Abstract
Industry 4.0 enables technological trends like Big Data Analytics and Machine Learning techniques to converge into and merge with traditional manufacturing processes, resulting in smart manufacturing. Smart manufacturing techniques leverage the use of Industrial Internet of things (IIoT) technology using IoT sensors that are fitted on physical assets to enhance manufacturing processes. IoT Sensors enable smart manufacturing facilities capable of autonomously exchanging information, which can be used to drive business decisions more accurately. Businesses that adopt Smart manufacturing techniques lead to a competitive advantage for these firms as they can bring in higher profit margins, reduced maintenance costs, energy savings, and better-quality products. This study proposes an architecture for IIoT based predictive maintenance. A case study from ancillary automobile industry is presented to demonstrate a predictive model for predicting sudden breakdown in industrial machines, thereby enabling the production and maintenance cycle to be ‘smart.’
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