Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification

Effective fault diagnosis has long been a research topic in the prognosis and health management of rotary machinery engineered systems due to the benefits such as safety guarantees, reliability improvements, and economical efficiency. This paper investigates an effective and reliable deep learning method known as stacked denoising autoencoder (SDA), which is shown to be suitable for certain health state identifications for signals containing ambient noise and working condition fluctuations. SDA has become a popular approach to achieve the promised advantages of deep architecture-based robust feature representations. In this paper, the SDA-based fault diagnosis method contains three successive steps: health states are first divided into training and testing groups for the SDA model, a deep hierarchical structure is then established with a transmitting rule of greedy training, layer by layer, where sparsity representation and data destruction are applied to obtain high-order characteristics with better robustness in the iteration learning. Validation data are finally employed to confirm the fault diagnosis results of the SDA, where existing health state identification methods are used for comparison. Rotating machinery datasets are employed to demonstrate the effectiveness of the proposed method.