Study of data mining based machinery fault diagnosis

Jiang, Dong; Huang, Shi-Tao; Lei, Wenping; Shi, Junye

doi:10.1109/icmlc.2002.1176814

Cited by 4 publications

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is widely used in various fields, such as market analysis, decision making, fault detection and so on. Bayesian network learning based on data mining can be used to detect fault in rotating machine [6]. Bayesian network can present a wealth of information about the fault diagnosis, however discovering and constructing complete or local graphical structures based on Bayesian learning require much high computational cost.…”

Section: Introductionmentioning

confidence: 99%

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

Zhou

Wang

Zhang

2014

AMM

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%