using machine learning on distributed fiber optic sensor signals that can accurately detect and identify damage events in real-time. Wu et al [8] proposed a comprehensive strategy that integrates a deep local adaptive network, dual-phase qualitative trend analysis, and a five-state Bayesian network. This strategy transforms abnormal variable continuous data into trend state data for fault detection, identification, and diagnosis, offering a new technical means for fault warning.…”

Section: Introductionmentioning

confidence: 99%

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

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.