{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:23:02Z","timestamp":1766067782983,"version":"build-2065373602"},"reference-count":60,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T00:00:00Z","timestamp":1692921600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"key laboratory of nuclear reactor system design open fund","award":["HT-KFKT-022017101"],"award-info":[{"award-number":["HT-KFKT-022017101"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Data-driven mechanical fault diagnosis has been successfully developed in recent years, and the task of training and testing data from the same distribution has been well-solved. However, for some large machines with complex mechanical structures, such as reciprocating pumps, it is often not possible to obtain data from specific sensor locations. When the sensor position is changed, the distribution of the features of the signal data also changes and the fault diagnosis problem becomes more complicated. In this paper, a cross-sensor transfer diagnosis method is proposed, which utilizes the sharing of information collected by sensors between different locations of the machine to complete a more accurate and comprehensive fault diagnosis. To enhance the model\u2019s perception ability towards the critical part of the fault signal, the local attention mechanism is embedded into the proposed method. Finally, the proposed method is validated by applying it to experimentally acquired vibration signal data of reciprocating pumps. Excellent performance is demonstrated in terms of fault diagnosis accuracy and sensor generalization capability. The transferability of practical industrial faults among different sensors is confirmed.<\/jats:p>","DOI":"10.3390\/s23177432","type":"journal-article","created":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T06:10:22Z","timestamp":1693203022000},"page":"7432","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Novel Cross-Sensor Transfer Diagnosis Method with Local Attention Mechanism: Applied in a Reciprocating Pump"],"prefix":"10.3390","volume":"23","author":[{"given":"Chen","family":"Wang","sequence":"first","affiliation":[{"name":"School of Nuclear Science and Technology, Naval University of Engineering, Wuhan 430033, China"}]},{"given":"Ling","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Nuclear Science and Technology, Naval University of Engineering, Wuhan 430033, China"}]},{"given":"Yongfa","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Nuclear Science and Technology, Naval University of Engineering, Wuhan 430033, China"}]},{"given":"Liming","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Nuclear Science and Technology, Naval University of Engineering, Wuhan 430033, China"},{"name":"Chongqing Pump Industry Co., Ltd., Chongqing 400033, China"},{"name":"Chongqing Machine Tool Co., Ltd., Chongqing 401336, China"}]},{"given":"Tian","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Nuclear Science and Technology, Naval University of Engineering, Wuhan 430033, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"23301","DOI":"10.1109\/ACCESS.2021.3056437","article-title":"An Integrated Approach Based on Improved CEEMDAN and LSTM Deep Learning Neural Network for Fault Diagnosis of Reciprocating Pump","volume":"9","author":"Bie","year":"2021","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1590\/S1678-58782004000200012","article-title":"Diagnostic significance of orbit shape analysis and its application to improve machine fault detection","volume":"26","author":"Bachschmid","year":"2004","journal-title":"J. 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