{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T12:57:37Z","timestamp":1790945857398,"version":"4.1.0"},"reference-count":38,"publisher":"AIP Publishing","issue":"10","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51605309"],"award-info":[{"award-number":["51605309"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012130","name":"Chinese Aeronautical Establishment","doi-asserted-by":"publisher","award":["20230033054001"],"award-info":[{"award-number":["20230033054001"]}],"id":[{"id":"10.13039\/501100012130","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005047","name":"Natural Science Foundation of Liaoning Province","doi-asserted-by":"publisher","award":["2022-MS-299"],"award-info":[{"award-number":["2022-MS-299"]}],"id":[{"id":"10.13039\/501100005047","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007620","name":"Department of Education of Liaoning Province","doi-asserted-by":"publisher","award":["LJKMZ20220529"],"award-info":[{"award-number":["LJKMZ20220529"]}],"id":[{"id":"10.13039\/501100007620","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Steady Fundamental Supporting Project Phase II for Scientific Research Institute of Military Industry","award":["03020051"],"award-info":[{"award-number":["03020051"]}]}],"content-domain":{"domain":["pubs.aip.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,10,1]]},"abstract":"<jats:p>When bearing failure occurs, the fault information in the vibration signals is characterized by weakness, complexity, and a compound nature, making it difficult to accurately identify the compound fault of the bearing. 1D local binary patterns (1D-LBP) depict fault information of bearings from the aspect of local analysis. Unexpectedly, the quantitative 1D-LBP shows no consideration of the features of signals caused by bearing failure. Therefore, it is difficult to correctly quantize signals and extract failure information. The root mean square, an effective indicator of impact signatures in bearing faults, serves as the quantitative criterion for enhancing the 1D-LBP approach. Furthermore, signal fusion is applied to improve 1D-LBP, and quantized reconstructed signals are blended based on the harmonic fusion vector bispectrum (HFVB) to enhance failure features. Finally, the extraction of failure features and identification of faults in bearings are carried out through spectral analysis. The result of the comparison indicates that the proposed improved 1D-ILBP-HFVB method can extract feature information more accurately during bearing faults while effectively suppressing noise and distinguishing compound fault types in rolling bearings.<\/jats:p>","DOI":"10.1063\/5.0313200","type":"journal-article","created":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T12:44:13Z","timestamp":1790945053000},"update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":0,"title":["Diagnosis of compound fault of bearing with improve 1D local binary patterns and vector bispectrum"],"prefix":"10.1063","volume":"97","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8161-6979","authenticated-orcid":false,"given":"Mingyue","family":"Yu","sequence":"first","affiliation":[{"name":"Shenyang Aerospace University , Shenyang,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0506-2575","authenticated-orcid":false,"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"Shenyang Aerospace University , Shenyang,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhujun","family":"Wang","sequence":"additional","affiliation":[{"name":"Shenyang Aerospace University , Shenyang,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0172-0212","authenticated-orcid":false,"given":"Fanzheng","family":"Meng","sequence":"additional","affiliation":[{"name":"Shenyang Aerospace University , Shenyang,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8457-8103","authenticated-orcid":false,"given":"Wenyou","family":"Du","sequence":"additional","affiliation":[{"name":"Shenyang Aerospace University , Shenyang,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"317","published-online":{"date-parts":[[2026,10,2]]},"reference":[{"key":"2026100208440682200_c1","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1016\/j.isatra.2020.05.043","article-title":"Reweighted generalized minimax-concave sparse regularization and application in machinery fault diagnosis","volume":"105","year":"2020","journal-title":"ISA Trans."},{"key":"2026100208440682200_c2","doi-asserted-by":"publisher","first-page":"109552","DOI":"10.1016\/j.measurement.2021.109552","article-title":"Fault diagnosis of rolling bearing based on multiscale one-dimensional hybrid binary pattern","volume":"181","year":"2021","journal-title":"Measurement"},{"issue":"2","key":"2026100208440682200_c3","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.32604\/cmes.2021.016980","article-title":"Fusion fault diagnosis approach to rolling bearing with vibrational and acoustic emission signals","volume":"129","year":"2021","journal-title":"Comput. Model. Eng. Sci."},{"issue":"3","key":"2026100208440682200_c4","doi-asserted-by":"publisher","first-page":"2495","DOI":"10.1109\/JSEN.2022.3227244","article-title":"Combined square envelope spectrum by integrating multiband bearing fault information","volume":"23","year":"2023","journal-title":"IEEE Sens. J."