Complex and changeable working conditions are important factors affecting the accuracy and robustness of diesel engine fault diagnosis models. Working condition identification can provide a basic reference for the unit operation state, which is of great significance for fault diagnosis. At present, most working condition identification models take power as the identification parameter, divide the power parameter into several discrete intervals, and obtain the power interval of the current state through the classification model. However, describing the working condition only by power will lead to the coupling of speed and load parameters, and the working condition parameters should be continuous variables. In this paper, a continuous working condition model decoupled by speed and load parameters is proposed, and the working condition identification model is established based on a graph self-attention network (GSAN). A large number of experimental data of 32 working conditions under normal and typical fault simulations was obtained on a diesel engine experimental bench, which was used for training and testing models. Under the condition of untrained working conditions, the ๐ 2 ๐๐๐ coefficients of the proposed method are 96.70% and 97.27% for normal and typical fault experimental data respectively, demonstrating the efficiency of the proposed approach.
Bearing fault diagnosis for equipment-safe operation has a crucial role. In recent years, more achievements have been made in bearing fault diagnosis. However, for the fault diagnosis model, the representation and sensitivity of bearing fault features have a great influence on the diagnosis output results; thus, the attention mechanism is particularly important for the selection of features. However, global attention focuses on all sequences, which is computationally expensive and not ideal for fault diagnosis tasks. The local attention mechanism ignores the relationship between non-adjacent sequences. To address the respective shortcomings of global attention and local attention, an adaptive sparse attention network is proposed in this paper to filter fault-sensitive information by soft threshold filtering. In addition, the effects of different signal representation domains on fault diagnosis results are investigated to filter out signal representation forms with better performance. Finally, the proposed adaptive sparse attention network is applied to cross-working conditions diagnosis of bearings. The adaptive sparse attention mechanism focuses on the signal characteristics of different frequency bands for different fault types. The proposed network model achieves better overall performance when comparing the cross-conditions diagnosis accuracy and model convergence speed.
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