Predictive Maintenance Resource Guide

Summary: This highly influential paper is a foundational survey of condition-based maintenance (CBM) and predictive maintenance techniques. It systematically reviews the state of the art (circa 2006) in machine diagnostics and prognostics for CBM across various industries. The authors define the three key steps of a CBM program -- data acquisition, data processing, and maintenance decision-making -- and emphasize how predictive maintenance optimizes the balance between reactive and scheduled repairs. Major contributions include a comprehensive overview of models and algorithms for fault diagnosis (detecting and isolating failures) and prognosis (predicting remaining useful life) in mechanical systems. The paper also highlights techniques for multi-sensor data fusion given the growing use of multiple condition sensors. Concluding with current practices and future trends, this review became a cornerstone reference that underpins much of the modern research in predictive maintenance, relevant to virtually all machine types.

Summary: This paper is a pioneering work linking predictive maintenance to the emerging concepts of Industry 4.0 and big data. It outlines a vision of smart factories where machines form a connected, collaborative ecosystem and highlights the need for "smart predictive informatics" to handle the massive data streams in such environments. Lee et al. discuss the transformation of manufacturing toward a service-oriented model (servitization) and how advanced predictive maintenance tools can improve transparency, decision-making, and productivity in a big-data-driven factory. Key contributions include framing a Cyber-Physical System (CPS) based architecture for maintenance, and emphasizing the role of IoT connectivity and cloud analytics in enabling real-time health monitoring and failure prediction across machine fleets.

Summary: This recent paper offers a comprehensive open-access review of the state-of-the-art in data-driven predictive maintenance, reflecting the latest (2020s) research progress. The authors survey modern machine prognostics techniques enabled by emerging sensing and AI/ML tools. Key contributions include a structured categorization of data-driven approaches for predictive maintenance and an overview of their applications across multiple fields (manufacturing, energy, transportation, etc.), underscoring the cross-domain relevance of the techniques. The paper first recaps fundamental methodologies (from classical machine learning models to deep learning architectures) used for equipment health prognostics. It then reviews various application areas for these prognostic methods, illustrating how predictive maintenance is implemented in practice for different types of machinery. Finally, it discusses current challenges, opportunities, and future trends -- such as handling big data, model uncertainty, and integrating domain knowledge -- to guide ongoing research.