A Novel Fault Diagnosis Method Under Dynamic Working Conditions Based on a CNN With an Adaptive Learning Rate

The fault diagnosis problem of prognostics and health management (PHM) is becoming more complicated and challenging due to the increasing demand for timeliness and adaptability under dynamic working conditions. This paper studies a new adaptive fault diagnosis (AdaFD) scheme and proposes a deep learning-based model and algorithm to enhance both the timeliness of fault diagnosis and its adaptability to dynamic conditions. The primary AdaFD model is based on a convolutional neural network (CNN). The two key modules of AdaFD are (a) an adaptive and independent learning rate design for model training and (b) an adaptive structure optimization approach for model application. During the training phase, we design an adaptive learning rate strategy by distinguishing the weight and bias parameters in the CNN model to accelerate the training process. In the application phase, the adaptive batch normalization (AdaBN) algorithm is adopted to improve the adaptability of the primary CNN model to dynamic working conditions and data noise. Finally, a case study is conducted to validate the superiority of AdaFD based on a benchmark rolling bearing dataset. The results show that the proposed fault diagnosis method has higher accuracy and better adaptability than other common methods and possesses advantages in terms of training efficiency.