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The non-linear and non-stationary vibration data generated by rotating machines can be used to analyze various fault conditions for predicting the remaining useful life(RUL). It offers great help to make prognostic and health management(PHM) develop. However, the complexity of the mechanical working environment makes the vibration data collected easily affected, so it is hard to form an appropriate health index(HI) to predict the RUL. In this paper, a PSR-former model is proposed including a Phase space reconstruction(PSR) layer and a Transformer layer. The PSR layer is utilized as an embedding to deepen the understanding of vibration data after feature fusion. In the Transformer layer, an attention mechanism is adopted to give different assignments, and a layer-hopping connection is used to accelerate the convergence and make the structure more stable. The effectiveness of the proposed method is validated through the Intelligent Maintenance Systems (IMS) bearing dataset. Through analysis, the prediction accuracy is judged by the parameter RMSE which is 1.0311. Some state-of-art methods such as LSTM, GRU, and CNN were also analyzed on the same dataset to compare. The result indicates that the proposed method can effectively establish a precise model for RUL predictions.

As an emerging object in aerospace actuators, electro-hydrostatic actuator (EHA) has the advantages of heavy load capacity and high reliability. An EHA fault diagnosis method based on a few-shot data augmentation technique is proposed to diagnose and isolate possible faults. The sensitive parameters of typical failure modes are demonstrated based on the mathematical model of EHA. By converting multi-dimensional experimental data into two-dimensional grayscale data and extracting local features, the time series characteristics and correlation between different signals can be highlighted. The Wasserstein Deep Convolutional Generative Adversarial Network (WDCGAN) is used to enhance the EHA small sample data. The diagnostic model WDCGAN-SDAE combined with WDCGAN and Stacked Denoised Auto Encoder (SDAE) is proposed to differentiate between multiple types of EHA failures. Compared with the five commonly used fault classification methods, the proposed method can effectively identify the typical fault modes of EHA, with the highest accuracy of fault classification and strong feature extraction ability.