Construct a health indicator for bearing based on unsupervised SDAE with Euclidean distance

Abstract

Rolling bearings are critical components in machinery, directly affecting performance and lifespan. Traditional fault diagnosis methods rely on expert knowledge, whereas bearing data are often unlabeled and noisy. This paper proposes an unsupervised health state evaluation model based on Euclidean distance and Stacked denoising autoencoders (SDAE). The model directly extracts features and constructs a Health Indicator (HI) from unlabeled, noisy time-domain vibration signals of bearings throughout their entire lifecycle using relatively simple computations. First, preliminary feature extraction is performed using the SDAE. Second, the extracted features are utilized to construct the HI using Euclidean distance. Third, the proposed model is compared with four other models, experimental results demonstrate that the proposed method outperforms the comparison models in terms of smoothness and noise-handling capability. Furthermore, the superiority of the proposed approach is validated through a comprehensive evaluation using three metrics: monotonicity (Mon), correlation (Corr), and robustness (Rob). The proposed method effectively constructs the HI under unsupervised conditions while maintaining the advantages of simple modeling and efficient computation. However, the model cannot directly perform fault type diagnosis and remaining useful life prediction. Future research may consider integrating other classifiers or adopting advanced deep learning techniques such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to enhance fault classification capabilities and improve the overall diagnostic performance.