SME 4.0: Health Monitoring of Maintenance Management Approaches in Smart Manufacturing

Now a days Small and Medium sized Enterprises (SMEs) are also interested in lean with smart manufacturing due to the increasing demand of the product and customer satisfaction in the developing country. In that situation, most of the SMEs addressed in the developing countries are facing lots of hurdles and challenges for converting their traditional manufacturing environment into a smart environment. The most important reason behind that digital transformation is the impact and the application of the recent technologies of the Industry 4.0 with the smart and autonomous systems in the SMEs. Maintenance is the most important activity of all the large and small-scale manufacturing industry in and around the world. The unexpected machine fault, causing machine down time and delay of maintenance actions leads to major losses in the industry. This study is to investigate the optimal decision support with smart maintenance management systems for SMEs. This study identified the most critical systems and their subsystems based on their performance. The most critical systems and their subsystems were monitored and implemented the IIoT based continuous real-time health monitoring approaches. Using artificial intelligence techniques and machine learning algorithms, the proposed methods have helped to predict the Remaining Useful Life (RUL) of those critical systems and their subsystems in the SMEs. The result of this study, maintenance personnel are scheduled and assigned to service actions at the right time automatically. Based on the optimal availability and RUL, it also identifies real-time health degradation and potential disturbances of critical subsystems.