{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T13:45:54Z","timestamp":1780494354789,"version":"3.54.1"},"reference-count":38,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,20]],"date-time":"2022-05-20T00:00:00Z","timestamp":1653004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>It is critical to deploy wireless data transmission technologies remotely, in real-time, to monitor the health state of diesel engines dynamically. The usual approach to data compression is to collect data first, then compress it; however, we cannot ensure the correctness and efficiency of the data. Based on sparse Bayesian optimization block learning, this research provides a method for compression reconstruction and fault diagnostics of diesel engine vibration data. This method\u2019s essential contribution is combining compressive sensing technology with fault diagnosis. To achieve a better diagnosis effect, we can effectively improve the wireless transmission efficiency of the vibration signal. First, the dictionary is dynamically updated by learning the dictionary using singular value decomposition to produce the ideal sparse form. Second, a block sparse Bayesian learning boundary optimization approach is utilized to recover structured non-sparse signals rapidly. A detailed assessment index of the data compression effect is created. Finally, the experimental findings reveal that the approach provided in this study outperforms standard compression methods in terms of compression efficiency and accuracy and its ability to produce the desired fault diagnostic effect, proving the usefulness of the proposed method.<\/jats:p>","DOI":"10.3390\/s22103884","type":"journal-article","created":{"date-parts":[[2022,5,21]],"date-time":"2022-05-21T09:18:08Z","timestamp":1653124688000},"page":"3884","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Compression Reconstruction and Fault Diagnosis of Diesel Engine Vibration Signal Based on Optimizing Block Sparse Bayesian Learning"],"prefix":"10.3390","volume":"22","author":[{"given":"Huajun","family":"Bai","sequence":"first","affiliation":[{"name":"Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang 050003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Wen","sequence":"additional","affiliation":[{"name":"Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang 050003, China"},{"name":"Hebei Key Laboratory of Condition Monitoring and Assessment of Mechanical Equipment, Shijiazhuang 050003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2528-309X","authenticated-orcid":false,"given":"Yunfei","family":"Ma","sequence":"additional","affiliation":[{"name":"Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang 050003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xisheng","family":"Jia","sequence":"additional","affiliation":[{"name":"Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang 050003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"108786","DOI":"10.1016\/j.measurement.2020.108786","article-title":"Random convolutional neural network structure: An intelligent health monitoring scheme for diesel engines","volume":"171","author":"Wang","year":"2021","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"12014","DOI":"10.1088\/1742-6596\/1670\/1\/012014","article-title":"Performance Prediction Method of Prognostics and Health Management of Marine Diesel Engine","volume":"1670","author":"Lan","year":"2020","journal-title":"J. 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