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Firstly, the wavelet attention (WA) mechanism is employed to respectively enhance two types of features, namely the high\u2010frequency periodic pulse information and the low\u2010frequency smooth information generated during drilling pump failures. Subsequently, the parallel mechanism of convolution in the temporal convolutional network (TCN) is utilized to effectively extract the high\u2010frequency periodic pulse information. Meanwhile, the memory module of Memory Transformer is used in parallel to effectively extract the low\u2010frequency smooth information. Finally, the results of the two groups are combined and classified by the fully connected layer. With the constructed network, the Bayesian optimization (BO) is utilized to seek the optimal combination of ideal hyperparameters. Through the proposed parallel deep learning model, fault diagnosis with an accuracy rate reaching 99.16% has been carried out on an actual five\u2010cylinder drilling pump, and the fluctuation range of the accuracy rate in five independent experiments is 0.34%. The proposed model for drilling pump fault diagnosis has demonstrated robustness and accuracy through verification.<\/jats:p>","DOI":"10.1002\/qre.70007","type":"journal-article","created":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T04:27:56Z","timestamp":1750393676000},"page":"2810-2829","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Parallel Diagnosis Scheme for Drilling Pumps Using WA\u2010Memory Transformer\u2010TCN Network"],"prefix":"10.1002","volume":"41","author":[{"given":"Qingsong","family":"Chen","sequence":"first","affiliation":[{"name":"School of Mechatronic Engineering Southwest Petroleum University  Chengdu Sichuan P. R. 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