{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T17:05:28Z","timestamp":1765040728036,"version":"build-2065373602"},"reference-count":11,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,12,26]],"date-time":"2017-12-26T00:00:00Z","timestamp":1514246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Shanxi Province, China and Science and technology Foundation of Shanxi Provincial","award":["2015011063"],"award-info":[{"award-number":["2015011063"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Strong background noise and complicated interfering signatures when implementing vibration-based monitoring make it difficult to extract the weak diagnostic features due to incipient faults in a multistage gearbox. This can be more challenging when multiple faults coexist. This paper proposes an effective approach to extract multi-fault features of a wind turbine gearbox based on an integration of minimum entropy deconvolution (MED) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA). By using simulated periodic transient signals with different noise to signal ratios (SNR), it evaluates the outstanding performance of MED in noise suppression and reveals the deficient in extract multiple impulses. On the other hand, MOMEDA can performs better in extracting multiple pulses but not robust to noise influences. To compromise the merits of them, therefore the diagnostic approach is formalized by extracting the multiple weak features with MOMEDA based on the MED denoised signals. Experimental verification based on vibrations from a wind turbine gearbox test bed shows that the approach allows successful identification of multiple faults occurring simultaneously on the shaft and bearing in the high speed transmission stage of the gearbox.<\/jats:p>","DOI":"10.3390\/e20010010","type":"journal-article","created":{"date-parts":[[2017,12,26]],"date-time":"2017-12-26T11:13:23Z","timestamp":1514286803000},"page":"10","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["A Novel Method for Multi-Fault Feature Extraction of a Gearbox under Strong Background Noise"],"prefix":"10.3390","volume":"20","author":[{"given":"Zhijian","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mechanical and Power Engineering, North University of China, Xueyuan Road, Taiyuan 030051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Power Engineering, North University of China, Xueyuan Road, Taiyuan 030051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhifang","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Mechanical and Power Engineering, North University of China, Xueyuan Road, Taiyuan 030051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rijun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Power Engineering, North University of China, Xueyuan Road, Taiyuan 030051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,12,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, Z.J., and Wang, J.Y. (2017). Weak Fault Diagnosis of Wind Turbine Gearboxes Based on MED-LMD. Entropy, 19.","DOI":"10.3390\/e19060277"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.isatra.2014.09.006","article-title":"A novel procedure for diagnosing multiple faults in rotating machinery","volume":"55","author":"Wang","year":"2015","journal-title":"ISA Trans."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Shi, Z.L., Song, W.Q., and Taheri, S. (2016). Improved LMD, permutation entropy and optimized K-means to fault diagnosis for roller bearings. Entropy, 18.","DOI":"10.3390\/e18030070"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1006\/jsvi.2001.4116","article-title":"Identification of multiple faults in rotor systems","volume":"254","author":"Bachschmid","year":"2002","journal-title":"J. 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Part C"},{"key":"ref_10","first-page":"70","article-title":"Weak fault diagnosis for rolling element bearing based on MED-EEMD","volume":"30","author":"Wang","year":"2014","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.ymssp.2016.05.036","article-title":"Multipoint Optimal Minimum Entropy Deconvolution and Convolution Fix: Application to vibration fault detection","volume":"82","author":"McDonald","year":"2017","journal-title":"Mech. Syst. Signal Process."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/20\/1\/10\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:55:35Z","timestamp":1760208935000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/20\/1\/10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,12,26]]},"references-count":11,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2018,1]]}},"alternative-id":["e20010010"],"URL":"https:\/\/doi.org\/10.3390\/e20010010","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2017,12,26]]}}}