Time series forecasting for predictive maintenance

Predictive maintenance refers to the prediction of errors and defects in hardware components before these errors occur. This can be used to obtain replacement parts early and avoid major subsequent failures.

Machine learning algorithms have a great potential for predictive maintenance, as machine learning can be used to discover structures in maintenance data and in this way predict a maintenance data time series. In doing so, imminent defects can be detected earlier by using established physical reasoning to find the defects using the data. At this point, there is potential for improvement by not only limiting a system to known rationales and also performing the defect detection itself using machine learning techniques.

The aim of this work is to investigate how machine learning based algorithms for time series prediction can be used, possibly combined with other methods, to do predictive maintenance.

Prerequisites

  • Very good knowledge about analysis, linear algebra and optimization
  • Interest and experience in machine learning
  • good programming skills (ideally Python)

Literature

  • Fernandes, M., Canito, A., Corchado, J. M., & Marreiros, G. (2019, June). Fault detection mechanism of a predictive maintenance system based on autoregressive integrated moving average models. In International Symposium on Distributed Computing and Artificial Intelligence (pp. 171-180). Springer, Cham.
  • Kanawaday, A., & Sane, A. (2017, November). Machine learning for predictive maintenance of industrial machines using IoT sensor data. In 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS) (pp. 87-90). IEEE.
  • Yang, H., Pan, Z., Tao, Q., & Qiu, J. (2018). Online learning for vector autoregressive moving-average time series prediction. Neurocomputing, 315, 9-17.