A novel intelligent diagnosis method of rolling bearing and rotor composite faults based on vibration signal-to-image mapping and CNN-SVM

Abstract The rolling bearing is a key element of rotating machine and its fault diagnosis is a research focus. When a single fault of a rolling bearing fails to be addressed in time, it will cause progressive composite faults between the bearing and other elements. In this paper, the different composite fault cases of bearing and rotor are considered. First, an information fusion-empirical mode decomposition-angle adaptive distribution of polar coordinates image method is proposed, which has an adaptive image expression ability for the tested vibration signal, and can provide high-quality vibration image samples for diagnosis model training. Second, an intelligent diagnosis model combining a convolutional neural network and a support vector machine is proposed, which has an excellent generalization ability to recognize the different composite faults. Third, the different composite faults between rolling bearing and rotor are fabricated, tested and then diagnosed. The results show the test accuracy of the proposed method is higher than the conventional method and simple in the image mapping, which proves that this work is effective for the composite fault diagnosis of a rolling bearing and rotor.