An industrial analytics approach to predictive maintenance for machinery applications

A key challenge in nowadays machinery applications is to guaranty availability and to increase productivity while reducing production costs. Companies, which are able to use their produced data (e.g., process values) to overcome this challenge, will have a significant competitive advantage. This paper shows how data-analytics approaches can be used to realize predictive maintenance solutions, taking advantage of already existing data of production facilities, and reducing the realization effort. The main advantages of data-analytic approaches are explained by means of two industrial applications.

[1]  T. Warren Liao,et al.  Clustering of time series data - a survey , 2005, Pattern Recognit..

[2]  O. Niggemann,et al.  Automated generation of timing models in distributed production plants , 2013, 2013 IEEE International Conference on Industrial Technology (ICIT).

[3]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[4]  Karsten P. Ulland,et al.  Vii. References , 2022 .