An enhancement denoising autoencoder for rolling bearing fault diagnosis

Denoising autoencoders can automatically learn in-depth features from complex data and extract concise expressions, which are used in fault diagnosis. However, they still have many drawbacks: (1) unsatisfactory results when the input data is not substantial; (2) difficulty in optimising the hyperparameter; (3) inability of existing regularisation methods to combine the advantages of L1 and L2 regularisation. To overcome the aforementioned challenges, here, a new data preprocessing method was proposed to obtain the training data. By reusing the data points between the adjacent samples, the fault identifying rate was significantly improved. Considering the different resilience of each layer after regularisation, the proposed method could alter the hyperparameter by changing the unit numbers of each layer. For a better sparse representation, the norm penalty combined L1 and L2 norm penalties, motivated by the elastic net. Comparison with a normal denoising autoencoder verified the superiority of the proposed method.