1. 深度信念网络在管道故障诊断中的应用.
- Author
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王新颖, 张惠然, 黄旭安, 张瑞程, 赵 斌, and 张 颖
- Subjects
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FAULT diagnosis , *ACOUSTIC emission , *NONDESTRUCTIVE testing , *FEATURE extraction , *DIAGNOSIS methods , *NATURAL gas pipelines - Abstract
In order to reduce the instability of manual extraction and screening features in pipeline fault diagnosis, a fault diagnosis method based on deep confidence network to reconstruct feature parameters and model is proposed. Under laboratory conditions, the acoustic emission signals of the pipeline under normal and different fault conditions are collected, the characteristic parameters are extracted, the characteristic parameters are reconstructed by the deep confidence network and the classification model is established, and the number of model nodes is adjusted according to the characteristics of the collected sample data, and the parameter optimization model is obtained after the final diagnosis. The studies have shown that: under the same conditions, using the classification model established after the reconstruction of the depth of belief networks characteristic parameters have better stability and higher accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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