{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:24:08Z","timestamp":1740122648406,"version":"3.37.3"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T00:00:00Z","timestamp":1732492800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T00:00:00Z","timestamp":1732492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62006028"],"award-info":[{"award-number":["62006028"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003819","name":"Natural Science Foundation of Hubei Province","doi-asserted-by":"publisher","award":["2023AFB909"],"award-info":[{"award-number":["2023AFB909"]}],"id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s10489-024-05868-2","type":"journal-article","created":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T13:10:44Z","timestamp":1732540244000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Intelligent fault diagnosis of automobile main reducer based onstacked convolutional auto-encoder and parallel attention-based convolutional blocks"],"prefix":"10.1007","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8706-006X","authenticated-orcid":false,"given":"Qing","family":"Ye","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changhua","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,25]]},"reference":[{"issue":"5","key":"5868_CR1","doi-asserted-by":"crossref","first-page":"1756","DOI":"10.1002\/qre.3308","volume":"39","author":"GJ Jiang","year":"2023","unstructured":"Jiang GJ, Yang JS, Cheng TC, Sun HH (2023) Remaining useful life prediction of rolling bearings based on bayesian neural network and uncertainty quantification. Qual Reliab Eng Int 39(5):1756\u20131774","journal-title":"Qual Reliab Eng Int"},{"key":"5868_CR2","volume-title":"An unsupervised deep feature learning model based on parallel convolutional autoencoder for intelligent fault diagnosis of main reducer[J]","author":"Q Ye","year":"2021","unstructured":"Ye Q, Liu CH et al (2021) An unsupervised deep feature learning model based on parallel convolutional autoencoder for intelligent fault diagnosis of main reducer[J]. Computational Intelligence and Neuroscience"},{"key":"5868_CR3","doi-asserted-by":"crossref","first-page":"109208","DOI":"10.1016\/j.measurement.2021.109208","volume":"176","author":"T Han","year":"2021","unstructured":"Han T, Ma RY, Zheng JG (2021) Combination bidirectional long short-term memory and capsule network for rotating machinery fault diagnosis. Measurement 176:109208","journal-title":"Measurement"},{"key":"5868_CR4","doi-asserted-by":"crossref","unstructured":"Ye Q, Liu SH, Liu CH (2020) A deep learning model for fault diagnosis with a deep neural network and feature fusion on multi-channel. Sens Signals[J] Sens 20(15)","DOI":"10.3390\/s20154300"},{"key":"5868_CR5","doi-asserted-by":"crossref","first-page":"108071","DOI":"10.1016\/j.engappai.2024.108071","volume":"133","author":"J Guo","year":"2024","unstructured":"Guo J, Yang Y, Li H, Dai L, Huang B (2024) A parallel deep neural network for intelligent fault diagnosis of drilling pumps. Eng Appl Artif Intell 133:108071","journal-title":"Eng Appl Artif Intell"},{"key":"5868_CR6","doi-asserted-by":"crossref","first-page":"112085","DOI":"10.1109\/ACCESS.2023.3322733","volume":"11","author":"MY Wu","year":"2023","unstructured":"Wu MY, Ye Q, Mu JX et al (2023) Remaining useful life Prediction via adata-driven deep learning fusion model\u2013CALAP[J]. IEEE Access 11:112085\u2013112096","journal-title":"IEEE Access"},{"key":"5868_CR7","doi-asserted-by":"crossref","first-page":"121982","DOI":"10.1016\/j.eswa.2023.121982","volume":"238","author":"FA Zhou","year":"2024","unstructured":"Zhou FA, Liu S, Fujita H et al (2024) Fault diagnosis based on federated learning