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R. Sharma, Niraj
- Assessment and review of maintenance practices in the 4th industrial revolution using the cognitive analytics framework
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Authors
Affiliations
1 Research Scholar, Department of Mining Engineering, IIT(ISM), Dhanbad., IN
2 Professor, Department of Mining Engineering, IIT(ISM), Dhanbad, IN
3 Research Scholar, Department of Mining Engineering, IIT(ISM), Dhanbad., IN
1 Research Scholar, Department of Mining Engineering, IIT(ISM), Dhanbad., IN
2 Professor, Department of Mining Engineering, IIT(ISM), Dhanbad, IN
3 Research Scholar, Department of Mining Engineering, IIT(ISM), Dhanbad., IN
Source
Journal of Mines, Metals and Fuels, Vol 67, No 09 (2019), Pagination: 416-423Abstract
This paper reviews past and the prevailing maintenance concepts practiced, evolved with industrial revolutions over the centuries and briefly outlines the cognitive process, methods and framework of tools and techniques which will be used in the days to come. The maintenance practices have continuously evolved in how the equipment was earlier managed using breakdown, corrective, preventive, total productive maintenance, condition-based maintenance, failure analysis reporting, risk-based maintenance and reliability centric maintenance. The core objective of maintenance remained the same “Increase useful life of an asset with minimal costs”. The thinking now has changed from viewing maintenance as “costs” to maintenance as “investments”. In the era of Industry 4.0, the maintenance value chain - an integrated cyber-physical system plays an important role in the maintenance of the mining equipment. A cognitive/AI (Artificial Intelligence) maintenance framework can be an effective tool in optimizing the maintenance programme with minimal costs when compared to the traditional maintenance programme in the industry. The optimal replacement policy can be calculated and determined by the computer to minimize the expected downtime or maximize the expected profit. The minimum expected downtime per unit time and maximum expected profit per unit time can also be determined. This replacement policy and mathematic models can be used as reference to the failure system maintenance and replacement.The evolution from traditional data-driven algorithms to blended intelligent algorithms is helping in developing new optimization models for maintenance management systems.Keywords
Equipment; industrial revolution; breakdown; AI; industry 4.0; cognitive
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