A novel fault early warning method for centrifugal blowers based on stacked denoising autoencoder and transfer learning
Centrifugal blowers are easy to get faults due to the harsh working environment, and appropriate fault early warning is of great significance for predictive maintenance. Traditional fault early warning methods have poor resistance and feature learning ability in dealing with multivariate data with n...
Saved in:
| Published in: | Journal of manufacturing systems Vol. 76; pp. 443 - 456 |
|---|---|
| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
01.10.2024
|
| Subjects: | |
| ISSN: | 0278-6125 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Centrifugal blowers are easy to get faults due to the harsh working environment, and appropriate fault early warning is of great significance for predictive maintenance. Traditional fault early warning methods have poor resistance and feature learning ability in dealing with multivariate data with noise, and cannot achieve domain adaptation in different working environments. Aimed at solving these problems, this paper proposes a novel fault early warning method for centrifugal blowers based on stacked denoising autoencoder with sliding window (SW-SDAE) and transfer learning. The developed SW-SDAE model can effectively learn representative degradation features and temporal dependence from multivariate time-series data with noise. The reconstruction errors of SW-SDAE are used to construct the health indicators, which accurately characterizes the health status of the centrifugal blower. Meanwhile, transfer learning is employed to solve the problem of domain adaptation for different working environments. The established source domain warning model is successfully transferred to the target domain by minimizing the maximum mean discrepancy. When the health indicator exceeds the warning threshold, a fault early warning is performed. Experimental results demonstrate that the developed SW-SDAE warning model integrating transfer learning significantly resists the interference of noise and improves the domain adaptability for different working conditions. The proposed method achieves fault early warning 5.67 h without false alarms before failure and shows superior warning performance compared with traditional warning methods.
•A stacked denoising autoencoder model with sliding window is developed to construct health indicators and achieve fault early warning.•A transfer learning method for solving domain adaption is employed in the fault early warning.•The fault warning model fully learns representative degradation features and temporal dependencies from multivariate time-series data with noise.•The proposed method significantly improves the domain adaptability for different working conditions and shows superior warning performance. |
|---|---|
| AbstractList | Centrifugal blowers are easy to get faults due to the harsh working environment, and appropriate fault early warning is of great significance for predictive maintenance. Traditional fault early warning methods have poor resistance and feature learning ability in dealing with multivariate data with noise, and cannot achieve domain adaptation in different working environments. Aimed at solving these problems, this paper proposes a novel fault early warning method for centrifugal blowers based on stacked denoising autoencoder with sliding window (SW-SDAE) and transfer learning. The developed SW-SDAE model can effectively learn representative degradation features and temporal dependence from multivariate time-series data with noise. The reconstruction errors of SW-SDAE are used to construct the health indicators, which accurately characterizes the health status of the centrifugal blower. Meanwhile, transfer learning is employed to solve the problem of domain adaptation for different working environments. The established source domain warning model is successfully transferred to the target domain by minimizing the maximum mean discrepancy. When the health indicator exceeds the warning threshold, a fault early warning is performed. Experimental results demonstrate that the developed SW-SDAE warning model integrating transfer learning significantly resists the interference of noise and improves the domain adaptability for different working conditions. The proposed method achieves fault early warning 5.67 h without false alarms before failure and shows superior warning performance compared with traditional warning methods.