driven by dynamic expansion for model layers of imbalanced client. Expert Syst Appl 238:121982","journal-title":"Expert Syst Appl"},{"key":"5868_CR8","doi-asserted-by":"crossref","first-page":"106587","DOI":"10.1016\/j.ymssp.2019.106587","volume":"138","author":"YG Lei","year":"2020","unstructured":"Lei YG, Yang B, Jiang XW et al (2020) Applications of machine learning to machine fault diagnosis: a review and roadmap. Mech Syst Signal Process 138:106587","journal-title":"Mech Syst Signal Process"},{"issue":"3","key":"5868_CR9","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1108\/SR-02-2021-0052","volume":"41","author":"RF Ribeiro","year":"2021","unstructured":"Ribeiro RF, Areias IAND, Gomes GF (2021) Fault detection and diagnosis using vibration signalanalysis in frequency domain for electric motors considering different real fault types. Sens Rev 41(3):311\u2013319","journal-title":"Sens Rev"},{"issue":"4","key":"5868_CR10","volume":"22","author":"F He","year":"2022","unstructured":"He F, Ye Q (2022) A bearing fault diagnosis method based on wavelet packettransform and convolutional neural network optimized bySimulated annealing Algorithm. Sensors 22(4):1410","journal-title":"Sensors"},{"key":"5868_CR11","doi-asserted-by":"crossref","unstructured":"Zhang HR, Sun JX, Hou KN et al (2022) Improved information entropy weighted vague support vector machine method for transformer fault diagnosis. High Voltage 7(3):510\u2013522","DOI":"10.1049\/hve2.12095"},{"issue":"4","key":"5868_CR12","doi-asserted-by":"crossref","first-page":"2100402","DOI":"10.1002\/adts.202100402","volume":"5","author":"X Xie","year":"2022","unstructured":"Xie X, Xiong GJ, Chen J et al (2022) Universal transparent artificial neural network-based fault section diagnosis models for power systems. Adv Theory Simul 5(4):2100402","journal-title":"Adv Theory Simulations"},{"key":"5868_CR13","doi-asserted-by":"crossref","unstructured":"Huang GB, Zhu Q (2006) Extreme learning machine theory and applications. Neurocomputing 70(1\u20133):489\u2013501","DOI":"10.1016\/j.neucom.2005.12.126"},{"issue":"9","key":"5868_CR14","doi-asserted-by":"crossref","first-page":"8475","DOI":"10.1007\/s13369-021-05527-5","volume":"46","author":"N Sikder","year":"2021","unstructured":"Sikder N, Arif AM, Manjurul I et al (2021) Induction motor bearing fault classification using extreme learning machine based on power features. Arab J Sci Eng 46(9):8475\u20138491","journal-title":"Arab J Sci Eng"},{"issue":"8","key":"5868_CR15","doi-asserted-by":"crossref","first-page":"4291","DOI":"10.1002\/qre.3208","volume":"38","author":"Z Jiang","year":"2022","unstructured":"Jiang Z, Zhou J, Ma YZ (2022) Fault diagnosis for rolling bearing based on parameter transfer bayesian network[J]. Qual Reliab Eng Int 38(8):4291\u20134308","journal-title":"Qual Reliab Eng Int"},{"issue":"19","key":"5868_CR16","doi-asserted-by":"crossref","first-page":"22834","DOI":"10.1007\/s10489-023-04753-8","volume":"53","author":"ZQ Zhang","year":"2023","unstructured":"Zhang ZQ, ,Zhou FN, Zhang CS et al (2023) A personalized federated learning-based fault diagnosis method for data suffering from network attacks. Appl Intell 53(19):22834\u201322849","journal-title":"Appl Intell"},{"issue":"1","key":"5868_CR17","doi-asserted-by":"crossref","first-page":"015011","DOI":"10.1088\/1361-6501\/acfba0","volume":"35","author":"B Gong","year":"2024","unstructured":"Gong B, An AM, Shi YK (2024) Photovoltaic arrays fault diagnosis based on an improved dilated convolutional neural network with feature-enhancement. Meas Sci Technol 35(1):015011","journal-title":"Meas Sci Technol"},{"key":"5868_CR18","volume-title":"Bearing Fault Diagnosis based on Convolution Neural