Intelligent fault diagnosis of bearings based on feature model and Alexnet neural network

Abstract

Bearings are necessary rotating machinery and plays an important role in the modern industrial systems for its safety and reliability. Timely fault diagnosis of bearings can reduce the probability of failure, thereby reducing economic losses and casualties. Many signal processing methods for fault feature extraction and fault recognition have been applied by scholars and engineers. Although numerous current methods identify and diagnose bearing faults correctly, they rely on a lot of existing information and experts experience, so it is not possible to establish a one-to-one correspondence between the original signal and the failure mode. Furthermore, the structure and parameters of the artificial intelligent neural network need to be optimized through experts and current knowledge. Alexnet neural network improves the learning ability and provides inspiration and direction for the above problem. The ensemble empirical mode decomposition (EEMD) solve the problem of mode mixing. The wavelet transform could impose the time and frequency features. Combing the prior of EEMD with continue wavelet transform, an adaptive fault feature model has been constructed that can directly provide the information to corresponding with the fault classified neural network. In this approach, fault signals are enhanced by extracting envelope decomposition and frequency signals. Numerous bearing data which containing different fault signals are used to verify the effectiveness and accuracy of the proposed method. The diagnosis results show that the novel alexnet neural network classifies bearings fault with high accuracy and robustness under complexity environment.