{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T13:50:15Z","timestamp":1768744215538,"version":"3.49.0"},"reference-count":43,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T00:00:00Z","timestamp":1700179200000},"content-version":"vor","delay-in-days":320,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52175379"],"award-info":[{"award-number":["52175379"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012131","name":"Department of Science and Technology of Liaoning Province","doi-asserted-by":"publisher","award":["2022JH2\/101300268"],"award-info":[{"award-number":["2022JH2\/101300268"]}],"id":[{"id":"10.13039\/501100012131","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:p>The vibration signal is easily interfered by noise due to the influence of environment and other factors, which can lead to the poor adaptability, low accuracy of remaining useful life (RUL) prediction, and other problems. To solve this problem, this paper proposes a novel RUL prediction method, which is based on multiscale stacking deep residual shrinkage network (MSDRSN). MSDRSN combines the ability of stacking in improving prediction accuracy and the advantages of deep residual shrinkage network (DRSN) in denoising. First, cumulative sum (CUSUM) from statistics is used to divide the full life cycle of the rolling bearings and discover the points of failure. Second, stacking is used for feature learning on the raw data, multiple convolutional kernels of different scales are selected as base\u2010learners, and fully connected neural networks are selected as meta\u2010learners for feature fusion and learning. Then, DRSN is used to do prediction, and the acquired results are fitted with Savitzky\u2013Golay (SG) smoothing. Finally, the effectiveness of the proposed method is proved by the IEEE PHM 2012 data challenge dataset. Compared with the multiscale convolutional neural network with fully connected layer (MSCNN\u2010FC) and the bidirectional long short\u2010term memory (BiLSTM) for RUL prediction under the noise. Using the proposed method, the mean absolute error (MSE) of the best result is 0.002 and the mean square error (MSE) is 0.014; meanwhile, the coefficient of determination (<jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup>) of the best prediction result can reach 97.6%. It is also compared with other machine learning methods, and all the results prove the accuracy and effectiveness of the proposed method for RUL prediction applications.<\/jats:p>","DOI":"10.1155\/2023\/6665534","type":"journal-article","created":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T22:50:06Z","timestamp":1700261406000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Hybrid Deep Learning Prediction Method of Remaining Useful Life for Rolling Bearings Using Multiscale Stacking Deep Residual Shrinkage Network"],"prefix":"10.1155","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4989-9091","authenticated-orcid":false,"given":"Xudong","family":"Song","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6074-0990","authenticated-orcid":false,"given":"Qi","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9769-3447","authenticated-orcid":false,"given":"Rui","family":"Sun","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4415-2917","authenticated-orcid":false,"given":"Rui","family":"Tian","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0231-3989","authenticated-orcid":false,"given":"Jialiang","family":"Sun","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2291-604X","authenticated-orcid":false,"given":"Changxian","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9066-5000","authenticated-orcid":false,"given":"Yunxian","family":"Cui","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2023,11,17]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2022.108528"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2021.109310"},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2022.108531"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.isatra.2020.12.052"},{"key":"e_1_2_11_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2021.109022"},{"key":"e_1_2_11_6_2","doi-asserted-by":"publisher","DOI":"10.1177\/1475921719841690"},{"key":"e_1_2_11_7_2","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899X\/1043\/4\/042008"},{"key":"e_1_2_11_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2021.110798"},{"key":"e_1_2_11_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2021.107394"},{"key":"e_1_2_11_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/S10035-021-01137-Y"},{"key":"e_1_2_11_11_2","doi-asserted-by":"publisher","DOI":"10.1021\/acs.energyfuels.1c03270"},{"key":"e_1_2_11_12_2","doi-asserted-by":"publisher","DOI":"10.1177\/09544070211031401"},{"key":"e_1_2_11_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2021.108300"},{"key":"e_1_2_11_14_2","doi-asserted-by":"publisher","DOI":"10.1049\/gtd2.12385"},{"key":"e_1_2_11_15_2","doi-asserted-by":"publisher","DOI":"10.3390\/act11060151"},{"key":"e_1_2_11_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compchemeng.2021.107599"},{"key":"e_1_2_11_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.09.035"},{"key":"e_1_2_11_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apm.2021.08.033"},{"key":"e_1_2_11_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2017.11.016"},{"key":"e_1_2_11_20_2","doi-asserted-by":"publisher","DOI":"10.3901\/jme.2009.12.089"},{"key":"e_1_2_11_21_2","doi-asserted-by":"crossref","unstructured":"WangF. ChenX. LiuC. YanD. HanQ. andLiH. Reliability