Network with Logistic Chaotic Map[J]","author":"FF Zhang","year":"2024","unstructured":"Zhang FF, Chen LB, Dai YY et al (2024) Bearing Fault diagnosis based on convolution neural network with logistic chaotic map. Adv Theory Simul 7(5):2301090"},{"issue":"6","key":"5868_CR19","doi-asserted-by":"crossref","first-page":"5081","DOI":"10.1109\/TIE.2019.2931255","volume":"67","author":"WK Yu","year":"2020","unstructured":"Yu WK, Zhao CH (2020) Broad convolutional neural network based industrialprocess fault diagnosis with incremental learning capability. IEEE TransInd Electron 67(6):5081\u20135091","journal-title":"IEEE TransInd Electron"},{"issue":"3","key":"5868_CR20","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1049\/cit2.12170","volume":"8","author":"CJ Qin","year":"2023","unstructured":"Qin CJ, Jin YR, Zhang ZN et al (2023) Anti-noise diesel engine misfire diagnosis using a multi-scale CNN-LSTM neural network with denoising module. CAAI Trans Intell Technol 8(3):963\u2013986","journal-title":"CAAI Trans Intell Technol"},{"key":"5868_CR21","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.neucom.2020.04.143","volume":"405","author":"S Plakias","year":"2020","unstructured":"Plakias S, Boutalis YS (2020) Fault detection and identification of rollingelement bearings with attentive dense CNN. Neurocomputing 405:208\u2013217","journal-title":"Neurocomputing"},{"issue":"3","key":"5868_CR22","doi-asserted-by":"crossref","first-page":"3353","DOI":"10.1109\/TIA.2022.3159617","volume":"58","author":"YX Wang","year":"2022","unstructured":"Wang YX, Yan J, Ye X et al (2022) Few-shot transfer learning with attention mechanism for high-voltage circuit breaker fault diagnosis. IEEE Trans Ind Appl 58(3):3353\u20133360","journal-title":"IEEE Trans Ind Appl"},{"issue":"6","key":"5868_CR23","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.3390\/jmse11061121","volume":"11","author":"YM Chen","year":"2023","unstructured":"Chen YM, Wang YZ, Yu Y et al (2023) A fault diagnosis method for the autonomous underwater vehicle via meta-self-attention multi-scale CNN. J Mar Sci Eng 11(6):1121","journal-title":"J Mar Sci Eng"},{"issue":"7","key":"5868_CR24","doi-asserted-by":"crossref","first-page":"075020","DOI":"10.1063\/5.0095530","volume":"12","author":"ZK Xing","year":"2022","unstructured":"Xing ZK, Liu YB, Wang Q et al (2022) Multi-sensor signals with parallel attention convolutional neural network for bearing fault diagnosis. AIP Adv 12(7):075020","journal-title":"AIP Adv"},{"key":"5868_CR25","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.sigpro.2019.03.019","volume":"161","author":"X Li","year":"2019","unstructured":"Li X, Zhang W, Ding Q (2019) Understanding and improving deep learning-based rolling bearing fault diagnosis with attention mechanism. Signal Process 161:136\u2013154","journal-title":"Signal Process"},{"key":"5868_CR26","first-page":"3512210","volume":"70","author":"CD Liu","year":"2021","unstructured":"Liu CD, Zhang LX, Yao R et al (2021) Dual attention-based temporalconvolutional network for fault prognosis under time-time-varying operatingconditions. IEEE Trans InstrumMeas 70:3512210","journal-title":"IEEE Trans InstrumMeas"},{"key":"5868_CR27","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.measurement.2019.05.049","volume":"144","author":"SN Chegini","year":"2019","unstructured":"Chegini SN, Bagheri A, Najafi F (2019) Application of a new EWT-based denoising technique in bearing fault diagnosis. Measurement 144:275\u2013297","journal-title":"Measurement"},{"key":"5868_CR28","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.108783","volume":"130","author":"PS Dinesh","year":"2022","unstructured":"Dinesh PS, Manikandan M (2022) Fully