An Explainable AI and Machine Learning Framework for Integrated Predictive Maintenance, Structural Health Monitoring, Energy Optimization, and Reliability Assessment Across Mechanical, Civil, and Electrical Engineering Systems

Artificial intelligence (AI) and machine learning can be used to track engineering assets, foresee faults and make operational decisions. But when engineers are not able to determine evidence behind their predictions or to evaluate uncertainty, predictions provide limited support. This review explores the use of explainable machine learning in mechanical predictive maintenance, civil structural health monitoring, electrical energy optimization, and reliability assessment of these applications. It examines the similarities and differences in their information sources, what they predict, what they need explained and what they need validated. A conceptual framework is suggested connecting data collection and prediction, explanation, reliability assessment and engineering action. It demonstrates how an alert may be interpretable and meaningful to construction and structural assessment, maintenance programmes and energy management, including the effect of failure. Typical issues are poor sensor information, inadequate failure history, varying operating conditions, and plausible explanations that are unstable. Expressions of uncertainty that are physically meaningful can enhance the relevance of the framework. It has yet to be tested for its performance and decision benefits; evaluation of its effects across assets and sites is needed to determine if explanations influence engineering decisions. In the case of errors that could potentially impact safety or continuity of service, engineers should continue to monitor the situation.