Rough Sets Algorithm and Its Application in Fault Diagnosis

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