•A stacked denoising autoencoder model with sliding window is developed to construct health indicators and achieve fault early warning.•A transfer learning method for solving domain adaption is employed in the fault early warning.•The fault warning model fully learns representative degradation features and temporal dependencies from multivariate time-series data with noise.•The proposed method significantly improves the domain adaptability for different working conditions and shows superior warning performance. |
| Author | Zhang, You Zhou, Feng Li, Congbo Zhang, Xu Tang, Ying |
| Author_xml | – sequence: 1 givenname: You surname: Zhang fullname: Zhang, You organization: State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing 400044, China – sequence: 2 givenname: Congbo surname: Li fullname: Li, Congbo email: congboli@cqu.edu.cn organization: State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing 400044, China – sequence: 3 givenname: Ying surname: Tang fullname: Tang, Ying organization: Department of Electrical and Computer Engineering, Rowan University, Glassboro, NJ 08028, USA – sequence: 4 givenname: Xu surname: Zhang fullname: Zhang, Xu organization: State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing 400044, China – sequence: 5 givenname: Feng surname: Zhou fullname: Zhou, Feng organization: State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing 400044, China |
| BookMark | eNp9kMtOwzAQRb0oEi3wA6z8AwljJ81DYlNVvKRKbGBtOfa4OKQ2st1W_XsSlRWLrmZGmnOlexZk5rxDQu4Z5AxY9dDn_S6ecg68zKHJgRUzMgdeN1nF-PKaLGLsARgvgc_JcUWdP-BAjdwPiaIMw4keZXDWbekO05fX1PhAFboUrNlv5UC7wR8xRNrJiJp6R2OS6ntcNTpv40TKffLolNcYqHSapiBdNOMx4Dn7llwZOUS8-5s35PP56WP9mm3eX97Wq02mCoCUKVhWUEhTK9aZrq04N2VX6bZsUUssoW1Ba4awVONbA7XmhndlgV1XS11gW9yQ5pyrgo8xoBHKJpmsH-tIOwgGYpImejFJE5M0AY0YpY0o_4f-BLuT4XQZejxDOJY6WAwiKjuaQG0DqiS0t5fwX7Dxjic |
| CitedBy_id | crossref_primary_10_1016_j_ymssp_2025_113282 crossref_primary_10_1016_j_rcim_2024_102943 crossref_primary_10_1016_j_hspr_2025_08_003 crossref_primary_10_1016_j_knosys_2025_113275 |
| Cites_doi | 10.1016/j.ymssp.2023.110109 10.1016/j.eswa.2023.120860 10.1109/TCYB.2020.3027549 10.1016/j.ress.2023.109608 10.1109/TCST.2020.2993068 10.1016/j.ymssp.2023.110239 10.1016/j.jmsy.2023.10.010 10.1016/j.eswa.2023.122215 10.1016/j.measurement.2019.06.029 10.1016/j.ress.2023.109740 10.1016/j.jmsy.2023.07.012 10.1109/TMECH.2017.2759301 10.1016/j.jmsy.2023.03.006 10.1109/TFUZZ.2020.3043673 10.1016/j.measurement.2023.113224 10.1109/TASE.2019.2913628 10.1007/s10845-023-02074-8 10.1016/j.measurement.2020.107570 10.1016/j.measurement.2023.112774 10.1016/j.eswa.2023.120002 10.1109/TIM.2020.2967113 10.1016/j.measurement.2022.110979 10.1109/TMECH.2020.3025615 10.1109/TSMC.2022.3180938 10.1016/j.ymssp.2023.110472 10.1109/TIM.2023.3323967 10.1109/TIE.2014.2301773 10.1109/TIE.2023.3234128 10.1109/TII.2009.2032654 10.1016/j.measurement.2021.109970 10.1016/j.ymssp.2020.107327 10.1109/TFUZZ.2022.3193456 10.1016/j.jmsy.2023.11.004 10.1109/TII.2020.2976752 10.1109/TASE.2020.2983061 10.1109/TIE.2019.2958297 10.1109/TSTE.2020.2989220 10.1109/TIE.2020.2988229 10.1016/j.ymssp.2018.12.051 |
| ContentType | Journal Article |
| Copyright | 2024 |
| Copyright_xml | – notice: 2024 |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.jmsy.2024.08.013 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EndPage | 456 |
| ExternalDocumentID | 10_1016_j_jmsy_2024_08_013 S0278612524001754 |
| GroupedDBID | --K --M -~X .DC .~1 0R~ 1B1 1~. 1~5 29K 3EH 3V. 4.4 457 4G. 5GY 5VS 7-5 71M 7WY 883 88I 8AO 8FE 8FG 8FL 8FW 8G5 8P~ 8R4 8R5 9JN 9M8 AACTN AAEDT AAEDW AAIKC AAIKJ AAKOC AALRI AAMNW AAOAW AAQFI AAQXK AAXKI AAXUO ABFNM ABJCF ABJNI ABMAC ABUWG ABXDB ACDAQ ACGFO ACGFS ACGOD ACIWK ACNNM ACRLP ADBBV ADEZE ADMUD ADTZH AEBSH AECPX AEKER AENEX AFFNX AFJKZ AFKRA AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AIEXJ AIKHN AITUG AJOXV AKRWK ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ARAPS ASPBG AVWKF AXJTR AZFZN AZQEC BENPR BEZIV BGLVJ BJAXD BKOJK BKOMP BLXMC BPHCQ C1A CCPQU CS3 D-I DU5 DWQXO E3Z EBS EFJIC EJD EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FRNLG FYGXN G-2 GBLVA GNUQQ GROUPED_ABI_INFORM_COMPLETE GROUPED_ABI_INFORM_RESEARCH GUQSH HCIFZ HVGLF HZ~ H~9 IHE J1W JJJVA K60 K6V K6~ K7- KOM L6V LY7 M0C M0F M0N M2O M2P M41 M7S MO0 MS~ N9A O-L O9- OAUVE OZT P-8 P-9 P2P P62 PC. PQBIZ PQBZA PQQKQ PRG PROAC PTHSS Q2X Q38 R2- RIG ROL RPZ RWL S0X SDF SES SET SPC SPCBC SST SSZ T5K TAE TN5 U5U WH7 WUQ ZHY ~G- 9DU AATTM AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFFHD AFPUW AGQPQ AIGII AIIUN AKBMS AKYEP ANKPU APXCP CITATION EFKBS EFLBG PHGZM PHGZT PQGLB ~HD |
