A New Snapshot Ensemble Convolutional Neural Network for Fault Diagnosis

Deep learning (DL) has been widely applied in fault diagnosis field. The ensemble of DL can significantly improve the generalization ability of DL. Snapshot ensemble learning, which uses the cyclic learning rate scheduler (CLR) and then combines the local minima in once training to form an ensemble method, has shown powerful performance. However, the learning rate range of CLR should be pre-defined by experiences, which may limit its prediction accuracy. In this paper, a new snapshot ensemble convolutional neural network (SECNN) is proposed, which can find the proper range of learning rate for SECNN automatically when facing a new dataset. First, a max-min cosine cyclic learning rate scheduler (MMCCLR) is designed to avoid learning rate range being affected by other parameters. Then, a new learning rate testing (logLR Test) is applied to estimate the proper learning rate range for MMCCLR. Finally, the SECNN with MMCCLR is developed. The SECNN is tested on three famous datasets, including bearing dataset of Case Western Reserve University, self-priming centrifugal pump dataset, and bearing dataset provided by the Machinery Failure Prevention Technology. The results have been improved a lot by the proposed SECNN methods. The SECNN has also achieved the start-of-the-art prediction accuracies by comparing with other DL and traditional methods.