assessment of rolling bearing based on principal component analysis and Weibull proportional hazard model Proceedings of the 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) May 2017 Turin Italy IEEE https:\/\/doi.org\/10.1109\/I2MTC.2017.7969987 2-s2.0-85026828340.","DOI":"10.1109\/I2MTC.2017.7969987"},{"key":"e_1_2_11_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.2999442"},{"key":"e_1_2_11_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3097311"},{"key":"e_1_2_11_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2021.107531"},{"key":"e_1_2_11_25_2","article-title":"Bearing remaining life prediction based on full convolutional layer neural networks","volume":"30","author":"Zhang J.","year":"2019","journal-title":"China Mechanical Engineering"},{"key":"e_1_2_11_26_2","doi-asserted-by":"publisher","DOI":"10.13462\/j.cnki.mmtamt.2020.10.040"},{"key":"e_1_2_11_27_2","first-page":"81","article-title":"Application of improved wavelet transform and minimum entropy deconvolution in railway bearing fault diagnosis","volume":"40","author":"Zhicheng Q.","year":"2021","journal-title":"Journal of Vibration and Shock"},{"key":"e_1_2_11_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2021.110236"},{"key":"e_1_2_11_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107386"},{"key":"e_1_2_11_30_2","doi-asserted-by":"crossref","unstructured":"HeK. ZhangX. RenS. andSunJ. Deep residual learning for image recognition Proceedings of the IEEE conference on computer vision and pattern recognition June 2016 Las Vegas NV USA https:\/\/doi.org\/10.1109\/CVPR.2016.90 2-s2.0-84986274465.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_11_31_2","doi-asserted-by":"crossref","unstructured":"YuZ. HuangH. TangY. WuD. andBaoY. Remaining useful life prediction via multi-neural network integration Proceedings of the 2021 China Automation Congress (CAC) October 2021 Beijing China IEEE https:\/\/doi.org\/10.1109\/CAC53003.2021.9728490.","DOI":"10.1109\/CAC53003.2021.9728490"},{"key":"e_1_2_11_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2943898"},{"key":"e_1_2_11_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2022.112282"},{"key":"e_1_2_11_34_2","doi-asserted-by":"publisher","DOI":"10.4236\/jcc.2023.115012"},{"key":"e_1_2_11_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.107038"},{"key":"e_1_2_11_36_2","doi-asserted-by":"publisher","DOI":"10.3934\/era.2023135"},{"key":"e_1_2_11_37_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.saa.2021.120187"},{"key":"e_1_2_11_38_2","doi-asserted-by":"publisher","DOI":"10.5937\/FME2103684M"},{"key":"e_1_2_11_39_2","unstructured":"NectouxP. GouriveauR. MedjaherK. RamassoE. Chebel-MorelloB. ZerhouniN. andVarnierC. PRONOSTIA: an experimental platform for bearings accelerated degradation tests Proceedings of the IEEE International Conference on Prognostics and Health Management PHM\u203212 June 2012 Denver CO USA."},{"key":"e_1_2_11_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2015.2427891"},{"key":"e_1_2_11_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2021.107813"},{"key":"e_1_2_11_42_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.mlwa.2022.100425"},{"key":"e_1_2_11_43_2","doi-asserted-by":"publisher","DOI":"10.11857\/j.issn.1674-5124.2020050152"}],"container-title":["International Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/ijis\/2023\/6665534.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/ijis\/2023\/6665534.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2023\/6665534","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T05:40:14Z","timestamp":1735623614000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2023\/6665534"}},"subtitle":[],"editor":[{"given":"Vasudevan","family":"Rajamohan","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2023,1]]},"references-count":43,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1]]}},"alternative-id":["10.1155\/2023\/6665534"],"URL":"https:\/\/doi.org\/10.1155\/2023\/6665534","archive":["Portico"],"relation":{},"ISSN":["0884-8173","1098-111X"],"issn-type":[{"value":"0884-8173","type":"print"},{"value":"1098-111X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1]]},"assertion":[{"value":"2023-07-19","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-11-06","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-11-17","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"6665534"}}