| ID | FETCH-LOGICAL-c300t-c05603af7c1bfb9622f4b6d949edae40990dd1e05c603807d2f2b43ebb7ad3e93 |
| ISICitedReferencesCount | 3 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001300179100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0278-6125 |
| IngestDate | Sat Nov 29 03:35:05 EST 2025 Tue Nov 18 21:58:12 EST 2025 Sat Oct 19 15:53:54 EDT 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Centrifugal blowers Stacked denoising autoencoder Transfer learning Fault early warning |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c300t-c05603af7c1bfb9622f4b6d949edae40990dd1e05c603807d2f2b43ebb7ad3e93 |
| PageCount | 14 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_jmsy_2024_08_013 crossref_primary_10_1016_j_jmsy_2024_08_013 elsevier_sciencedirect_doi_10_1016_j_jmsy_2024_08_013 |
| PublicationCentury | 2000 |
| PublicationDate | October 2024 2024-10-00 |
| PublicationDateYYYYMMDD | 2024-10-01 |
| PublicationDate_xml | – month: 10 year: 2024 text: October 2024 |
| PublicationDecade | 2020 |
| PublicationTitle | Journal of manufacturing systems |
| PublicationYear | 2024 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Zheng, Luan, Shardt (bib15) 2024; 241 Li, Dai, Zhu (bib12) 2023; 218 Kim, Yang, Ko (bib20) 2023; 68 Wu, Zhang, Cheng (bib23) 2021; 149 Deng, Deng, Miao (bib22) 2024; 242 Fu, Xue, Wu (bib36) 2022; 52 Fan, Hsu, Tsai (bib8) 2020; 17 Gienger, Wagner, Bohm (bib7) 2021; 29 Huang, Zhang, Qin (bib18) 2024; 72 Yin, Ding, Xie (bib40) 2014; 61 Chen, Ma, Hu (bib27) 2023; 214 Yang, Lei, Jia (bib34) 2019; 122 Li, Tang, Deng (bib35) 2020; 156 He, Wang, Zhang (bib14) 2022; 193 Langarica, Ruffelmacher, Nunez (bib17) 2020; 17 Brito, Susto, Brito (bib1) 2023; 232 Odiowei, Cao (bib38) 2010; 6 Chen, Fu, Zheng (bib3) 2023; 71 Jiang, Wang, Chen (bib9) 2024; 238 Li, Huang, He (bib29) 2021; 26 Ross, Sheeba, Shibi (bib31) 2024; 35 Chen, Huang, Chen (bib30) 2023; 193 Zhang, Li, Wang (bib19) 2021; 185 Sun, Huang, Mao (bib32) 2023; 72 Jin, Xu, Qiao (bib10) 2021; 12 Zhao, Ma (bib11) 2020; 69 Rao, Zuo, Tian (bib25) 2023; 189 Sun, Wang, Liu (bib39) 2019; 146 Li, Ding, Peng (bib6) 2022; 30 Min, Fang, Wu (bib33) 2023; 224 Wu, Wang, Zhang (bib24) 2023; 72 Wu, Zhao, Sun (bib26) 2020; 16 Xiao, Shao, Feng (bib2) 2023; 70 Yang, Fang (bib4) 2020; 67 Zuheros, Martínez-Cámara, Herrera-Viedma (bib21) 2023; 53 Jiang, Xie, He (bib16) 2018; 23 Shen, Hui, Yan (bib28) 2020; 67 Shiri, Zimroz, Wodecki (bib5) 2023; 200 Wei, Li (bib37) 2023; 70 Zhang, Yan, Wang (bib13) 2023; 31 Gienger (10.1016/j.jmsy.2024.08.013_bib7) 2021; 29 Langarica (10.1016/j.jmsy.2024.08.013_bib17) 2020; 17 Kim (10.1016/j.jmsy.2024.08.013_bib20) 2023; 68 Chen (10.1016/j.jmsy.2024.08.013_bib30) 2023; 193 Shiri (10.1016/j.jmsy.2024.08.013_bib5) 2023; 200 Huang (10.1016/j.jmsy.2024.08.013_bib18) 2024; 72 Wei (10.1016/j.jmsy.2024.08.013_bib37) 2023; 70 Wu (10.1016/j.jmsy.2024.08.013_bib24) 2023; 72 Chen (10.1016/j.jmsy.2024.08.013_bib27) 2023; 214 Zhang (10.1016/j.jmsy.2024.08.013_bib19) 2021; 185 Chen (10.1016/j.jmsy.2024.08.013_bib3) 2023; 71 Zhao (10.1016/j.jmsy.2024.08.013_bib11) 2020; 69 Li (10.1016/j.jmsy.2024.08.013_bib35) 2020; 156 Li (10.1016/j.jmsy.2024.08.013_bib12) 2023; 218 Jiang (10.1016/j.jmsy.2024.08.013_bib16) 2018; 23 Jiang (10.1016/j.jmsy.2024.08.013_bib9) 2024; 238 Jin (10.1016/j.jmsy.2024.08.013_bib10) 2021; 12 Xiao (10.1016/j.jmsy.2024.08.013_bib2) 2023; 70 Rao (10.1016/j.jmsy.2024.08.013_bib25) 2023; 189 Zhang (10.1016/j.jmsy.2024.08.013_bib13) 2023; 31 Yin (10.1016/j.jmsy.2024.08.013_bib40) 2014; 61 Li (10.1016/j.jmsy.2024.08.013_bib6) 2022; 30 Zheng (10.1016/j.jmsy.2024.08.013_bib15) 2024; 241 Sun (10.1016/j.jmsy.2024.08.013_bib39) 2019; 146 He (10.1016/j.jmsy.2024.08.013_bib14) 2022; 193 Min (10.1016/j.jmsy.2024.08.013_bib33) 2023; 224 Brito (10.1016/j.jmsy.2024.08.013_bib1) 2023; 232 Yang (10.1016/j.jmsy.2024.08.013_bib4) 2020; 67 Fu (10.1016/j.jmsy.2024.08.013_bib36) 2022; 52 Fan (10.1016/j.jmsy.2024.08.013_bib8) 2020; 17 Yang (10.1016/j.jmsy.2024.08.013_bib34) 2019; 122 Li (10.1016/j.jmsy.2024.08.013_bib29) 2021; 26 Shen (10.1016/j.jmsy.2024.08.013_bib28) 2020; 67 Zuheros (10.1016/j.jmsy.2024.08.013_bib21) 2023; 53 Odiowei (10.1016/j.jmsy.2024.08.013_bib38) 2010; 6 Deng (10.1016/j.jmsy.2024.08.013_bib22) 2024; 242 Ross (10.1016/j.jmsy.2024.08.013_bib31) 2024; 35 Wu (10.1016/j.jmsy.2024.08.013_bib23) 2021; 149 Sun (10.1016/j.jmsy.2024.08.013_bib32) 2023; 72 Wu (10.1016/j.jmsy.2024.08.013_bib26) 2020; 16 |
