A Robust Fault Diagnosis Method for Rolling Bearings Based on Deep Convolutional Neural Network

Fault diagnosis of rolling bearings has been an important and challenging research issue. The existing conventional algorithms for diagnosing rolling bearings rely on artificial feature extraction requiring a wealth of knowledge of signal handling, expertise and human efforts, lacking adaptability. With the capacity for learning features from original signals automatically, deep learning methods can solve the shortcomings of conventional diagnosis methods. This paper puts forward a deep learning method for rolling bearing fault diagnosis based on two-dimensional convolutional neural network, called 2dCNN-FD. The method has the capability of automatic feature extraction and robust noise tolerance. Experimental results demonstrate that comparing with other traditional diagnosis algorithms, the 2dCNN-FD model achieves high diagnosis accuracy and better robustness under different noise conditions.