Integrated Asset Lifecycle Management for Rotating and Static Equipment Using Machine Learning: A Review

Abstract

Abstract The accelerated pace of digital advancement has propelled organizations’ adaptation of sensor technologies in the operations and maintenance of the production facilities to stay relevant. Together with it, the industry is also encountering post-pandemic challenges in logistics, costs and the commitment towards the net zero environmental targets in 2050. Further, without a sound understanding of machine learning and statistical techniques, an organization might fail to harness the optimum value of sensor data. Thus, this paper provides an overview of various techniques used in operations and maintenance of rotating and static equipment. The paper aims to find an integrated model that can anticipate equipment failure and optimize the spare parts replacement time, ultimately optimizing decision-making in managing the asset's lifecycle.