| References_xml | – volume: 17 start-page: 1925 year: 2020 end-page: 1936 ident: bib8 article-title: Data-driven approach for fault detection and diagnostic in semiconductor manufacturing publication-title: IEEE Trans Autom Sci Eng – volume: 242 year: 2024 ident: bib22 article-title: Semi-supervised ensemble fault diagnosis method based on adversarial decoupled auto-encoder with extremely limited labels publication-title: Reliab Eng Syst Saf – volume: 185 year: 2021 ident: bib19 article-title: A novel fault diagnosis method based on multi-level information fusion and hierarchical adaptive convolutional neural networks for centrifugal blowers publication-title: Measurement – volume: 72 start-page: 93 year: 2024 end-page: 103 ident: bib18 article-title: Interpretable real-time monitoring of pipeline weld crack leakage based on wavelet multi-kernel network publication-title: J Manuf Syst – volume: 23 start-page: 89 year: 2018 end-page: 100 ident: bib16 article-title: Wind turbine fault detection using a denoising autoencoder with temporal information publication-title: IEEE/ASME Trans Mech – volume: 68 start-page: 117 year: 2023 end-page: 129 ident: bib20 article-title: Deep learning-based data registration of melt-pool-monitoring images for laser powder bed fusion additive manufacturing publication-title: J Manuf Syst – volume: 72 start-page: 3534610 year: 2023 ident: bib24 article-title: Wind turbine blade breakage monitoring with mogrifier lstm autoencoder publication-title: IEEE Trans Instrum Meas – volume: 72 start-page: 3521712 year: 2023 ident: bib32 article-title: Multiscale margin disparity adversarial network transfer learning for fault diagnosis publication-title: IEEE Trans Instrum Meas – volume: 241 year: 2024 ident: bib15 article-title: Dynamic-controlled principal component analysis for fault detection and automatic recovery publication-title: Reliab Eng Syst Saf – volume: 149 year: 2021 ident: bib23 article-title: A hybrid classification autoencoder for semi-supervised fault diagnosis in rotating machinery publication-title: Mech Syst Signal Process – volume: 12 start-page: 202 year: 2021 end-page: 210 ident: bib10 article-title: Condition monitoring of wind turbine generators using Scada data analysis publication-title: IEEE Trans Sustain Energy – volume: 67 start-page: 8743 year: 2020 end-page: 8754 ident: bib28 article-title: A new penalty domain selection machine enabled transfer learning for gearbox fault recognition publication-title: IEEE Trans Ind Electron – volume: 218 year: 2023 ident: bib12 article-title: A novel fault early warning method for mechanical equipment based on improved mset and ccpr publication-title: Measurement – volume: 200 year: 2023 ident: bib5 article-title: Using long-term condition monitoring data with non-Gaussian noise for online diagnostics publication-title: Mech Syst Signal Process – volume: 53 start-page: 369 year: 2023 end-page: 379 ident: bib21 article-title: Crowd decision making: sparse representation guided by sentiment analysis for leveraging the wisdom of the crowd publication-title: IEEE Trans Syst Man Cybern-Syst – volume: 122 start-page: 692 year: 2019 end-page: 706 ident: bib34 article-title: An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings publication-title: Mech Syst Signal Process – volume: 31 start-page: 970 year: 2023 end-page: 981 ident: bib13 article-title: Asynchronous fault detection filter design for t-s fuzzy singular systems via dynamic event-triggered scheme publication-title: IEEE Trans Fuzzy Syst – volume: 238 year: 2024 ident: bib9 article-title: An orbit-based encoder-forecaster deep learning method for condition monitoring of large turbomachines publication-title: Expert Syst Appl – volume: 193 year: 2023 ident: