Bearing fault diagnosis method and device based on multi-domain feature fusion, and medium
The invention discloses a multi-domain feature fusion bearing fault diagnosis method and device and a medium. The method comprises the following steps: acquiring a vibration signal of a bearing; extracting a time domain feature, a frequency domain feature and a time-frequency domain feature accordin...
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| creator | ZHOU LINGMENG DENG FEIQI |
| description | The invention discloses a multi-domain feature fusion bearing fault diagnosis method and device and a medium. The method comprises the following steps: acquiring a vibration signal of a bearing; extracting a time domain feature, a frequency domain feature and a time-frequency domain feature according to the vibration signal; fusing the time domain feature, the frequency domain feature and the time-frequency domain feature by adopting a principal component analysis method to obtain a fused feature; and the fused features are preset in a convolutional neural network, and bearing state classification identification is carried out. According to the method, the time domain feature, the frequency domain feature and the time-frequency domain feature are fused through the principal component analysis method, classification and recognition are carried out through the convolutional neural network, and the fault diagnosis accuracy can be greatly improved. The method can be widely applied to the technical field of bearin |
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The method comprises the following steps: acquiring a vibration signal of a bearing; extracting a time domain feature, a frequency domain feature and a time-frequency domain feature according to the vibration signal; fusing the time domain feature, the frequency domain feature and the time-frequency domain feature by adopting a principal component analysis method to obtain a fused feature; and the fused features are preset in a convolutional neural network, and bearing state classification identification is carried out. According to the method, the time domain feature, the frequency domain feature and the time-frequency domain feature are fused through the principal component analysis method, classification and recognition are carried out through the convolutional neural network, and the fault diagnosis accuracy can be greatly improved. 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The method comprises the following steps: acquiring a vibration signal of a bearing; extracting a time domain feature, a frequency domain feature and a time-frequency domain feature according to the vibration signal; fusing the time domain feature, the frequency domain feature and the time-frequency domain feature by adopting a principal component analysis method to obtain a fused feature; and the fused features are preset in a convolutional neural network, and bearing state classification identification is carried out. According to the method, the time domain feature, the frequency domain feature and the time-frequency domain feature are fused through the principal component analysis method, classification and recognition are carried out through the convolutional neural network, and the fault diagnosis accuracy can be greatly improved. The method can be widely applied to the technical field of bearin</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING MEASURING PHYSICS TESTING TESTING STATIC OR DYNAMIC BALANCE OF MACHINES ORSTRUCTURES TESTING STRUCTURES OR APPARATUS NOT OTHERWISE PROVIDED FOR |
| title | Bearing fault diagnosis method and device based on multi-domain feature fusion, and medium |
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