Machine learning for predictive maintenance of industrial machines using IoT sensor data

The industrial Internet of Things (IIoT) is the use of Internet of Things (IoT) technologies in manufacturing which harnesses the machine data generated by various sensors and applies various analytics on it to gain useful information. The data captured by the machines is usually accompanied by a date time component which proves vital for predictive modelling. This paper explores the use of AutoRegressive Integrated Moving Average (ARIMA) forecasting on the time series data collected from various sensors from a Slitting Machine, to predict the possible failures and quality defects, thus improving the overall manufacturing process. The use of Machine Learning thus proves a vital component in IIoT having use cases in quality management and quality control, lowering the cost of maintenance and improving the overall manufacturing process.