Development of Decision Support System (DSS) Framework for Predictive Maintenance of Rolling Element Bearing in the Emerging Era of Industry 4.0

The aim of this paper is to framework the architecture of decision support system (DSS) based on data generated by vibration analyzer for predictive maintenance of rolling element bearing in the emerging era of industry 4.0. Inspired by big data generated in manufacturing plant and necessity of predictive maintenance decision making cell, the framework developed for managers to trigger the decision related to breakdown of machines. This study work has been carried out at one of biggest fabrication facilities in Gujarat which is going towards industry 4.0 paradigm. The case of one of predictive maintenance technique; Vibration analysis has been done for the bearing and the failure prediction of rolling element bearing. For the same case, architecture model of maintenance activities; Majiwala-Gandhi Maintenance Charter Model (MGMC) has been developed to help the managers to make the decision. Same decision support system can be applied to other machine maintenance in manufacturing industries. Results and findings from the study show that current solution algorithms; Majiwala-Gandhi Maintenance Charter Model can be useful to large data sets to lay out executable decisions for managers.