Anomaly Detection in Machinery and Smart Autonomous Maintenance in Industry 4.0 During Covid-19

Abstract

During this Covid 19 pandemic, the government introduced many restrictions on industries, such as 50% working manpower, social distance, permits only for essential industry, work from home policy, etc. Hence the industries were forced to face severe manpower reduction, which led to low production with less quality and an increase in equipment downtime. This lower productivity and inadequate machine performance with low speed and minor stoppages lead to failure in achieving the customer demand in manufacturing industries. By implementing data-centric digitalized smart Autonomous Maintenance called Jisu Hazen (JH) pillar of Total Productive Maintenance through Industry 4.0 transformation, around 70% of breakdowns due to forced deterioration, manual error, poor skill and poor decisions can be eliminated. The failure part can be identified for decreasing the Mean Time To Rectify (MTTR) and increased productivity can be achieved with less manpower by adhering to the Covid-19 protocol. Through smart JH, operators work hours together for Clean, Lubricate, Inspection and Tightening (CLIT) time, repair time, operators’ working duration due to minor stoppages and machine low speed, the number of employees related to machine operation and maintenance are reduced along with planned productivity.