Enhancing LightGBM for Industrial Fault Warning: An Innovative Hybrid Algorithm

Abstract

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.

Using Learning Focal Point Algorithm to Classify Emotional Intelligence

“…The reliance on OpenCV equips the system with sophisticated tools for navigating diverse environmental conditions and capturing nuanced facial expressions, establishing a robust basis for understanding and interpreting the student's emotional states. The precision achieved in this initial step enhances the overall accuracy and effectiveness of the subsequent emotion classification process [22], [23], [24], [25].…”

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

“…This division ratio aims to ensure that the model has good generalization ability, while allowing us to fairly evaluate the performance of the model. The data normalisation we used is shown in Equation (35) and some of the data collection pages are shown in Figure 2.…”

“…We chose the IPSO [15], GWO [34], and improved arithmetic optimization algorithm (SAOA) [35] as benchmark algorithms. These algorithms demonstrate an excellent performance.…”

A New Fault Classification Approach Based on Decision Tree Induced by Genetic Programming

“…A feed loss (s1) XMEAS (6) Reactor feed rate (s6) IDV (7) C header pressure loss (s4) XMEAS (7) Reactor pressure IDV (8) A, B, C feed composition (s4) XMEAS (8) Reactor level IDV (9) D feed temperature (s2) XMEAS ( 9) Reactor temperature IDV (10) C feed temperature (s4) XMEAS (10) Purge rate (s9) IDV (11) Reactor cooling water inlet temperature XMEAS (11) Product separator temperature IDV (12) Condenser cooling water inlet temperature XMEAS (12) Product separator level IDV (13) Reaction kinetics XMEAS (13) Product separator pressure IDV (14) Reactor cooling water valve XMEAS ( 14) Product separator underflow (s10) IDV (15) Condenser cooling water valve XMEAS (15) Stripper level IDV (16) Unknown XMEAS (16) Stripper pressure IDV (17) Unknown XMEAS (17) Stripper underflow (s11) IDV (18) Unknown XMEAS (18) Stripper temperature IDV (19) Unknown XMEAS (19) Stripper steam flow IDV (20) Unknown XMEAS (20) Compressor work IDV (21) The valve for s4 was fixed at steady state XMEAS (21) Reactor cooling water outlet temperature XMEAS (22) Separator cooling water outlet temperature Input Variables XMEAS (23) Reactor feed analysis (s6) Comp. A Name Description XMEAS (24) Reactor feed analysis (s6) Comp.…”

“…Neural networks [11] and fuzzy logic [12,13] are alternative approaches to FDI and pattern recognition that do not rely on explicit mathematical models. These techniques can be applied to both quantitative and qualitative models and have been successfully implemented in different practical applications [14][15][16]. Qualitative model approaches for FDI include techniques such as signed directed graph [17], fault tree [18], K-nearest neighbor [19], qualitative trend analysis [20,21], artificial immune systems [22], Bayesian networks [23], and hybrid strategies [24,25].…”