An Analytical Approach to Industry 4.0 for Predictive and Preventive Maintenance

The world is moving towards the (Industrial Internet of Things) IIoT as each industry is trying their best to meet their demand and supply across the globe. When it comes to the production process for any industry, they face losses when there is an unexpected machine failure or scheduled maintenance. There is a need for predictive and preventive maintenance mechanisms and algorithm that can predict the possible failures and thereby reduce down time, increase profit, reduce human effort, improve production throughput, and reduce productivity losses. This can be achieved by predictive algorithms that can analyze patterns in the time series data and by performing ARIMA model and Markov Chain model. Thereby we can figure out the downfalls, identify them, and fix them for the benefit of the industry and business. By this algorithm, we can expect reduced maintenance time, new revenue streams, increased efficiency, improved customer satisfaction, and competitive advantage. We are in the middle of transformation regarding the way we produce products. This transition is called Industry 4.0 which, is used to represent the fourth revolution that has occurred in manufacturing. From the first industrial revolution (mechanization through water and steam power) to mass production, the second industrial revolution which involves assembly lines using electricity, the fourth industrial revolution will take the advantages of third revolution with the adoption of computers and automation and enhance it with smart and autonomous systems fueled by data and machine learning. Industry 4.0 is a network of machines which are digitally connected with one another to share information. In this paper, the authors discuss how time series forecasting helps us to figure out the failures and how to do proper maintenance of the industry processes.