Tutorial: Predictive Maintenance 



Predictive maintenance strives to anticipate equipment failures to allow for advance scheduling of corrective maintenance, thereby preventing unexpected equipment downtime and improving service quality for the customers. There is a tremendous interest in industry to leverage recent advances in machine learning and data mining to tackle this problem. Whereas the key enabling techniques (such as failure diagnostics and prediction) for predictive maintenance have been of considerable emphasis in the community, the design of practical predictive maintenance systems has not enjoyed the same attention. This is partially due to the lack of access to real-world use cases being an obstacle for researchers to consider the unique characteristics of data and the nature of the problem for the practical design. 


In this tutorial, we aim to fill the gap between the real-world needs and technology offerings by a detailed study on the nature and requirements of the real-world predictive maintenance problems as well as a comprehensive survey of the techniques tacking the problems. We will survey the underlying data sources and feature engineering techniques, the learning scenarios and model creation and selection techniques, and will also present several real-world case studies and lessons learned.


Tutorial Speaker: Zhuang (John) Wang




IEEE BigData 2015