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.

KEYWORDS:

• Anamoly detection
• Industry 4.0
• Predictive maintenance
• Zero downtime
• MTTR
• Smart JH

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

T. Roosefert Mohan

T Roosefert Mohan received his BE degree in electronics and communication from Government College of Engineering, Tirunelveli, Madurai Kamaraj University, Tamil Nadu, India, in 1990 and received his ME (power electronics and drives) degree from St. Peter's University, India. He is currently a PhD research scholar at SRM Institute of Science and Technology, India. His area of research is predictive maintenance, zero down time, Industry 4.0, total productive maintenance, and IIoT. Email: [email protected]

J. Preetha Roselyn

J Preetha Roselyn received her BE (electrical and electronics engineering) from Madras University, India, in 2022, received her MS in power system by research from Anna University, Chennai, and received a PhD from SRM University, India. She is currently working as a professor in the Department of Electrical and Electronics Engineering at SRM Institute of Science and Technology, India. She has published 41 international publications, including 16 SCI-indexed journals, 2 lecture notes, and 25 conference publications and a book, and she has received many funded projects. She is appointed as Faculty Mentor of Marine Technology Society, USA, which received the best student chapter award for 2019. Her areas of interest are power system stability, intelligent controllers for microgrids, soft computing, and power system security.

R. Annie Uthra

R Annie Uthra received BE degree in computer science and engineering from Manonmaniam Sundaranar University, India. She obtained her MS degree and PhD from SRM University, Chennai, India. She is serving as a professor and head of the Department of Computational Intelligence at SRM Institute of Technology. Additionally, she was adjunct associate teaching professor at the Institute for Software Research in the School of Computer Science at Carnegie Mellon University, Pittsburgh, USA, from 2102 till 2018. She served as visiting professor at Henan University of Economics and Law, Zhengzhou, Henan Province, China, from 2018–2020. Awards received by her include IET CLN Women Engineer 2016–2017, outstanding reviewer Award from JNCA (Elsevier), Best teacher award 2006. Her scholarly and teaching interests include wireless sensor networks, machine learning,positioning and navigation, IoT, and energy aware routing techniques. Email: [email protected]