A Novel Intelligent Fault Diagnosis Method for Rolling Bearings Based on Compressed Sensing and Stacked Multi-Granularity Convolution Denoising Auto-Encoder

This paper investigates the unsupervised automatic feature extraction method with a large amount of unlabeled data for the fault diagnosis of rolling bearings in automobile production line, where the fault information is hard to identify due to the low-level features of a single category and the massive fault data is difficult to process. Different from the existing methods, which only combine the compressive sensing with single category of low-level features, or extract features from raw data, a novel intelligent fault diagnosis method for rolling bearings based on the compressive sensing and a stacked multi-granularity convolution denoise auto-encoder network is proposed, which utilizes the nonlinear projection to achieve the compressed acquisition and resolves issues with character unicity by extracting a diverse category of high-level features. Moreover, a regularization method called ‘dropout’ is used to prevent overfitting during the training process. The amount of measured data that contained all the information of faults is reduced and the classification accuracy is improved by extracting more robust features based on the proposed method. Finally, the effectiveness of the proposed method is validated using data sets from rolling bearings in an automotive production line and the analysis result show that it is superior to the existing methods and is able to obtain high diagnostic accuracies.