Constructing a health indicator for roller bearings by using a stacked auto-encoder with an exponential function to eliminate concussion

Most deep-learning models, especially stacked auto-encoders (SAEs), have been used in recent years for the diagnosis of faults in rotating machinery. However, very few studies have reported on health indicator (HI) construction by using SAEs in deep learning. SAEs have a good feature-extraction ability when several hidden layers are used to reconstruct the original input. In this study, we first introduce a method to reduce dependence on prior knowledge that is based on SAEs and enables extraction of the preliminary degradation trend from the bearing's frequency domain directly. Second, to construct the final HI and improve the monotonicity of the indicators, an exponential function is used to eliminate global severe vibration after an SAE has extracted the preliminary degradation trend. To prove the effect of our presented method, some other HI construction models, such as root mean square, kurtosis, approximate entropy, permutations entropy, empirical mode decomposition-singular value decomposition, K-means/K-medoids, and various time–frequency fusion indicators are used for comparison. Moreover, to prove that the exponential-function effect exceeds other severe vibration-eliminating methods, examples of the latter methods such as exponentially weighted moving-average and outlier detection are used for comparative analysis. Finally, the results shows that our proposed model is better than the above-mentioned existing models.