Steam turbine vibration fault diagnosis method based on deep neural network and manifold alignment
The invention aims to provide a steam turbine vibration fault diagnosis method based on a deep neural network and manifold alignment, and the method comprises the steps: collecting vibration fault data through a vibration sensor, selecting features and fault types, and further carrying out the stand...
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Main Authors | , , , , |
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Format | Patent |
Language | Chinese English |
Published |
13.08.2021
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Subjects | |
Online Access | Get full text |
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Summary: | The invention aims to provide a steam turbine vibration fault diagnosis method based on a deep neural network and manifold alignment, and the method comprises the steps: collecting vibration fault data through a vibration sensor, selecting features and fault types, and further carrying out the standardization processing of original data, thereby facilitating the weighting and training; constructing a deep neural network, extracting abstract features, maintaining an original geometric structure of the data by using a manifold alignment item, predicting categories in a classification layer, obtaining a loss function, finally obtaining an overall objective function, iteratively updating a network parameter training model through a gradient descent method until the maximum number of iterations is reached, and obtaining a final network model, predicting a fault category. According to the method, weighting of different characteristic indexes is facilitated, and the learning process is accelerated. Data complex stru |
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Bibliography: | Application Number: CN202110362575 |