bib30 article-title: Transfer learning algorithms for bearing remaining useful life prediction: a comprehensive review from an industrial application perspective publication-title: Mech Syst Signal Process – volume: 67 start-page: 10856 year: 2020 end-page: 10864 ident: bib4 article-title: A new nonlinear model-based fault detection method using Mann-Whitney test publication-title: IEEE Trans Ind Electron – volume: 214 year: 2023 ident: bib27 article-title: An effective fault diagnosis approach for bearing using stacked de-noising auto-encoder with structure adaptive adjustment publication-title: Measurement – volume: 6 start-page: 36 year: 2010 end-page: 45 ident: bib38 article-title: Nonlinear dynamic process monitoring using canonical variate analysis and kernel density estimations publication-title: IEEE Trans Ind Inform – volume: 232 year: 2023 ident: bib1 article-title: Fault diagnosis using explainable AI: a transfer learning-based approach for rotating machinery exploiting augmented synthetic data publication-title: Expert Syst Appl – volume: 193 year: 2022 ident: bib14 article-title: Anomaly detection and early warning via a novel multiblock-based method with applications to thermal power plants publication-title: Measurement – volume: 146 start-page: 305 year: 2019 end-page: 314 ident: bib39 article-title: A sparse stacked denoising autoencoder with optimized transfer learning applied to the fault diagnosis of rolling bearings publication-title: Measurement – volume: 70 start-page: 12851 year: 2023 end-page: 12859 ident: bib37 article-title: Spatiotemporal entropy for abnormality detection and localization of Li-ion battery packs publication-title: IEEE Trans Ind Electron – volume: 61 start-page: 6418 year: 2014 end-page: 6428 ident: bib40 article-title: A review on basic data-driven approaches for industrial process monitoring publication-title: IEEE Trans Ind Electron – volume: 30 start-page: 579 year: 2022 end-page: 590 ident: bib6 article-title: Optimal observer-based fault detection and estimation approaches for t-s fuzzy systems publication-title: IEEE Trans Fuzzy Syst – volume: 69 start-page: 6212 year: 2020 end-page: 6220 ident: bib11 article-title: From polynomial fitting to Kernel Ridge regression: A generalized difference filter for encoder signal analysis publication-title: IEEE Trans Instrum Meas – volume: 52 start-page: 5113 year: 2022 end-page: 5123 ident: bib36 article-title: A fault diagnosability evaluation method for dynamic systems without distribution knowledge publication-title: IEEE Trans Cybern – volume: 71 start-page: 581 year: 2023 end-page: 594 ident: bib3 article-title: The advance of digital twin for predictive maintenance: the role and function of machine learning publication-title: J Manuf Syst – volume: 26 start-page: 1591 year: 2021 end-page: 1601 ident: bib29 article-title: A two-stage transfer adversarial network for intelligent fault diagnosis of rotating machinery with multiple new faults publication-title: IEEE/ASME Trans Mech – volume: 17 start-page: 284 year: 2020 end-page: 295 ident: bib17 article-title: An industrial internet application for real-time fault diagnosis in industrial motors publication-title: IEEE Trans Autom Sci Eng – volume: 189 year: 2023 ident: bib25 article-title: A speed normalized autoencoder for rotating machinery fault detection under varying speed conditions publication-title: Mech Syst Signal Process – volume: 224 year: 2023 ident: bib33 article-title: A fault diagnosis framework for autonomous vehicles with sensor self-diagnosis publication-title: Expert Syst Appl – volume: 16 start-page: 7479 year: 2020 end-page: 7488 ident: bib26 article-title: Fault-attention generative probabilistic adversarial autoencoder for machine anomaly detection publication-title: IEEE Trans Ind Inform – volume: 29 start-page: 1131 year: 2021 end-page: 1146 ident: bib7 article-title: Robust fault diagnosis for adaptive structures with unknown stochastic disturbances publication-title: IEEE Trans Control Syst Technol – volume: 70 start-page: 186 year: 2023 end-page: 201 ident: bib2 article-title: Towards trustworthy rotating machinery fault diagnosis via attention uncertainty in transformer publication-title: J Manuf Syst – volume: 35 start-page: 757 year: 2024 end-page: 775 ident: bib31 article-title: A novel approach