Harnessing machine learning for predictive maintenance in energy infrastructure: A review of challenges and solutions

Abstract

Predictive maintenance (PdM) has emerged as a vital strategy for optimizing the reliability and efficiency of energy infrastructure. In this paper, we present a comprehensive review of the challenges and solutions associated with harnessing machine learning (ML) techniques for predictive maintenance in the energy sector. The adoption of ML algorithms in predictive maintenance holds immense promise for mitigating equipment failures, reducing downtime, and optimizing maintenance schedules. However, several challenges impede the effective implementation of ML-based PdM strategies. These challenges include the need for large and high-quality data sets, the complexity of integrating heterogeneous data sources, and the interpretability of ML models in real-world settings. To address these challenges, we discuss various solutions and best practices. These include data preprocessing techniques to handle noisy and incomplete data, feature engineering methods for extracting meaningful insights, and model interpretability approaches for enhancing trust and understanding of ML predictions. Additionally, we explore the integration of domain knowledge and human expertise into ML algorithms to improve predictive accuracy and relevance. Furthermore, we examine the role of edge computing and distributed ML techniques in enabling real-time predictive maintenance, particularly in remote or resource-constrained environments. We also discuss the importance of regulatory compliance, privacy protection, and ethical considerations in the deployment of ML-based PdM solutions.