Constructing a health indicator for bearing degradation assessment via an unsupervised and enhanced stacked autoencoder

Abstract

To eliminate the noise from the extracted health indicator (HI) and reduce dependence on manual experience including knowledge for selecting the appropriate time–frequency domain indicators and conversions for data pre-processing, multiple-step processing, and multiple model combinations. An enhanced stacked autoencoder (ESAE) based on an exponent weight moving average (EWMA) in a deep learning model is proposed. This ESAE uses the vibration amplitude of an unlabeled original time-domain signal to construct the HI directly. To demonstrate our proposed method is better than other models, including the stacked autoencoder (SAE), stacked denoising autoencoder (SDAE), root mean square (RMS), kurtosis, K-medoids clustering, and self-organizing map (SOM) neural network models. The proposed model is simulated for multiple bearings in two case studies. The experiment result shows that the extracted HI curve is smoother than mentioned-above other models reduce the wrong judgment of bearing health caused by noise effectively. Moreover, high monotonicity is lay a good foundation for following reaming useful life prediction. An index, named Mon, is used to assess the monotonicity performance of all models, the experiment result also shows that the extracted HI by our proposed is superior to other models.