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• W4386320390 abstract "Rotating machinery is widely applied in various industries, and its health indicator (HI) construction is significant in the data-driven status assessment and remaining useful life (RUL) prediction. However, most existing HI construction methods adopt manual features and simple fusion models, which are hard to detect early fault points and quantify degradation trends due to insufficient feature completeness and poor nonlinear characterization. To overcome the mentioned issues, this paper proposes a novel integrated HI automatic construction method by coupling multi-mode samples of vibration signal. To construct the unsupervised HI automatically, a deep spatio-temporal fusion autoencoder network (MSCLACAE) is developed by integrating multi-scale convolution (MSCNN), convolutional long short term memory network (ConvLSTM), and attention mechanism (AM). On this basis, a quadratic function-based shape constraint is introduced to improve the performance of HI constructed by MSCLACAE network. The effectiveness of the proposed method is verified by the standard bearing dataset from Xi’an Jiaotong University, the average comprehensive score under different bearings is 0.7327, which is 0.1835 higher than other methods on average. Moreover, the proposed method is also tested by the reducer platform, and the comprehensive score is 0.9144, which is increased by 0.2712 averagely compared with different methods. Furthermore, the experimental results verify that MSCLACAE not only can find early degradation points or state degradation points earlier, but also can predict the RUL with lower error." @default.
• W4386320390 created "2023-09-01" @default.
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• W4386320390 date "2023-10-15" @default.
• W4386320390 modified "2023-10-18" @default.
• W4386320390 title "A Spatio-temporal Fusion Autoencoder-based Health Indicator Automatic Construction Method for Rotating Machinery Considering Vibration Signal Expression" @default.
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• W4386320390 doi "https://doi.org/10.1109/jsen.2023.3309013" @default.
• W4386320390 hasPublicationYear "2023" @default.
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