USING MACHINE LEARNING TO INCREASE THE DEPENDABILITY OF PREDICTIVE MAINTENANCE

The purpose of this paper is to examine reliability as it pertains to predictive maintenance (PdM), specifically looking at how ML integration can improve performance. The four pillars of dependability, availability, safety, maintainability, and reliability, form a composite measure. The paper assesses the shortcomings of conventional PdM techniques, pointing out that threshold-triggered and time-based systems are prone to false alarms, slow fault detection, and wasteful use of resources, among other problems. Data quality, appropriate feature extraction, and complete sensor coverage are emphasised as essential components of ML-based PdM systems, which are dependably impacted by data characteristics, feature engineering, and sensor architecture. In order to enhance trust, transparency, and operational alignment, a structured framework is suggested that incorporates explainability mechanisms with ML models. This framework then embeds predicted outputs into maintenance workflows. The paper shows that in order to achieve reliable PdM, a comprehensive strategy is needed. This strategy should incorporate strong data governance, optimised sensor deployment, advanced feature engineering, continuous improvement, workforce training, and more. Achieving resilient and dependable PdM performance in modern industrial environments requires integrating technical, organisational, and human elements, according to the study.