Diagnosis Rule Discovery Based on Causality in the Context of Fault Diagnosis in Rotating Machinery

Abstract:

The rotating machineries in a factory usually have the characteristics of complex structure and highly automated logic, which generated a large amounts of monitoring data. It is an infeasible task for uses to deal with the massive data and locate fault timely. In this paper, we explore the causality between symptom and fault in the context of fault diagnosis in rotating machinery. We introduce data mining into fault diagnosis and provide a formal definition of causal diagnosis rule based on statistic test. A general framework for diagnosis rule discovery based on causality is provided and a simple implementation is explored with the purpose of providing some enlightenment to the application of causality discovery in fault diagnosis of rotating machinery.

• Periodical: Applied Mechanics and Materials (Volume 532)
• Pages: 113-117
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.532.113
• Online since: February 2014
• Authors: Zhou Jin*, Ru Jing Wang, Jie Zhang
• Keywords: Causality, Data Mining (DM), Fault Diagnosis, Rotating Machinery

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