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Fault diagnosis and classification for bearing based on EMD-ICA

A method of bearing fault diagnosis and classification based on Empirical Mode Decomposition (EMD) and Independent Component Analysis (ICA) is presented. There are aliasing phenomenon and correlation among intrinsic mode functions decomposed by EMD. Through elimination of information redundancy, an estimated intrinsic mode functions that includes much information of fault is separated for fault diagnosis and classification by the method of ICA, in fault diagnosis, multi-signals are composed of one engineering signal sampled by single sensor by using time-delay technique, and a fault sub-signal for fault diagnosis is separated from multi-signals by the method of EMD-ICA. In fault classification, based on fault sub-signal estimated by EMD-ICA, a parameter vector is composed of the coefficients of frequency, the residual of multi-information, correlation coefficients, entropy of frequency, approximate entropy and kurtosis, and this parameter vector is regarded as input of general regression neural network for judging bearing's three fault type.