Bearing fault diagnosis using CWT , BGA and Artificial Bee Colony Algorithm

Health diagnosis of bearing is essential reduce the breakdowns of rotating machinery. An intelligent method to diagnose the bearing fault using vibration signal is proposed. This paper proposes a binary genetic algorithm (BGA) in feature selection process and discuss about the role of fitness functions in feature selection process by application of different fitness functions in GA process. A vibration signal from various conditions of bearing is extracted from a test rig and statistical features extracted using wavelet coefficients by continuous wavelet transform (CWT). A new heuristic classifier artificial bee colony (ABC) algorithm is applied and fault diagnosis results are compared with learning vector quantization (LVQ) classifier and their relative efficiency were compared based on their classification accuracy. To select the predominant features a famous feature selection approach a binary genetic algorithm (BGA) were used. Index Term-Test rig, Fault diagnosis, Continuous wavelet decomposition, Statistical features, binary genetic algorithm (BGA), Artificial bee colony (ABC) algorithm and Learning vector quantization (LVQ),