A Differential Enhanced ConvNet for Rotating Machinery Diagnosis Under Strong Noise

Deep learning, with its outstanding ability of feature extraction and nonlinear fitting, has been widely used in equipment intelligent diagnosis. However, equipment fault diagnosis in strong noise environment has always been a difficult problem. As known, the equipment degradation states evolved from the health states, which will lead to different characteristic changes for the sensed signals compared to the reference health signals. Considering that both health signals and fault signals contain environmental noise, this article proposes a deep reference learning (DRL) method, which aims to enhance the sensitive differential information via multiple referential net units (RNUs). First, based on convolutional neural network (ConvNet), a series of two-class units is prebuilt as the RNU of the desired class, where each two-class unit contains the signals of a desired class and one of the reference health classes. Then, once these RNUs were pretrained, those sensed RNUs are later synthesized together for referential learning. Finally, combined with an output ConvNet, a differential enhanced ConvNet (DECN) can be built, and the architecture parameters are further superimposed and fused to enhance the characteristics of the fault signals. In the process of deep referential learning, the RNU will enhance the differential features of fault signals and contribute to the effect of fault class identification in the final ConvNet. The results and comparisons with other five deep learning models illustrate its outstanding effect and robustness where the accuracy of DECN is higher than the one of CNNs by more than 5% even if the working condition is variational.