1. A hybrid deep learning model towards fault diagnosis of drilling pump.
- Author
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Guo, Junyu, Yang, Yulai, Li, He, Wang, Jiang, Tang, Aimin, Shan, Daiwei, and Huang, Bangkui
- Subjects
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FAULT diagnosis , *OPTIMIZATION algorithms , *DEEP learning , *GAS industry , *NOISE control , *FEATURE extraction - Abstract
This paper proposes a novel method namely WaveletKernelNet-Convolutional Block Attention Module-BiLSTM for intelligent fault diagnosis of drilling pumps. Initially, the random forest method is applied to determine the target signals that can reflect the fault characteristics of drilling pumps. Accordingly, the WaveletKernelNet-Convolutional Block Attention Module Net is constructed for noise reduction and fault feature extraction based on signals. The Convolutional Block Attention Module embedded in WaveletKernelNet-CBAM adjusts the weight and enhances the feature representation of channel and spatial dimension. Finally, the Bidirectional Long-Short Term Memory concept is introduced to enhance the ability of the model to process time series data. Upon constructing the network, a Bayesian optimization algorithm is utilized to ascertain and fine-tune the ideal hyperparameters, thereby ensuring the network reaches its optimal performance level. With the hybrid deep learning model presented, an accurate fault diagnosis of a real five-cylinder drilling pump is carried out and the results confirmed its applicability and reliability. Two sets of comparative experiments validated the superiority of the proposed method. Additionally, the generalizability of the model is verified through domain adaptation experiments. The proposed method contributes to the safe production of the oil and gas sector by providing accurate and robust fault diagnosis of industrial equipment. • A novel intelligent drilling pumps fault diagnosis method, WCCN-BiLSTM, is proposed. • The WCCN is constructed for noise reduction and feature extraction of signals • The CBAM module embedded in WCCN enhances the feature representation of channel and spatial dimension. • The diagnostic ability and generalization ability of the model are verified by drilling pump data under different working conditions. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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