},{"issue":"7","key":"2026100208440682200_c5","doi-asserted-by":"publisher","first-page":"1946","DOI":"10.3390\/s20071946","article-title":"GMPSO-VMD algorithm and its application to rolling bearing fault feature extraction","volume":"20","year":"2020","journal-title":"Sensors"},{"issue":"3","key":"2026100208440682200_c6","doi-asserted-by":"publisher","first-page":"16878132221086132","DOI":"10.1177\/16878132221086132","article-title":"Intelligent data expansion approach of vibration gray texture images of rolling bearing based on improved WGAN-GP","volume":"14","year":"2022","journal-title":"Adv. Mech. Eng."},{"key":"2026100208440682200_c7","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2010.2044957","article-title":"A completed modeling of local binary pattern operator for texture classification","volume":"19","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"2026100208440682200_c8","doi-asserted-by":"publisher","first-page":"106019","DOI":"10.1016\/j.asoc.2019.106019","article-title":"An improved feature extraction method using texture analysis with LBP for bearing fault diagnosis","volume":"87","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"2026100208440682200_c9","doi-asserted-by":"publisher","first-page":"137517","DOI":"10.1109\/ACCESS.2020.3011980","article-title":"An intelligent approach for bearing fault diagnosis: Combination of 1D-LBP and GRA","volume":"8","year":"2020","journal-title":"IEEE Access"},{"issue":"11","key":"2026100208440682200_c10","doi-asserted-by":"publisher","first-page":"64","DOI":"10.3901\/JME.2011.11.064","article-title":"Intelligent fault diagnosis method based on vector-bispectrum","volume":"47","year":"2011","journal-title":"J.\u00a0Mech. Eng."},{"issue":"18","key":"2026100208440682200_c11","first-page":"169","article-title":"A damage severity assessment method for bearings with rolling element damage","volume":"32","year":"2013","journal-title":"J.\u00a0Vib. Shock"},{"issue":"2022","key":"2026100208440682200_c12","doi-asserted-by":"publisher","first-page":"111637","DOI":"10.1016\/j.measurement.2022.111637","article-title":"Composite fault diagnosis for rolling bearing based on parameter-optimized VMD","volume":"201","year":"2022","journal-title":"Measurement"},{"issue":"12","key":"2026100208440682200_c13","doi-asserted-by":"publisher","first-page":"125105","DOI":"10.1088\/1361-6501\/ac8894","article-title":"Rolling bearing fault diagnosis method based on multi-sensor two-stage fusion","volume":"33","year":"2022","journal-title":"Meas. Sci. Technol."},{"key":"2026100208440682200_c14","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1016\/j.isatra.2022.07.026","article-title":"Local lowest-rank dynamic mode decomposition for transient feature extraction of rolling bearings","volume":"133","year":"2023","journal-title":"ISA Trans."},{"key":"2026100208440682200_c15","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1016\/j.eswa.2017.07.007","article-title":"Feature based local binary pattern for rotation invariant texture classification","volume":"88","year":"2017","journal-title":"Exp. Syst. Appl."},{"issue":"1","key":"2026100208440682200_c16","doi-asserted-by":"publisher","first-page":"537","DOI":"10.2991\/ijcis.d.201228.002","article-title":"Fault diagnosis of bearings using an intelligence-based autoregressive learning Lyapunov algorithm","volume":"14","year":"2021","journal-title":"Int. J. Comput. Intelligence Systems"},{"issue":"10","key":"2026100208440682200_c17","doi-asserted-by":"publisher","first-page":"e02613","DOI":"10.1016\/j.heliyon.2019.e02613","article-title":"Effective and fast binarization method for combined degradation on ancient documents","volume":"5","year":"2019","journal-title":"Heliyon"},{"issue":"2","key":"2026100208440682200_c18","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1006\/mssp.2001.1462","article-title":"Artificial neural network based fault diagnostics of rolling element bearings using time-domain features","volume":"17","year":"2003","journal-title":"Mech. Syst. Signal Process."},{"key":"2026100208440682200_c19","first-page":"319","article-title":"Hybrid LBP and HOG features with particle swarm optimization for facial emotion recognition","year":"2025"},{"key":"2026100208440682200_c20","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1186\/1687-5281-2013-29","article-title":"Fault diagnosis of induction motors utilizing local binary pattern-based texture analysis","volume":"2013","journal-title":"EURASIP J. Image Video Process."},{"key":"2026100208440682200_c21","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.jsv.2016.01.015","article-title":"Generalized stepwise demodulation transform and synchro squeezing for time\u2013frequency analysis and bearing fault diagnosis","volume":"368","year":"2016","journal-title":"J.\u00a0Sound Vib."},{"key":"2026100208440682200_c22","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2025.113201","article-title":"Improved mode decomposition method for vibration signal denoising of aero-engine shaft bearing","volume":"238","year":"2025","journal-title":"Mech. Syst. Signal Process."},{"key":"2026100208440682200_c23","doi-asserted-by":"publisher","first-page":"3805729","DOI":"10.1155\/2019\/3805729","article-title":"Bispectrum texture feature manifold for feature extraction in rolling bearing fault diagnosis","volume":"2019","journal-title":"Math. Probl. Eng."