Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Bearing fault diagnosis method based on SAVMD and CNN
Non-stationary and nonlinear vibrating signals in rotating mechanical equipment are always disturbed by noises from working environment and other devices. Bearing fault signals in mechanical systems are reduced which makes it difficult to extract fault characteristic, diagnose fault type. Selection of global optimal solution was solved by studying the parameter [K,α] about variational mode decomposition (VMD) and simulating physical system annealing， that was simulated annealing variational mode decomposition (SAVMD) extract characteristic value. The intrinsic mode function (IMF) components were reconstructed by weighted kurtosis evaluation index, put them into convolutional neural network (CNN) model for fault classification. Finally, the proposed method was verified by numerical simulations with the open bearing data of Case Western Reserve University. The accuracy rate is 99.28%. After introducing -6 dB white noise to simulate the noisy environment, the accuracy rate is 93.6%. These results show that this method can be used to achieve bearing fault diagnosis in complex working environment.
Bearing fault diagnosis method based on SAVMD and CNN
Non-stationary and nonlinear vibrating signals in rotating mechanical equipment are always disturbed by noises from working environment and other devices. Bearing fault signals in mechanical systems are reduced which makes it difficult to extract fault characteristic, diagnose fault type. Selection of global optimal solution was solved by studying the parameter [K,α] about variational mode decomposition (VMD) and simulating physical system annealing， that was simulated annealing variational mode decomposition (SAVMD) extract characteristic value. The intrinsic mode function (IMF) components were reconstructed by weighted kurtosis evaluation index, put them into convolutional neural network (CNN) model for fault classification. Finally, the proposed method was verified by numerical simulations with the open bearing data of Case Western Reserve University. The accuracy rate is 99.28%. After introducing -6 dB white noise to simulate the noisy environment, the accuracy rate is 93.6%. These results show that this method can be used to achieve bearing fault diagnosis in complex working environment.
Bearing fault diagnosis method based on SAVMD and CNN
SONG ChunSheng (Autor:in) / LIANG YaRu (Autor:in) / LU NiFang (Autor:in) / DU Gang (Autor:in) / JIA Bo (Autor:in)
2024
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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Research on unsupervised domain adaptive bearing fault diagnosis method
| DOAJ | 2024