Multi-fault diagnosis of rotating machinery based on deep convolution neural network and support vector machine

Because multi-fault vibration signals in rotating machinery are often more complicated than single faults, human-designed fault feature sets are not yet able to respond adequately to the complex task of fault diagnosis. To address this, a fault diagnosis method, based on deep convolution neural network (DCNN) and support vector machine (SVM), is proposed. The DCNN, a data-driven deep-learning model, is applied to extract the fault features automatically. The fault-feature information can be extracted adaptively according to the minute differences in local fault signals. First, the envelope spectrum obtained by Hilbert transform was fed into the DCNN to extract the fault features, and then combined with SVM (the classifier) to diagnose multi faults of bearings and rotors. Then, 16 frequency-domain features were extracted by the DCNN, and the average accuracy was found to be 90.29%, which showed that machine wisdom alone was insufficient for this task. Therefore, two additional time-domain features extracted by human experts were adopted together with the machine-designed features to create the method that we call the semi-DCNN method. The theoretical mechanism of the proposed method was clear: it utilized both machine wisdom and human experience in designing the fault features. The effectiveness of the new method was proved by the fact that average diagnostic accuracy increased to 98.71% during diagnosis of composite faults in N205 bearing and rotor. (C) 2020 Elsevier Ltd. All rights reserved.