convolutional deep stacked denoising sparse auto encoder network for partial face reconstruction. Pattern Recogn 130:108783","journal-title":"Pattern Recogn"},{"key":"5868_CR29","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.knosys.2019.04.022","volume":"178","author":"JB Yu","year":"2019","unstructured":"Yu JB (2019) Evolutionary manifold regularized stacked denoisingautoencoders for gearbox fault diagnosis. Knowl Based Syst 178:111\u2013122","journal-title":"Knowl Based Syst"},{"issue":"17","key":"5868_CR30","volume":"10","author":"K Gu","year":"2021","unstructured":"Gu K, Zhang Y, Liu X et al (2021) DWT-LSTM-based fault diagnosis of rolling bearings with multi-sensors. Electronics 10(17):2076","journal-title":"Electronics"},{"issue":"4","key":"5868_CR31","doi-asserted-by":"crossref","first-page":"4263","DOI":"10.1007\/s40747-022-00925-0","volume":"9","author":"YM Wang","year":"2023","unstructured":"Wang YM, Cao GQ (2023) A multiscale convolution neural network for bearing fault diagnosis based on frequency division denoising under complex noise conditions. Complex Intell Syst 9(4):4263\u20134285","journal-title":"Complex Intell Syst"},{"key":"5868_CR32","first-page":"8058723","volume":"2020","author":"FY Guo","year":"2020","unstructured":"Guo FY, Zhang YC, Wang Y et al (2020) Fault detectionof reciprocating compressor valve based on one-dimensional convolutional neural network. Math Prob Engi 2020:8058723","journal-title":"Math Prob Engi"},{"issue":"22","key":"5868_CR33","doi-asserted-by":"crossref","first-page":"6465","DOI":"10.3390\/s20226465","volume":"20","author":"Q Song","year":"2020","unstructured":"Song Q, Zhao SF, Wang MS (2020) On the accuracy of fault diagnosis for rolling element bearings using improved DFA and multi-sensor data fusion method. Sensors 20(22):6465","journal-title":"Sensors"},{"issue":"8","key":"5868_CR34","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1007\/s11265-023-01846-y","volume":"95","author":"ZQ Zhao","year":"2023","unstructured":"Zhao ZQ, Jiao YH, Zhang X (2023) A Fault diagnosis method of rotor system based on parallel convolutional neural network architecture with attention mechanism. J Signal Process Syst 95(8):965\u2013977","journal-title":"J Signal Process Syst"},{"issue":"5","key":"5868_CR35","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s40430-023-04139-4","volume":"45","author":"XY Zhong","year":"2023","unstructured":"Zhong XY, Li YF, Xia TY (2023) Parallel learning attention-guided CNN for signal denoising and mechanical fault diagnosis. J Brazi Soc Mech Sci Eng 45(5):239","journal-title":"J Brazi Soc Mech Sci Eng"},{"issue":"18","key":"5868_CR36","doi-asserted-by":"crossref","first-page":"21312","DOI":"10.1007\/s10489-023-04701-6","volume":"53","author":"AA Wei","year":"2023","unstructured":"Wei AA, Han SY, Li W et al (2023) A new framework for intelligent fault diagnosis of spiral bevel gears with unbalanced data. Appl Intell 53(18):21312\u201321324","journal-title":"Appl Intell"},{"issue":"7","key":"5868_CR37","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1049\/elp2.12063","volume":"15","author":"YL Karnavas","year":"2021","unstructured":"Karnavas YL, Plakias S, Chasiotis ID (2021) Extracting spatially global and local attentive features for rolling bearing fault diagnosis in electrical machines using attention stream networks. IET Electr Power Appl 15(7):903\u2013915","journal-title":"IET Electr Power Appl"},{"issue":"16","key":"5868_CR38","doi-asserted-by":"crossref","first-page":"14067","DOI":"10.1007\/s00521-022-07219-z","volume":"34","author":"K Rai","year":"2022","unstructured":"Rai K, Hojatpanah F, Ajaei FB et al (2022) Deep learning for high-impedance fault detection