of tool condition monitoring in sustainable machining of Ni alloy with transfer learning models publication-title: J Intell Manuf – volume: 156 year: 2020 ident: bib35 article-title: Deep balanced domain adaptation neural networks for fault diagnosis of planetary gearboxes with limited labeled data publication-title: Measurement – volume: 189 year: 2023 ident: 10.1016/j.jmsy.2024.08.013_bib25 article-title: A speed normalized autoencoder for rotating machinery fault detection under varying speed conditions publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2023.110109 – volume: 232 year: 2023 ident: 10.1016/j.jmsy.2024.08.013_bib1 article-title: Fault diagnosis using explainable AI: a transfer learning-based approach for rotating machinery exploiting augmented synthetic data publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2023.120860 – volume: 52 start-page: 5113 issue: 6 year: 2022 ident: 10.1016/j.jmsy.2024.08.013_bib36 article-title: A fault diagnosability evaluation method for dynamic systems without distribution knowledge publication-title: IEEE Trans Cybern doi: 10.1109/TCYB.2020.3027549 – volume: 241 year: 2024 ident: 10.1016/j.jmsy.2024.08.013_bib15 article-title: Dynamic-controlled principal component analysis for fault detection and automatic recovery publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2023.109608 – volume: 29 start-page: 1131 issue: 3 year: 2021 ident: 10.1016/j.jmsy.2024.08.013_bib7 article-title: Robust fault diagnosis for adaptive structures with unknown stochastic disturbances publication-title: IEEE Trans Control Syst Technol doi: 10.1109/TCST.2020.2993068 – volume: 193 year: 2023 ident: 10.1016/j.jmsy.2024.08.013_bib30 article-title: Transfer learning algorithms for bearing remaining useful life prediction: a comprehensive review from an industrial application perspective publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2023.110239 – volume: 71 start-page: 581 year: 2023 ident: 10.1016/j.jmsy.2024.08.013_bib3 article-title: The advance of digital twin for predictive maintenance: the role and function of machine learning publication-title: J Manuf Syst doi: 10.1016/j.jmsy.2023.10.010 – volume: 238 year: 2024 ident: 10.1016/j.jmsy.2024.08.013_bib9 article-title: An orbit-based encoder-forecaster deep learning method for condition monitoring of large turbomachines publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2023.122215 – volume: 146 start-page: 305 year: 2019 ident: 10.1016/j.jmsy.2024.08.013_bib39 article-title: A sparse stacked denoising autoencoder with optimized transfer learning applied to the fault diagnosis of rolling bearings publication-title: Measurement doi: 10.1016/j.measurement.2019.06.029 – volume: 242 year: 2024 ident: 10.1016/j.jmsy.2024.08.013_bib22 article-title: Semi-supervised ensemble fault diagnosis method based on adversarial decoupled auto-encoder with extremely limited labels publication-title: Reliab Eng Syst Saf doi: 10.1016/j.ress.2023.109740 – volume: 70 start-page: 186 year: 2023 ident: 10.1016/j.jmsy.2024.08.013_bib2 article-title: Towards trustworthy rotating machinery fault diagnosis via attention uncertainty in transformer publication-title: J Manuf Syst doi: 10.1016/j.jmsy.2023.07.012 – volume: 23 start-page: 89 issue: 1 year: 2018 ident: 10.1016/j.jmsy.2024.08.013_bib16 article-title: Wind turbine fault detection using a denoising autoencoder with temporal information publication-title: IEEE/ASME Trans Mech doi: 10.1109/TMECH.2017.2759301 – volume: 68 start-page: 117 year: 2023 ident: 10.1016/j.jmsy.2024.08.013_bib20 article-title: Deep learning-based data registration of melt-pool-monitoring images for laser powder bed fusion additive manufacturing publication-title: J Manuf Syst doi: 10.1016/j.jmsy.2023.03.006 – volume: 30 start-page: 579 issue: 2 year: 2022 ident: 10.1016/j.jmsy.2024.08.013_bib6 article-title: Optimal observer-based fault detection and estimation approaches for t-s fuzzy systems publication-title: IEEE Trans Fuzzy Syst doi: 10.1109/TFUZZ.2020.3043673 – volume: 218 year: 2023 ident: 10.1016/j.jmsy.2024.08.013_bib12 article-title: A novel fault early warning method for mechanical equipment based on improved mset and ccpr publication-title: Measurement doi: 10.1016/j.measurement.2023.113224 – volume: 17 start-page: 284 issue: 1 year: 2020 ident: 