Application of rough set neural network in fault diagnosing of test-launching control system of missiles

Yue-gang, Wang; Liu, Bin; Zhibin, Guo; Yongyuan, Qin

doi:10.1109/wcica.2004.1340981

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…For the purpose of automatically achieving the task of diagnosis faults, a variety of data-driven methods have been used to do diagnosis analysis. Data-driven methods such as Neural Network [1], Rough Set [2], Expert System [3], Principal Component Analysis [4], Support Vector Machines [5], have some advantages over model-based methods under the condition of complex systems. These models do not require much domain knowledge about the faults and can be directly constructed using observed data.…”

Section: Introductionmentioning

confidence: 99%

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

Zhou

Wang

Zhang

2014

AMM

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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.

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