Affiliation:
1. Department of information and Computing Science Guangdong Ocean University Zhanjiang Guangdong 524088 China
2. Educational Technology and Information Center Guangdong Medical University Zhanjiang Guangdong 524023 China
3. CNOOC China Limited ZhanJiang Branch Zhanjiang Guangdong 524057 China
4. Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang) Zhanjiang Guangdong 524088 China
Abstract
A convolutional neural network support vector machine (CNN–SVM) method based on multichannel feature fusion is used for progressive fault diagnosis of offshore oil and gas wells. The excellent classification performance of CNN is attributed to its ability to extract feature representations from large amounts of easily distinguishable data. However, the capability of CNN is severely constrained by the noisy and small sample amount of the electric submersible pump fault data to be studied in this article. First, 12 representative statistical features are extracted from the raw data to reduce the noise. Then, the feature mapping model is designed based on CNN migration learning. Finally, SVM is used instead of softmax function to adopt representative features directly from the mapping model for fault classification. Comparative experimental results show that the accuracy of fault diagnosis using feature‐extracted samples is better than using the raw samples directly. The proposed CNN–SVM approach has the best classification results compared to SVM, BPNN, CNN, BPNN–SVM, CNN–Attention, CNN–LSTM, and CNN–LSTM–Attention, which implies that manual feature extraction is still an indispensable tool in the fault diagnosis process.
Funder
Natural Science Foundation of Guangdong Province
Cited by
1 articles.
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