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Huiling Liu
North University of China

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Rough Sets Algorithm and Its Application in Fault Diagnosis Huiling Liu; Hongxia Pan; Aiyu Wang
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 9: September 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

Gearbox is one of the most complicate rotary mechanical apparatus, the fault signal shows non-linear and non-stationary, and how to recognize the faults effectively is a key issue. A novel method based on wavelet packet transform and rough sets theory was presented for fault diagnosis of gearbox. First, the vibration signals were decomposed into eight bands from low frequency to high frequency by wavelet packet transform, energy characteristics were extracted as the condition attributes. Second, an improved NaiveScaler algorithm was put forward to discrete continuous attributes in the case of assuring classification ability. A new reduction algorithm based on condition equivalence classifications was proposed to delete the redundant features, which could improve the reduction efficiency. Lastly the decision rules were drawn and utilized to test the samples. The results show that the method could obtain more sensitive fault characteristic parameters and have better classification ability accordingly. DOI: http://dx.doi.org/10.11591/telkomnika.v11i9.3308 
Study on Fault Feature Extraction of High-Speed Automaton Aiyu Wang; Hongxia Pan; Huiling Liu
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 10: October 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

In order to effectively extract the weak fault feature of high-speed automaton (HSA) in the environment with strong noises, a method of fault feature extraction was proposed based on wavelet packet energy entropy and fuzzy clustering algorithm. In this paper, wavelet packet was utilized to denoise the vibration signals of three working conditions of automaton, to decompose the signals and then to obtain eight frequency band energy entropies of each signal. Through processing and analyzing the features, the results show that there are obvious differences between three conditions, and the fuzzy clustering algorithm can identify the fault pattern of HSA accurately. The feature proposed by this extraction approach is proved to be able to effectively reflect the working state of the automaton, therefore the wavelet packet energy entropy could be considered as the feature parameters of HAS for fault identification and diagnosis. The fault feature extraction method can also provide a certain engineering application value for real-time monitoring and early fault diagnosis of this type HSA. DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.3417 
Co-Authors Aiyu Wang Hongxia Pan