Deep Learning Algorithms for Predictive Maintenance in Industrial Machinery

Abstract

Predictive maintenance (PdM) has become one of the most vital technological changes in the industrial machinery that allow organizations to leave the old scheduled maintenance approach and make decisions and actions using data and based on conditions. The improved ability of modeling nonlinear trends, managing high-dimensional sensor data, and learning complex time-series signals makes deep learning an important addition to the PdM in the current Industry 4.0 setting. The paper will give a comprehensive exploration of predictive maintenance technologies based on deep-learning, how they are applied, their comparative advantages, and their practical performance. The paper involves a thorough literature review, an intensive mechanism of creating PdM systems, a carefully-organized analysis of data and results, and a general discussion of the implications to industrial sectors. The study ends with a set of recommendations on how to enhance the adoption of PdM and the management of industrial assets using the deep learning techniques.