{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T13:34:51Z","timestamp":1770471291943,"version":"3.49.0"},"reference-count":49,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2018,6,22]],"date-time":"2018-06-22T00:00:00Z","timestamp":1529625600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2018,6,22]]},"abstract":"<jats:p>\n                    In allusion to performance degradation condition recognition issue for rolling bearing, a method based on improved pattern spectrum entropy (abbreviated as\n                    <jats:italic>IPSE<\/jats:italic>\n                    ) and fuzzy C-means algorithm (abbreviated as\n                    <jats:italic>FCM<\/jats:italic>\n                    ) is proposed in this paper. Basic pattern spectrum analysis is improved by introducing morphological corrosion operator and\n                    <jats:italic>IPSE<\/jats:italic>\n                    is proposed as the degradation feature parameter in describing bearing performance degradation degree. Simulation analysis shows that\n                    <jats:italic>IPSE<\/jats:italic>\n                    value will increase monotonously along with the deepening of the degradation degree.\n                    <jats:italic>IPSE<\/jats:italic>\n                    and degradation degree has a stable relevance. On this basis, in consideration of the fuzzy character of performance degradation condition boundary,\n                    <jats:italic>FCM<\/jats:italic>\n                    is introduced in degradation condition recognition so that the degradation condition could be recognized effectively in line with maximum subordination degree principle. Rolling bearing fatigue life enhancement testing was carried out in Hangzhou Bearing Test &amp; Research Center, the whole life data was gathered and applied using the proposed technique. The classification coefficient reaches 0.9849 and average fuzzy entropy gets 0.0239 for training set clustering, meanwhile, the whole recognition ratio reaches 90% for testing set. The analysis shows that the technique has a good clustering effect and an acceptable recognition result.\n                  <\/jats:p>","DOI":"10.3233\/jifs-169543","type":"journal-article","created":{"date-parts":[[2018,6,26]],"date-time":"2018-06-26T14:54:55Z","timestamp":1530024895000},"page":"3681-3693","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Bearing performance degradation condition recognition based on a combination of improved pattern spectrum entropy and fuzzy C-means"],"prefix":"10.1177","volume":"34","author":[{"given":"Bing","family":"Wang","sequence":"first","affiliation":[{"name":"Logistics Engineering College, ShangHai Maritime University, ShangHai, China"}]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"Logistics Engineering College, ShangHai Maritime University, ShangHai, China"}]},{"given":"Meihui","family":"Hou","sequence":"additional","affiliation":[{"name":"Logistics Engineering College, ShangHai Maritime University, ShangHai, China"}]},{"given":"Xiong","family":"Hu","sequence":"additional","affiliation":[{"name":"Logistics Engineering College, ShangHai Maritime University, ShangHai, China"}]}],"member":"179","published-online":{"date-parts":[[2018,6,22]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"1","article-title":"Prognostics of multiple failure modes in rotating machinery using a pattern-based classifier and cumulative incidence functions","author":"Ragab A.","year":"2016","unstructured":"A.Ragab, S.Yacout, M.S.Ouali and H.Osman, Prognostics of multiple failure modes in rotating machinery using a pattern-based classifier and cumulative incidence functions, Journal of Intelligent Manufacturing (2016), 1\u201320.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/s16060895"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2015.08.061"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11668-015-0056-z"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1002\/ceat.201600025"},{"key":"e_1_3_1_7_2","first-page":"1","article-title":"Compound fault prediction of rolling bearing using multimedia data","author":"Singh S.K.","year":"2017","unstructured":"S.K.Singh, S.Kumar and J.Dwivedi, Compound fault prediction of rolling bearing using multimedia data, Multimedia Tools and Applications (2017), 1\u201318.","journal-title":"Multimedia Tools and Applications"},{"key":"e_1_3_1_8_2","first-page":"2016","article-title":"Phase space similarity as a signature for rolling bearing fault diagnosis and remaining useful life estimation","author":"Liu F.","year":"2016","unstructured":"F.Liu, B.He, Y.Liu, S.Lu, Y.Zhao and J.Zhao, Phase space similarity as a signature for rolling bearing fault diagnosis and remaining useful life estimation, Shock and Vibration (2016), 2016\u20132022.","journal-title":"Shock and Vibration"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.proeng.2016.05.026"},{"key":"e_1_3_1_10_2","first-page":"30","article-title":"Overview of prognostics and health management of mechanical equipment","volume":"35","author":"Sun X.","year":"2016","unstructured":"X.Sun, G.Zhou, Y.Yu and F.Li, Overview of prognostics and health management of mechanical equipment, Ordnance Industry Automation35 (2016), 30\u201333.","journal-title":"Ordnance Industry Automation"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2015.12.011"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-015-8220-x"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/aa56c9"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1515\/msr-2016-0018"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1002\/qre.1771"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2015.2509913"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-gtd.2016.1459"},{"key":"e_1_3_1_18_2","first-page":"23","article-title":"A