and classification: transformer-CNN. Neural Comput Appl 34(16):14067\u201314084","journal-title":"Neural Comput Appl"},{"issue":"3","key":"5868_CR39","doi-asserted-by":"crossref","first-page":"035108","DOI":"10.1088\/1361-6501\/abc6e3","volume":"32","author":"PL Wu","year":"2021","unstructured":"Wu PL, Nie XY, Xie G (2021) Multi-sensor signal fusion for a compound fault diagnosis method with strong generalization and noise-tolerant performance. Meas Sci Technol 32(3):035108","journal-title":"Meas Sci Technol"},{"key":"5868_CR40","doi-asserted-by":"crossref","first-page":"108679","DOI":"10.1016\/j.ymssp.2021.108679","volume":"168","author":"YL Ma","year":"2022","unstructured":"Ma YL, Cheng JS, Wang P et al (2022) A novel Lanczos quaternion singular spectrum analysis method and its application to bevel gear fault diagnosis with multi-channel signals. Mech Syst Signal Process 168:108679","journal-title":"Mech Syst Signal Process"},{"key":"5868_CR41","volume-title":"Intelligent fault diagnosis method of rolling bearing based on stacked denoisingautoencoder and convolutional neural network[J]","author":"C Che","year":"2020","unstructured":"Che C, Wang H, Ni X et al (2020) Intelligent fault diagnosis method of rolling bearing based on stacked denoisingautoencoder and convolutional neural network. Ind Lubr Tribol 72(7):947\u2013953"},{"key":"5868_CR42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TII.2019.2921431","volume":"2019","author":"C Sun","year":"2019","unstructured":"Sun C, Ma M, Zhao ZB et al (2019) Deep transfer learning based on sparse auto-encoder for remaining useful life prediction of tool in manufacturing. IEEE Trans Ind Informatics 2019:1\u201310","journal-title":"IEEE Trans Ind Informatics"},{"issue":"3","key":"5868_CR43","doi-asserted-by":"crossref","first-page":"2044","DOI":"10.1109\/TII.2019.2934901","volume":"16","author":"QW Guo","year":"2020","unstructured":"Guo QW, Li YB, Song Y et al (2020) Intelligent fault diagnosis method based on full 1-D convolutional generative adversarial network. IEEE Trans Industrial Inf 16(3):2044\u20132053","journal-title":"IEEE Trans Industrial Inf"},{"key":"5868_CR44","doi-asserted-by":"crossref","first-page":"29857","DOI":"10.1109\/ACCESS.2020.2972859","volume":"8","author":"S Zhang","year":"2020","unstructured":"Zhang S, Zhang SB, Wang BN et al (2020) Deep learning algorithms for bearing fault diagnostics\u2014a comprehensive review. IEEE Access 8:29857\u201329881","journal-title":"IEEE Access"},{"key":"5868_CR45","first-page":"7132","volume":"2018","author":"J Hu","year":"2018","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks[C]. Proc IEEE Conf Comput Vis Pattern Recognit 2018:7132\u20137141","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"5868_CR46","first-page":"2261","volume":"2017","author":"G Huang","year":"2017","unstructured":"Huang G, Liu Z, Maaten LVD et al (2017) Densely connected convolutional networks. Proc IEEE Conf Comput Vis Pattern Recognit 2017:2261\u20132269","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05868-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05868-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05868-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,2]],"date-time":"2025-01-02T15:09:05Z","timestamp":1735830545000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05868-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,25]]},"references-count":46,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["5868"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05868-2","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2024,11,25]]},"assertion":[{"value":"7 November 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"24"}}