{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:55:48Z","timestamp":1777704948208,"version":"3.51.4"},"reference-count":31,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,12,2]]},"abstract":"<jats:p>Intelligent bearing fault diagnosis plays an important role in improving equipment safety and reducing equipment maintenance costs. Noise in the signal can seriously reduce the accuracy of fault diagnosis. To improve the accuracy of fault diagnosis, a novel noise reduction method based on weighted multi-scale morphological filter (WMMF) is proposed. Firstly, Teager energy operator (TEO) is used to amplify the morphological information of the signal. Then, a scale filtering operator using envelope entropy (SFOEE) is proposed to select appropriate scales. At these scales, the noise in the signal can be adequately suppressed. A new weighting method is proposed to integrate the selected scales to construct the WMMF. Finally, multi-headed self-attention capsule restricted boltzmann network (MSCRBN) is proposed to diagnose bearing faults.The performance of the TEO-SFOEE-WMMF-MSCRBN fault diagnosis method is verified on the CWRU dataset. Compared with existing fault diagnosis methods, this approach achieves 100% identification accuracy. The experimental results indicate that the proposed diagnosis method can effectively resist noise and precisely diagnose bearing faults.<\/jats:p>","DOI":"10.3233\/jifs-232737","type":"journal-article","created":{"date-parts":[[2023,9,12]],"date-time":"2023-09-12T13:37:37Z","timestamp":1694525857000},"page":"9915-9928","source":"Crossref","is-referenced-by-count":2,"title":["A new bearing fault diagnosis method based on improved weighted multi-scale morphological filter and multi-headed self-attention capsule restricted boltzmann network"],"prefix":"10.1177","volume":"45","author":[{"given":"Yiyang","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer and Communication Engineering, Dalian Jiaotong University, Dalian, Liaoning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changxian","family":"Li","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, Dalian Jiaotong University, Dalian, Liaoning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunxian","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Dalian Jiaotong University, Dalian, Liaoning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xudong","family":"Song","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, Dalian Jiaotong University, Dalian, Liaoning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-232737_ref1","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1016\/j.isatra.2022.06.027","article-title":"Conditional empirical wavelet transform with modified ratio of cyclic content for bearing fault diagnosis[J]","volume":"133","author":"Mo","year":"2023","journal-title":"ISA Transactions"},{"key":"10.3233\/JIFS-232737_ref2","doi-asserted-by":"crossref","first-page":"108317","DOI":"10.1016\/j.sigpro.2021.108317","article-title":"Selective fixed-filter active noise control based on convolutional neural network[J]","volume":"190","author":"Shi","year":"2022","journal-title":"Signal Processing"},{"issue":"6","key":"10.3233\/JIFS-232737_ref3","doi-asserted-by":"crossref","first-page":"065103","DOI":"10.1088\/1361-6501\/ac4a18","article-title":"A hybrid deep-learning model for fault diagnosis of rolling bearings in strong noise environments[J]","volume":"33","author":"Zhang","year":"2022","journal-title":"Measurement Science and Technology"},{"key":"10.3233\/JIFS-232737_ref4","doi-asserted-by":"crossref","first-page":"112400","DOI":"10.1016\/j.chaos.2022.112400","article-title":"Dispersion entropy-based Lempel-Ziv complexity: a new metric for signal analysis[J]","volume":"161","author":"Li","year":"2022","journal-title":"Chaos, Solitons & Fractals"},{"key":"10.3233\/JIFS-232737_ref5","doi-asserted-by":"crossref","unstructured":"Zhao H.M. , Liu J. , Chen H.Y. et al., Intelligent diagnosis using continuous wavelet transform and gauss convolutional deep belief network[J], IEEE Transactions on Reliability, 2022.","DOI":"10.1109\/TR.2022.3180273"},{"issue":"3","key":"10.3233\/JIFS-232737_ref6","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1177\/14759217211013535","article-title":"Structural damage detection method based on the complete ensemble empirical mode decomposition with adaptive noise: