Research: Machine Learning for Predictive Maintenance of Industrial Machines using IoT Sensor Data

 Abstract 

"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."

Having spoken to a mining technical expert, I discovered that the drill bit sits on a PLC system. This means the model I plan to build will have to focus on performing inference and providing the current PLC system with the appropriate signals and or proposing a new to send the drill compensation values.

the paper focuses on preventative and proactive maintenance using ARIMA. ARIMA was widely discussed and I will be using it as a start point of the Machine Learning approach once I have received the view of all the tables with the parameters I am interested in.