},{"issue":"6","key":"2026100208440682200_c24","doi-asserted-by":"publisher","first-page":"800","DOI":"10.5755\/j01.mech.24.6.22471","article-title":"Rolling bearing fault detection using autocorrelation based morphological filtering and empirical mode decomposition","volume":"24","year":"2019","journal-title":"Mechanics"},{"issue":"2","key":"2026100208440682200_c25","doi-asserted-by":"publisher","first-page":"e0297935","DOI":"10.1371\/journal.pone.0297935","article-title":"Vibration characterization of rolling bearings with compound fault features under multiple interference factors","volume":"19","year":"2024","journal-title":"PLoS One"},{"key":"2026100208440682200_c26","doi-asserted-by":"publisher","first-page":"110664","DOI":"10.1016\/J.YMSSP.2023.110664","article-title":"Machinery multi-sensor fault diagnosis based on adaptive multivariate feature mode decomposition and multi-attention fusion residual convolutional neural network","volume":"202","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"2026100208440682200_c27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/10589759.2024.2408670","article-title":"Remaining useful life prediction of rolling bearings based on CNN-GRU-MSA with multi-channel feature fusion","year":"2024","journal-title":"Nondestr. Test. Eval."},{"issue":"4","key":"2026100208440682200_c28","doi-asserted-by":"publisher","first-page":"3848","DOI":"10.1109\/JSEN.2022.3232707","article-title":"A feature extraction method using VMD and improved envelope spectrum entropy for rolling bearing fault diagnosis","volume":"23","year":"2023","journal-title":"IEEE Sens. J."},{"key":"2026100208440682200_c29","doi-asserted-by":"publisher","first-page":"3506010","DOI":"10.1109\/TIM.2023.3237848","article-title":"A three-dimensional vibration data compression method for rolling bearing condition monitoring","volume":"72","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"3","key":"2026100208440682200_c30","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1177\/09544100221107256","article-title":"Feature extraction of rotor-stator rubbing faults based on harmonic fusion vector bispectrum, ITD and Hjorth parameters","volume":"237","year":"2023","journal-title":"Proc. Inst. Mech. Eng., Part G"},{"issue":"6","key":"2026100208440682200_c31","doi-asserted-by":"publisher","first-page":"3617","DOI":"10.1177\/14759217241270747","article-title":"Combined failure identification of intermediate bearing in aeroengine based on wavelet transform, second-order difference, and 1D-LBP","volume":"24","year":"2025","journal-title":"Struct. Health Monit."},{"issue":"Part A","key":"2026100208440682200_c32","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.isatra.2025.08.029","article-title":"Fault identification for rolling bearing based on ITD-ILBP-Hankel matrix","volume":"167","year":"2025","journal-title":"ISA Trans."},{"key":"2026100208440682200_c33","doi-asserted-by":"publisher","first-page":"115990","DOI":"10.1016\/j.image.2020.115990","article-title":"Face anti-spoofing detection based on DWT-LBP-DCT features","volume":"89","year":"2020","journal-title":"Signal Process.: Image Commun."},{"issue":"1","key":"2026100208440682200_c34","doi-asserted-by":"publisher","first-page":"16","DOI":"10.17531\/ein.2023.1.16","article-title":"Intelligent fault diagnosis of rolling bearings based on continuous wavelet transform-multiscale feature fusion and improved channel attention mechanism","volume":"25","year":"2023","journal-title":"Maint. Reliab."},{"key":"2026100208440682200_c35","doi-asserted-by":"publisher","first-page":"3839","DOI":"10.1007\/s42835-023-01453-8","article-title":"Bearing fault diagnosis of single-channel data by a 3D DCN with bilinear LBP and modified KPCA","volume":"18","year":"2023","journal-title":"J.\u00a0Electr. Eng. Technol"},{"key":"2026100208440682200_c36","doi-asserted-by":"publisher","first-page":"109964","DOI":"10.1016\/j.ress.2024.109964","article-title":"Domain generalization for cross-domain fault diagnosis: An application-oriented perspective and a benchmark study","volume":"245","year":"2024","journal-title":"Reliab. Eng. Syst. Saf."},{"issue":"4","key":"2026100208440682200_c37","first-page":"11","article-title":"Bearing fault diagnosis method based on morphological filtering, time-delayed autocorrelation and time-frequency slice analysis","volume":"33","year":"2014","journal-title":"J.\u00a0Vib. Shock"},{"issue":"2","key":"2026100208440682200_c38","doi-asserted-by":"publisher","first-page":"165","DOI":"10.13433\/j.cnki.1003-8728.20200322","article-title":"CQTBEW algorithm and its application in bearing early fault diagnosis","volume":"41","year":"2022","journal-title":"Mech. Sci. Technol. Aerosp. Eng."}],"container-title":["Review of Scientific Instruments"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.aip.org\/aip\/rsi\/article-pdf\/doi\/10.1063\/5.0313200\/21160019\/105201_1_5.0313200.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/pubs.aip.org\/aip\/rsi\/article-pdf\/doi\/10.1063\/5.0313200\/21160019\/105201_1_5.0313200.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T12:44:14Z","timestamp":1790945054000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.aip.org\/rsi\/article\/97\/10\/105201\/3405849\/Diagnosis-of-compound-fault-of-bearing-with"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,1]]},"references-count":38,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2026,10,1]]}},"URL":"https:\/\/doi.org\/10.1063\/5.0313200","relation":{},"ISSN":["0034-6748","1089-7623"],"issn-type":[{"value":"0034-6748","type":"print"},{"value":"1089-7623","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,10]]},"published":{"date-parts":[[2026,10,1]]},"article-number":"105201"}}