a model steel truss bridge case study[J]","volume":"21","author":"Mousavi","year":"2022","journal-title":"Structural Health Monitoring"},{"key":"10.3233\/JIFS-232737_ref7","doi-asserted-by":"crossref","first-page":"108216","DOI":"10.1016\/j.ymssp.2021.108216","article-title":"A fault information-guided variational mode decomposition (FIVMD) method for rolling element bearings diagnosis[J]","volume":"164","author":"Ni","year":"2022","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.3233\/JIFS-232737_ref8","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1016\/j.isatra.2023.03.022","article-title":"Intelligent fault detection scheme for constant-speed wind turbines based on improved multiscale fuzzy entropy and adaptive chaotic Aquila optimization-based support vector machine[J]","volume":"138","author":"Wang","year":"2023","journal-title":"ISA Transactions"},{"key":"10.3233\/JIFS-232737_ref9","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1016\/j.isatra.2020.12.054","article-title":"Modified multiscale weighted permutation entropy and optimized support vector machine method for rolling bearing fault diagnosis with complex signals[J]","volume":"114","author":"Wang","year":"2021","journal-title":"ISA Transactions"},{"issue":"3","key":"10.3233\/JIFS-232737_ref10","doi-asserted-by":"crossref","first-page":"4275","DOI":"10.3233\/JIFS-189688","article-title":"An intelligent fault diagnosis method based on curve segmentation and SVM for rail transit turnout[J]","volume":"41","author":"Ji","year":"2021","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"key":"10.3233\/JIFS-232737_ref11","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.ins.2023.03.142","article-title":"Multi-strategy competitive-cooperative co-evolutionary algorithm and its application[J]","volume":"635","author":"Zhou","year":"2023","journal-title":"Information Sciences"},{"issue":"4","key":"10.3233\/JIFS-232737_ref12","doi-asserted-by":"crossref","first-page":"045013","DOI":"10.1088\/1361-6501\/acb074","article-title":"An anti-noise fault diagnosis approach for rolling bearings based on multiscale CNN-LSTM and a deep residual learning model[J]","volume":"34","author":"Chen","year":"2023","journal-title":"Measurement Science and Technology"},{"issue":"2","key":"10.3233\/JIFS-232737_ref13","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1007\/s42417-022-00595-9","article-title":"Bearing fault diagnosis based on VMD fuzzy entropy and improved deep belief networks[J]","volume":"11","author":"Jin","year":"2023","journal-title":"Journal of Vibration Engineering & Technologies"},{"issue":"3","key":"10.3233\/JIFS-232737_ref14","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.3233\/JIFS-202730","article-title":"Intelligent fault diagnosis using image representation of multi-domain features[J]","volume":"42","author":"Zhang","year":"2022","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"key":"10.3233\/JIFS-232737_ref15","doi-asserted-by":"crossref","first-page":"104106","DOI":"10.1016\/j.dsp.2023.104106","article-title":"A Fault Diagnosis for Rolling Bearing Based on Multilevel Denoising Method and Improved Deep Residual Network[J]","author":"Feng","year":"2023","journal-title":"Digital Signal Processing"},{"issue":"8","key":"10.3233\/JIFS-232737_ref16","doi-asserted-by":"crossref","first-page":"085106","DOI":"10.1088\/1361-6501\/acce55","article-title":"A hybrid intelligent rolling bearing fault diagnosis method combining WKN-BiLSTM and attention mechanism[J]","volume":"34","author":"Wang","year":"2023","journal-title":"Measurement Science and Technology"},{"key":"10.3233\/JIFS-232737_ref17","doi-asserted-by":"crossref","first-page":"110270","DOI":"10.1016\/j.ymssp.2023.110270","article-title":"Product envelope spectrum optimization-gram: An enhanced envelope analysis for rolling bearing fault diagnosis[J]","volume":"193","author":"Chen","year":"2023","journal-title":"Mechanical Systems and Signal Processing"},{"issue":"3","key":"10.3233\/JIFS-232737_ref18","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/0734-189X(86)90002-2","article-title":"Introduction to mathematical morphology[J]","volume":"35","author":"Serra","year":"1986","journal-title":"Computer Vision, Graphics, and Image Processing"},{"issue":"7","key":"10.3233\/JIFS-232737_ref19","doi-asserted-by":"crossref","first-page":"4165","DOI":"10.1109\/TIM.2019.2948414","article-title":"Performance