Estimating Remaining Useful Life in Machines Using Artificial Intelligence: A Scoping Review

The remaining useful life (RUL) estimations become one of the most essential aspects of predictive maintenance (PdM) in the era of industry 4.0. Predictive maintenance aims to minimize the downtime of machines or process, decreases maintenance costs, and increases the productivity of industries. The primary objective of this bibliometric paper is to understand the scope of literature available related to RUL prediction. Scopus database is used to perform the analysis of 1673 extracted scientific literature from the year 1985 to 2020. Based on available published documents, analysis is done on the year-wise publication data, document types, language-wise distribution of documents, funding sponsors, authors contributions, affiliations, document wise citations, etc. to give an in-depth view of the research trends in the area of RUL prediction. The paper also focuses on the available maintenance methods, predictive maintenance models, RUL models, deep learning algorithms for RUL prediction challenges and future directions in the RUL prediction area.