{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T17:53:42Z","timestamp":1762624422137,"version":"3.41.2"},"reference-count":21,"publisher":"Wiley","issue":"24","license":[{"start":{"date-parts":[[2020,7,6]],"date-time":"2020-07-06T00:00:00Z","timestamp":1593993600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LY16E050001"],"award-info":[{"award-number":["LY16E050001"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007834","name":"Natural Science Foundation of Ningbo","doi-asserted-by":"publisher","award":["2019A610118","2017A610138"],"award-info":[{"award-number":["2019A610118","2017A610138"]}],"id":[{"id":"10.13039\/100007834","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2020,12,25]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Traditional fault diagnosis methods assume that the rolling bearing fault samples are precise data. However, this assumption may be wrong when there are problems of measurement uncertainty etc. Due to this, this paper proposes an interval native Bayes uncertain fault diagnosis method based on the firefly algorithm. First, the interval fault vibration signals are decomposed by intrinsic time scale decomposition, and several proper rotation components (PRC) are obtained. Features of PRC, such as interval kurtosis etc., are extracted as fault samples. Then, an interval native Bayes uncertain fault diagnosis method is designed for these uncertain rolling bearing interval features. Conventionally, the fault diagnosis method utilizes the same interval features to distinguish different fault types. However, each type of fault has its own distinctive classification accuracy for different features. Thus, this paper uses the firefly algorithm to extract different optimal interval fault features for different fault types. Experimental results show: (i) the proposed interval native Bayes method can be effectively applied in the interval fault diagnosis of rolling bearing under measurement uncertainty conditions. (ii) Compared with two traditional methods which extract the same fault features for all fault types, the new method can obtain higher classification accuracy.<\/jats:p>","DOI":"10.1002\/cpe.5911","type":"journal-article","created":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T01:00:03Z","timestamp":1594083603000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A new interval native Bayes uncertain fault diagnosis method based on the firefly algorithm"],"prefix":"10.1002","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9567-2456","authenticated-orcid":false,"given":"Yongqi","family":"Chen","sequence":"first","affiliation":[{"name":"College of Science and Technology Ningbo University  Ningbo PR China"}]},{"given":"Qinge","family":"Dai","sequence":"additional","affiliation":[{"name":"College of Science and Technology Ningbo University  Ningbo PR China"}]},{"given":"Yang","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Science and Technology Ningbo University  Ningbo PR China"}]}],"member":"311","published-online":{"date-parts":[[2020,7,6]]},"reference":[{"issue":"9","key":"e_1_2_9_2_1","first-page":"43","article-title":"Fault analysis and diagnosis method of rolling bearing of rail vehicle running section","volume":"47","author":"Zou JS","year":"2015","journal-title":"Tech Inst Admin"},{"doi-asserted-by":"publisher","key":"e_1_2_9_3_1","DOI":"10.1016\/j.isatra.2018.04.005"},{"doi-asserted-by":"publisher","key":"e_1_2_9_4_1","DOI":"10.1109\/JSEN.2019.2898634"},{"doi-asserted-by":"publisher","key":"e_1_2_9_5_1","DOI":"10.1016\/j.mechmachtheory.2014.01.011"},{"doi-asserted-by":"publisher","key":"e_1_2_9_6_1","DOI":"10.1016\/j.mechmachtheory.2014.03.014"},{"issue":"4","key":"e_1_2_9_7_1","first-page":"77","article-title":"Fault diagnosis method of bearing based on EMD and BP network","volume":"33","author":"Zhang YJ","year":"2014","journal-title":"Microcomput Appl"},{"issue":"7","key":"e_1_2_9_8_1","first-page":"149","article-title":"Fault diagnosis of rolling bearings based on EMD and RBF","volume":"34","author":"Tian F","year":"2014","journal-title":"Ship Electron Eng"},{"issue":"7","key":"e_1_2_9_9_1","first-page":"1683","article-title":"VMD based adaptive composite multiscale fuzzy entropy and its application to fault diagnosis of rolling bearing","volume":"32","author":"Zheng JD","year":"2017","journal-title":"J Aerospace Power"},{"issue":"3","key":"e_1_2_9_10_1","first-page":"129","article-title":"Feature extraction method for rolling bearing vibration signals based on VMD and Volterra prediction model","volume":"37","author":"Zhang YQ","year":"2018","journal-title":"J Vib Shock"},{"issue":"11","key":"e_1_2_9_11_1","first-page":"2624","article-title":"Fault diagnosis of rolling bearing based on ITD fuzzy entropy and GG clustering","volume":"35","author":"Zhang LG","year":"2014","journal-title":"Chin J Sci Instrument"},{"doi-asserted-by":"publisher","key":"e_1_2_9_12_1","DOI":"10.21595\/jve.2018.19613"},{"issue":"1","key":"e_1_2_9_13_1","first-page":"161","article-title":"Support vector classification with input data uncertain [J]","volume":"17","author":"Bi JB","year":"2005","journal-title":"Adv Neural Inf Process Syst"},{"issue":"1","key":"e_1_2_9_14_1","first-page":"148","article-title":"Exploiting uncertain data in support vector classification","volume":"1","author":"Yang JQ","year":"2007","journal-title":"Knowl Based Intell Inf Eng Syst"},{"issue":"1","key":"e_1_2_9_15_1","first-page":"64","article-title":"Decision trees for uncertain data","volume":"23","author":"Smith T","year":"2009","journal-title":"IEEE Trans Knowl Data Eng"},{"doi-asserted-by":"publisher","key":"e_1_2_9_16_1","DOI":"10.1016\/S0165-0114(01)00195-6"},{"doi-asserted-by":"crossref","unstructured":"RenJ LeeSD ChenXL et al.. 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