Investigation of Fault Diagnosis and Prognostics Techniques for Predictive Maintenance in Industrial Machinery - Varghese, Aleena; Ande, Janaki Rama Phanendra Kumar; Mahadasa, Ravikiran; Gutlapalli, Sai Srujan; Surarapu, Pavani (Engineering International, 2023)
This research looks into fault detection and prognostic methods for industrial machinery predictive maintenance to maximize equipment dependability, reduce downtime, and improve operational effectiveness. The project aims to investigate integrated fault diagnosis and prognostics methodologies, analyze their applications in different industrial sectors, and determine policy implications to encourage implementation. Peer-reviewed articles, industry reports, case studies, and other current material are thoroughly reviewed as part of the technique. Major conclusions demonstrating the value of integrated fault diagnosis and prognostics in early fault identification, proactive decision-making, and optimal maintenance scheduling have been drawn from case studies in the power generating, petrochemical refining, and automotive manufacturing industries. The policy ramifications encompass the requirement for staff training, data standardization, investment in R&D, and regulatory frameworks to surmount constraints and stimulate innovation in industrial maintenance procedures. Organizations must adopt predictive maintenance technology to maintain competitiveness, cut expenses, and guarantee the dependable operation of vital mechanical assets in changing circumstances.