10.1016/j.jmsy.2024.08.013_bib17 article-title: An industrial internet application for real-time fault diagnosis in industrial motors publication-title: IEEE Trans Autom Sci Eng doi: 10.1109/TASE.2019.2913628 – volume: 35 start-page: 757 issue: 2 year: 2024 ident: 10.1016/j.jmsy.2024.08.013_bib31 article-title: A novel approach of tool condition monitoring in sustainable machining of Ni alloy with transfer learning models publication-title: J Intell Manuf doi: 10.1007/s10845-023-02074-8 – volume: 156 year: 2020 ident: 10.1016/j.jmsy.2024.08.013_bib35 article-title: Deep balanced domain adaptation neural networks for fault diagnosis of planetary gearboxes with limited labeled data publication-title: Measurement doi: 10.1016/j.measurement.2020.107570 – volume: 214 year: 2023 ident: 10.1016/j.jmsy.2024.08.013_bib27 article-title: An effective fault diagnosis approach for bearing using stacked de-noising auto-encoder with structure adaptive adjustment publication-title: Measurement doi: 10.1016/j.measurement.2023.112774 – volume: 224 year: 2023 ident: 10.1016/j.jmsy.2024.08.013_bib33 article-title: A fault diagnosis framework for autonomous vehicles with sensor self-diagnosis publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2023.120002 – volume: 69 start-page: 6212 issue: 9 year: 2020 ident: 10.1016/j.jmsy.2024.08.013_bib11 article-title: From polynomial fitting to Kernel Ridge regression: A generalized difference filter for encoder signal analysis publication-title: IEEE Trans Instrum Meas doi: 10.1109/TIM.2020.2967113 – volume: 193 year: 2022 ident: 10.1016/j.jmsy.2024.08.013_bib14 article-title: Anomaly detection and early warning via a novel multiblock-based method with applications to thermal power plants publication-title: Measurement doi: 10.1016/j.measurement.2022.110979 – volume: 26 start-page: 1591 issue: 3 year: 2021 ident: 10.1016/j.jmsy.2024.08.013_bib29 article-title: A two-stage transfer adversarial network for intelligent fault diagnosis of rotating machinery with multiple new faults publication-title: IEEE/ASME Trans Mech doi: 10.1109/TMECH.2020.3025615 – volume: 53 start-page: 369 issue: 1 year: 2023 ident: 10.1016/j.jmsy.2024.08.013_bib21 article-title: Crowd decision making: sparse representation guided by sentiment analysis for leveraging the wisdom of the crowd publication-title: IEEE Trans Syst Man Cybern-Syst doi: 10.1109/TSMC.2022.3180938 – volume: 200 year: 2023 ident: 10.1016/j.jmsy.2024.08.013_bib5 article-title: Using long-term condition monitoring data with non-Gaussian noise for online diagnostics publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2023.110472 – volume: 72 start-page: 3534610 year: 2023 ident: 10.1016/j.jmsy.2024.08.013_bib24 article-title: Wind turbine blade breakage monitoring with mogrifier lstm autoencoder publication-title: IEEE Trans Instrum Meas doi: 10.1109/TIM.2023.3323967 – volume: 72 start-page: 3521712 year: 2023 ident: 10.1016/j.jmsy.2024.08.013_bib32 article-title: Multiscale margin disparity adversarial network transfer learning for fault diagnosis publication-title: IEEE Trans Instrum Meas – volume: 61 start-page: 6418 issue: 11 year: 2014 ident: 10.1016/j.jmsy.2024.08.013_bib40 article-title: A review on basic data-driven approaches for industrial process monitoring publication-title: IEEE Trans Ind Electron doi: 10.1109/TIE.2014.2301773 – volume: 70 start-page: 12851 issue: 12 year: 2023 ident: 10.1016/j.jmsy.2024.08.013_bib37 article-title: Spatiotemporal entropy for abnormality detection and localization of Li-ion battery packs publication-title: IEEE Trans Ind Electron doi: 10.1109/TIE.2023.3234128 – volume: 6 start-page: 36 issue: 1 year: 2010 ident: 10.1016/j.jmsy.2024.08.013_bib38 article-title: Nonlinear dynamic process monitoring using canonical variate analysis and kernel density estimations publication-title: IEEE Trans Ind Inform doi: 10.1109/TII.2009.2032654 – volume: 185 year: 2021 ident: 10.1016/j.jmsy.2024.08.013_bib19 article-title: A novel fault diagnosis method based on multi-level information fusion and hierarchical adaptive convolutional neural networks for centrifugal blowers publication-title: Measurement doi: 10.1016/j.measurement.2021.109970 – volume: 149 year: 2021 ident: 10.1016/j.jmsy.2024.08.013_bib23 article-title: A hybrid classification autoencoder for semi-supervised