Artificial Intelligence-Enabled Predictive Maintenance for the Resilient Manufacturing: Current Applications and Challenges

Maintenance, manufacturing, and quality constitute the foundational pillars of any thriving manufacturing industry. The integrity of these pillars directly impacts a firm’s competitive standing, as the breakdown of any can result in significant disadvantage. Maintenance ensures the optimal functioning of machinery and equipment, and any unscheduled downtime can lead to substantial losses, accounting for an average of 11% of annual turnover for Fortune Global 500 companies. Predictive Maintenance (PdM) emerges as a solution employing data and analytics to prognosticate equipment failures, enabling pre-emptive maintenance measures that drastically curtail the risk of unscheduled disruptions. The ascendance of AI/ML technologies adds a novel dimension to PdM. The prowess of AI/ML in scrutinizing extensive datasets and discerning intricate patterns unapparent to human analysts is increasingly pivotal. Through this analytical prowess, predictive models materialize, offering accurate prognostications of potential machine failures. Moreover, the integration of Resilient Manufacturing practices enhances the robustness of PdM strategies, ensuring the manufacturing system’s adaptability and recovery from disruptions. This review article presents a comprehensive survey of cutting-edge research concerning the fusion of AI and ML with PdM and Resilient Manufacturing. Additionally, it delves into the pragmatic challenges intertwined with the real-world integration of these transformative technologies.