feature-reduction fuzzy clustering algorithm based on feature-weighted entropy","volume":"99","author":"Yang M.S.","year":"2017","unstructured":"M.S.Yang and Y.Nataliani, A feature-reduction fuzzy clustering algorithm based on feature-weighted entropy, IEEE Transactions on Fuzzy Systems99 (2017), 23\u201328.","journal-title":"IEEE Transactions on Fuzzy Systems"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11668-016-0133-y"},{"key":"e_1_3_1_20_2","first-page":"401","article-title":"Health assessment for rolling bearing based on local characteristic-scale decomposition- Approximate entropy and manifold distance","author":"Zhou B.","year":"2016","unstructured":"B.Zhou, C.Lu, L.Li, et al., Health assessment for rolling bearing based on local characteristic-scale decomposition- Approximate entropy and manifold distance, Intelligent Control and Automation (2016), 401\u2013406.","journal-title":"Intelligent Control and Automation"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsv.2015.09.016"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-spr.2016.0341"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1177\/1077546314532671"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRD.2016.2571341"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtbi.2015.11.019"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2016.06.014"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btw413"},{"key":"e_1_3_1_28_2","first-page":"1","article-title":"Application of bandwidth EMD and adaptive multi-scale morphology analysis for incipient fault diagnosis of rolling bearings","volume":"99","author":"Li Y.","year":"2017","unstructured":"Y.Li, M.Xu, X.Liang, et al., Application of bandwidth EMD and adaptive multi-scale morphology analysis for incipient fault diagnosis of rolling bearings, IEEE Transactions on Industrial Electronics99 (2017), 1\u201313.","journal-title":"IEEE Transactions on Industrial Electronics"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10409-016-0573-3"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2016.06.004"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2016.02.064"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12539-016-0172-9"},{"key":"e_1_3_1_33_2","first-page":"1115","article-title":"Fault diagnosis for power grid based on adaptive improved FCM algorithm","author":"Zhou Z.","year":"2016","unstructured":"Z.Zhou and X.Tong, Fault diagnosis for power grid based on adaptive improved FCM algorithm, Power and Energy Engineering Conference (APPEEC) (2016), 1115\u20131119.","journal-title":"Power and Energy Engineering Conference (APPEEC)"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2016.01.038"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cej.2016.06.127"},{"key":"e_1_3_1_36_2","first-page":"596","article-title":"Morphological pattern spectrum based image manipulation detection","author":"Nayak N.","year":"2017","unstructured":"N.Nayak, P.N.Hegde, Anusha, et al., Morphological pattern spectrum based image manipulation detection, IEEE Advance Computing Conference, 2017, pp. 596\u2013599.","journal-title":"IEEE Advance Computing Conference"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2016.04.038"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2017.02.003"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1177\/1077546314532671"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.3233\/IFS-162097"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2016.2526683"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2016.01.048"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jappgeo.2016.03.027"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.05.017"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/9281098"},{"key":"e_1_3_1_46_2","first-page":"107","article-title":"Performance degradation assessment of rolling element bearings based on hierarchical entropy and general distance","volume":"6","author":"Zhu K.","year":"2017","unstructured":"K.Zhu, Performance degradation assessment of rolling element bearings based on hierarchical entropy and general distance, Journal of Vibration & Control6 (2017), 107\u2013112.","journal-title":"Journal of Vibration & Control"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2017.05.033"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.microrel.2017.03.020"},{"key":"e_1_3_1_49_2","first-page":"1","article-title":"Cascaded hidden space feature mapping, fuzzy clustering, and nonlinear switching regression on large datasets","volume":"99","author":"Wang J.","year":"2017","unstructured":"J.Wang, H.Liu, X.Qian, et al., Cascaded hidden space feature mapping, fuzzy clustering, and nonlinear switching regression on large datasets, IEEE Transactions on Fuzzy Systems99 (2017), 1\u201313.","journal-title":"IEEE Transactions on Fuzzy Systems"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apacoust.2016.07.026"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-169543","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-169543","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-169543","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T18:18:42Z","timestamp":1770401922000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-169543"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,22]]},"references-count":49,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2018,6,22]]}},"alternative-id":["10.3233\/JIFS-169543"],"URL":"https:\/\/doi.org\/10.3233\/jifs-169543","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,6,22]]}}}