Welcome to the homepage of the 2nd ACM SIGKDD Workshop on Machine Learning for Prognostics and Health Management (ML for PHM 2017).

Prognostics and Health Management (PHM) is the study of system behaviors to detect anomalies, determine root causes and when possible predict future system behavior. Common applications include large mechanical systems such as aircraft, electronics and increasingly complex cyber-physical systems with humans in the loop. The dramatic increase of sensors, data rates and communication capabilities continue to drive the complexity of PHM applications to new levels.

At KDD, data mining for PHM systems has been a long-standing application area. The application of deep learning methods to these large multi-model sensor data sets can be viewed as a disruptive extension of this traditional domain for the KDD community. Following up on our successful inaugural workshop at KDD 2016, we would like to again bring together academic and industrial researchers in the fields of data mining, machine learning, systems engineering, mechanical engineering, and the broader prognostics communities, in the collaborative effort of identifying and discussing major technical challenges and recent results related to machine learning-based approaches in PHM. It will feature an invited talk, presentation of accepted papers, and a panel discussion.