Enhancing Predictive Maintenance Hyperparameter Optimization and Adopted Strategies

Abstract

Maintenance operations constitute a substantial cost element within the manufacturing sector, typically representing 15% to 60% of the plant conversion budget. The optimization of these operations is paramount in reducing costs and avoiding the traditional "Run to Failure" methodology. This research paper investigates machine learning techniques, specifically Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, to enhance the efficacy of predictive maintenance strategies. Utilizing C-MAPSS, NCMAPSS, and the NASA battery dataset, our investigation focuses on predicting machinery's Remaining Useful Life (RUL) and the State of Health (SOH) of lithium-ion batteries.Our findings not only demonstrate the effectiveness of models employing a Parallel CNN-LSTM architecture, further optimized through Genetic Algorithms (GA), but also highlight their potential to significantly enhance prediction accuracy compared to conventional models. For instance, an optimized Parallel CNN-LSTM model applied to the C-MAPSS dataset achieved a Test Root Mean Squared Error (RMSE) of 14.98 and an RMSE of 0.047 on the NASA battery dataset. These results underscore the potential of integrating CNNs with LSTMs to improve predictive maintenance outcomes, thereby reducing maintenance frequency and associated costs while enhancing machine and plant availability. This research opens up exciting possibilities for a more efficient and cost-effective future in the manufacturing sector, offering a promising outlook.This paper's contributions to the academic and practical domains are twofold: It not only illustrates the effective application of machine learning in predictive maintenance, a topic of immediate relevance, but also offers a viable approach to cost reduction and efficiency improvement in manufacturing operations. These practical implications highlight the immediate benefits that can be derived from our research, making it highly applicable in real-world scenarios.