prediction using high-order differential mathematical morphology gradient spectrum entropy and extreme learning machine[J]","volume":"69","author":"Zhao","year":"2019","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"10.3233\/JIFS-232737_ref20","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1016\/j.ymssp.2017.08.020","article-title":"Average combination difference morphological filters for fault feature extraction of bearing[J]","volume":"100","author":"Lv","year":"2018","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.3233\/JIFS-232737_ref21","doi-asserted-by":"crossref","first-page":"106856","DOI":"10.1016\/j.measurement.2019.106856","article-title":"Research on an enhanced scale morphological-hat product filtering in incipient fault detection of rolling element bearings[J]","volume":"147","author":"Yan","year":"2019","journal-title":"Measurement"},{"key":"10.3233\/JIFS-232737_ref22","doi-asserted-by":"crossref","first-page":"111964","DOI":"10.1016\/j.measurement.2022.111964","article-title":"Research on mathematical morphological operators for fault diagnosis of rolling element bearings[J]","volume":"203","author":"Li","year":"2022","journal-title":"Measurement"},{"key":"10.3233\/JIFS-232737_ref23","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1016\/j.ymssp.2017.09.007","article-title":"Train axle bearing fault detection using a feature selection scheme based multi-scale morphological filter[J]","volume":"101","author":"Li","year":"2018","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.3233\/JIFS-232737_ref24","doi-asserted-by":"crossref","unstructured":"Yu J.B. , Xiao C.A. , Hu T.Z. et al., Selective weighted multi-scale morphological filter for fault feature extraction of rolling bearings[J], ISA Transactions 2022.","DOI":"10.1016\/j.isatra.2022.06.003"},{"key":"10.3233\/JIFS-232737_ref25","doi-asserted-by":"crossref","unstructured":"Luo X.J. and Zhang L.L. , Adaptive Morphological Filter for Extracting Features of Fault on Transmission Line[J], IEEJ Transactions on Electrical and Electronic Engineering, 2022.","DOI":"10.1002\/tee.23626"},{"issue":"3","key":"10.3233\/JIFS-232737_ref26","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1016\/j.ymssp.2007.09.010","article-title":"Multiscale morphology analysis and its application to fault diagnosis[J]","volume":"22","author":"Zhang","year":"2008","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.3233\/JIFS-232737_ref27","doi-asserted-by":"crossref","first-page":"112345","DOI":"10.1016\/j.measurement.2022.112345","article-title":"Mechanical fault intelligent diagnosis using attention-based dual-scale feature fusion capsule network[J]","volume":"207","author":"Zhang","year":"2023","journal-title":"Measurement"},{"issue":"2","key":"10.3233\/JIFS-232737_ref28","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1007\/s11063-022-10918-2","article-title":"Adaptive meta transfer learning with efficient self-attention for few-shot bearing fault diagnosis[J]","volume":"55","author":"Zhao","year":"2023","journal-title":"Neural Processing Letters"},{"key":"10.3233\/JIFS-232737_ref29","doi-asserted-by":"crossref","first-page":"119619","DOI":"10.1016\/j.eswa.2023.119619","article-title":"Saits: Self-attention-based imputation for time series[J]","volume":"219","author":"Du","year":"2023","journal-title":"Expert Systems with Applications"},{"key":"10.3233\/JIFS-232737_ref30","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.ymssp.2015.04.021","article-title":"Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study[J]","volume":"64","author":"Smith","year":"2015","journal-title":"Mechanical Systems and Signal Processing"},{"key":"10.3233\/JIFS-232737_ref31","first-page":"1","article-title":"A bearing fault diagnosis model based on CNN with wide convolution kernels[J]","author":"Song","year":"2021","journal-title":"Journal of Ambient Intelligence and Humanized Computing"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-232737","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:42:29Z","timestamp":1777455749000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-232737"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,2]]},"references-count":31,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.3233\/jifs-232737","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,2]]}}}