Equipment State Assessment Based on Convolutional Neural Network
| Autor: | Yin Yuanwei, Chen Yanglong, Ma Yanheng |
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| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | Journal of Physics: Conference Series. 1237:042037 |
| ISSN: | 1742-6596 1742-6588 |
| DOI: | 10.1088/1742-6596/1237/4/042037 |
| Popis: | Aiming at the problem of the defect of less-information and poor reliability of assessment results when assessing equipment state, this paper proposes a method of equipment status assessment based on convolution neural network by utilizing the ability of equipment under cognitive testability design to easily obtain multi-source information. The assessment system is divided into feature layer and information layer. In the feature layer assessment, the analytic hierarchy process is used to determine the constant weight, then the variable weight theory is used to realize the real-time adjustment of the weight. And the convolutional neural network model is used in the assessment of the information layer. Finally, through the example verification and comparison with BP algorithm, it can be seen that this assessment method has fast recognition speed, high accuracy, more reliable assessment results, and has certain versatility and application value in engineering. |
| Databáze: | OpenAIRE |
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