A hybrid deep learning model towards fault diagnosis of drilling pump

Guo, J, Yang, Y, Li, H ORCID: 0000-0001-6429-9097, Wang, J, Tang, A, Shan, D and Huang, B (2025) A hybrid deep learning model towards fault diagnosis of drilling pump. Applied Energy, 372. ISSN 0306-2619

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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.

Item Type:	Article
Uncontrolled Keywords:	Drilling pump; Fault diagnosis; WaveletKernelNet-CBAM net; Bidirectional long -short term memory; 40 Engineering; Machine Learning and Artificial Intelligence; 09 Engineering; 14 Economics; Energy; 33 Built environment and design; 38 Economics; 40 Engineering
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Engineering
Publisher:	Elsevier
Date of acceptance:	20 June 2024
Date of first compliant Open Access:	28 July 2025
Date Deposited:	28 Jul 2025 14:35
Last Modified:	28 Jul 2025 14:45
DOI or ID number:	10.1016/j.apenergy.2024.123773
URI:	https://researchonline.ljmu.ac.uk/id/eprint/26835

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