As the most popular power source equipment in commercial aviation, turbofan engines face problems such as difficulties in data acquisition and unbalanced data sets. In addition, it is enormously challenging to construct a realistic remaining useful life (RUL) prediction model that can be applied because of the complex physical mechanisms involved in turbofan engine operation and the strong nonlinear properties of the data collected by multiple sensors. Moreover, most of the existing RUL prediction models lack interpretability. Therefore, in this study, a 3D Attention-enhanced hybrid neural network is proposed to solve the RUL prediction problem of turbofan engines. The model employs a convolutional network to extract local features from sensor data and uses a bi-directional long short-term memory (BiLSTM) framework to learn long-dependent nonlinear features. To enhance the learning capability of the model, this study incorporates a 3D attention module capable of balancing space and channels to visualize and analyze the action weights learned by the neural network through the attention mechanism, and the proposed attention module is found to have good robustness and generalization performance with good interpretability. In addition, handcrafted features with degradation trend coefficients and average degradation values are used as auxiliary inputs for all-round learning of the hybrid model in this study. Finally, tested on a real C-MAPSS benchmark dataset, the proposed method in this study has the most superior performance and better practical application compared with existing advanced RuL prediction methods.

To address the problem of large parameters in the ResNet model, this article proposes a rolling bearing fault diagnosis method with gram angle field (GAF) and Ghost-ResNet. For addressing the low fault diagnosis accuracy caused by time dimension information loss in image coding, the raw vibration signal is converted into the Gram difference field image. The Ghost convolution module is employed to replace the standard convolution layer in the ResNet model, and a Ghost-ResNet model is proposed for bearing fault diagnosis for the purpose of reducing the parameter sizes and floating point operations. To improve the performance of the Ghost-ResNet model, the RAdam optimization algorithm and Gramian angular difference fields image coding method is used for model training. Experiments with single fault and compound fault are performed for verifying the superiority of the proposed Ghost-ResNet model. The comparison results show that the GAF image coding method is more powerful in representing fault features than the other methods, and the RAdam optimization algorithm has achieved stable and higher diagnostic results with faster convergence speed. Compared with other diagnostic methods, the Ghost-ResNet model has higher diagnostic accuracy, stronger anti-noise performance, and better generalization ability.

The operating conditions of rolling bearings are complex and variable, and their vibration monitoring signals are filled with strong noise interference, resulting in a low accuracy in remaining useful life (RUL) prediction. For this issue, this paper proposes a denoising method with vibration fault signals modeling, and a novel RUL prediction method with Gate-convolutional neural networks (CNN) and Conv-Transformer encoder. Firstly, the theoretical fault signal is obtained through the vibration fault signal model, and the quality of the extracted features is improved by the wavelet threshold denoising algorithm in the process of feature extraction and selection. Moreover, the CNN is combined with the gating mechanism to construct a feature extractor with the feature evaluation function, and the convolution layers are introduced into the transformer to expand the encoder’s ability to explore local information in temporal data. By using fixed-time step temporal features as the input to the prediction module and minimizing the Huber function as the optimization objective, the relationship between temporal features and RUL is obtained. The comparison with the existing state-of-the-art RUL methods illustrates that the combination of gate control and convolutional structure proposed in this paper can not only reduce the prediction error of the model but also improve its generalization ability and robustness.