Fault Diagnosis of Reciprocating Compressor Based on Convolutional Neural Networks with Multisource Raw Vibration Signals
Joint Authors
Liu, Shulin
Zhang, Hongli
Yang, Hong-bai
Zhang, Jiang-an
Chen, Lei-lei
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-07-04
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
Reciprocating compressors are widely used in petroleum industry.
Due to containing complex nonlinear signal, it is difficult to extract the fault features from its vibration signals.
This paper proposes a new method named Convolutional Neural Network based on Multisource Raw vibration signals (MSRCNN).
The proposed method uses multisource raw vibration signals collected by several sensors as input and uses the designed CNN to operate both the feature extraction and classification.
The gas valve signals of reciprocating compressor in different states are used as the experimental data.
In order to test the effectiveness of the proposed method, it is compared with the traditional BP (Back-Propagation) neural network fault diagnosis method based on power spectrum energy and wavelet packet energy.
In order to further test the antinoise performance of the proposed method, some noisy signals with different signal-to-noise ratios were constructed by adding white noise into sampled signals for testing.
The results show that the MSRCNN model has higher fault recognition rate than the traditional methods.
This indicates that the MSRCNN method not only has good fault recognition effect, but also has certain antinoise performance.
American Psychological Association (APA)
Yang, Hong-bai& Zhang, Jiang-an& Chen, Lei-lei& Zhang, Hongli& Liu, Shulin. 2019. Fault Diagnosis of Reciprocating Compressor Based on Convolutional Neural Networks with Multisource Raw Vibration Signals. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-7.
https://search.emarefa.net/detail/BIM-1196741
Modern Language Association (MLA)
Yang, Hong-bai…[et al.]. Fault Diagnosis of Reciprocating Compressor Based on Convolutional Neural Networks with Multisource Raw Vibration Signals. Mathematical Problems in Engineering No. 2019 (2019), pp.1-7.
https://search.emarefa.net/detail/BIM-1196741
American Medical Association (AMA)
Yang, Hong-bai& Zhang, Jiang-an& Chen, Lei-lei& Zhang, Hongli& Liu, Shulin. Fault Diagnosis of Reciprocating Compressor Based on Convolutional Neural Networks with Multisource Raw Vibration Signals. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-7.
https://search.emarefa.net/detail/BIM-1196741
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1196741