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.

[1]  Pushe Zhao,et al.  Advanced correlation-based anomaly detection method for predictive maintenance , 2017, 2017 IEEE International Conference on Prognostics and Health Management (ICPHM).

[2]  Lei Xu,et al.  ADE: An ensemble approach for early Anomaly Detection , 2017, 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM).

[3]  Kjell G. Robbersmyr,et al.  Early detection and classification of bearing faults using support vector machine algorithm , 2017, 2017 IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD).

[4]  Enrico Zio,et al.  A comparison between extreme learning machine and artificial neural network for remaining useful life prediction , 2016, 2016 Prognostics and System Health Management Conference (PHM-Chengdu).

[5]  Zhen Gao,et al.  Macro-level accident fatality prediction using a combined model based on ARIMA and multivariable linear regression , 2016, 2016 International Conference on Progress in Informatics and Computing (PIC).

[6]  Marcos V. O. de Assis,et al.  Anomaly Detection Using Forecasting Methods ARIMA and HWDS , 2013, 2013 32nd International Conference of the Chilean Computer Science Society (SCCC).

[7]  Junya Shimada,et al.  A statistical approach to reduce failure facilities based on predictive maintenance , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[8]  Yash Gupta,et al.  Classification of Abstract Images using Machine Learning , 2017, ICDLT '17.

[9]  Radu Stefan Niculescu,et al.  Predictive maintenance applications for machine learning , 2017, 2017 Annual Reliability and Maintainability Symposium (RAMS).