fault diagnosis in rotating machinery publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2020.107327 – volume: 31 start-page: 970 issue: 3 year: 2023 ident: 10.1016/j.jmsy.2024.08.013_bib13 article-title: Asynchronous fault detection filter design for t-s fuzzy singular systems via dynamic event-triggered scheme publication-title: IEEE Trans Fuzzy Syst doi: 10.1109/TFUZZ.2022.3193456 – volume: 72 start-page: 93 year: 2024 ident: 10.1016/j.jmsy.2024.08.013_bib18 article-title: Interpretable real-time monitoring of pipeline weld crack leakage based on wavelet multi-kernel network publication-title: J Manuf Syst doi: 10.1016/j.jmsy.2023.11.004 – volume: 16 start-page: 7479 issue: 12 year: 2020 ident: 10.1016/j.jmsy.2024.08.013_bib26 article-title: Fault-attention generative probabilistic adversarial autoencoder for machine anomaly detection publication-title: IEEE Trans Ind Inform doi: 10.1109/TII.2020.2976752 – volume: 17 start-page: 1925 issue: 4 year: 2020 ident: 10.1016/j.jmsy.2024.08.013_bib8 article-title: Data-driven approach for fault detection and diagnostic in semiconductor manufacturing publication-title: IEEE Trans Autom Sci Eng doi: 10.1109/TASE.2020.2983061 – volume: 67 start-page: 10856 issue: 12 year: 2020 ident: 10.1016/j.jmsy.2024.08.013_bib4 article-title: A new nonlinear model-based fault detection method using Mann-Whitney test publication-title: IEEE Trans Ind Electron doi: 10.1109/TIE.2019.2958297 – volume: 12 start-page: 202 issue: 1 year: 2021 ident: 10.1016/j.jmsy.2024.08.013_bib10 article-title: Condition monitoring of wind turbine generators using Scada data analysis publication-title: IEEE Trans Sustain Energy doi: 10.1109/TSTE.2020.2989220 – volume: 67 start-page: 8743 issue: 10 year: 2020 ident: 10.1016/j.jmsy.2024.08.013_bib28 article-title: A new penalty domain selection machine enabled transfer learning for gearbox fault recognition publication-title: IEEE Trans Ind Electron doi: 10.1109/TIE.2020.2988229 – volume: 122 start-page: 692 year: 2019 ident: 10.1016/j.jmsy.2024.08.013_bib34 article-title: An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2018.12.051 |
| SSID | ssj0012402 |
| Score | 2.3810422 |
| Snippet | Centrifugal blowers are easy to get faults due to the harsh working environment, and appropriate fault early warning is of great significance for predictive... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 443 |
| SubjectTerms | Centrifugal blowers Fault early warning Stacked denoising autoencoder Transfer learning |
| Title | A novel fault early warning method for centrifugal blowers based on stacked denoising autoencoder and transfer learning |
| URI | https://dx.doi.org/10.1016/j.jmsy.2024.08.013 |
| Volume | 76 |
| WOSCitedRecordID | wos001300179100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 issn: 0278-6125 databaseCode: AIEXJ dateStart: 19950101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: false ssIdentifier: ssj0012402 providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9swDBaydIftMOyJtXtAh90MF7Yk1_YxKDpsOxQD1gG5GZIlFwlSu2jitPsR-88lJdkR9ii2AbsYiSGFgfiZoqmPJCHvaqa4ZAW2bzcqFkInsdJ1FpcZ041IiyaV0jabyE9Pi_m8_DyZfB9yYbarvG2Lm5vy8r-qGu6BsjF19i_UPf4o3IDPoHS4gtrh-keKn0VttzWrqJH9ahMZW8D42sc_XL9oSy20rMxF08MOgex17JUW4Zam8fgAXEZ4unUERqlb2GiC7Dcd1rzE0hOWdGkdXvji206c_8bLvZBtj8kTLhtyHdRHD6PVYHNGZpClFxx37bnqdmEFP2yQE86d92HkgomRA-fDaUNKzY6_hFaPwWstul2hic5DGytcXSe_XQtXl_ynncAFJZaHy4v1t0OUbiu1pny3741sxC8oE0UinxbcKXGP7LE8K4sp2Zt9PJl_Go-l8CjKBu38f_RZWI4w-KOkX3s6gfdy9pg88gqhMweXJ2Ri2qfkYVCM8hm5nlELHGqBQy1wqAcOdcChABwaAId64FALHNq11AOHjsChAXAoAIcOwKEDcJ6Tr-9Pzo4_xL4tR1zzJNnENfjMCZdNXqeqUeURY41QR7oUpdHSCDxp1To1SVbDsCLJNWuYEtwolUvNTclfkGnbteYloWXKUyOTVCWmEEoKKUWmc8Wxm1qdJ2afpMMSVrWvWY-tU1bVQE5cVrjsFS57hf1UU75PonHOpavYcufobNBM5X1O50tWAKQ75h3847xX5MHuUXhNppur3rwh9-vtZrG-euvxdgsi4605 |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+novel+fault+early+warning+method+for+centrifugal+blowers+based+on+stacked+denoising+autoencoder+and+transfer+learning&rft.jtitle=Journal+of+manufacturing+systems&rft.au=Zhang%2C+You&rft.au=Li%2C+Congbo&rft.au=Tang%2C+Ying&rft.au=Zhang%2C+Xu&rft.date=2024-10-01&rft.pub=Elsevier+Ltd&rft.issn=0278-6125&rft.volume=76&rft.spage=443&rft.epage=456&rft_id=info:doi/10.1016%2Fj.jmsy.2024.08.013&rft.externalDocID=S0278612524001754 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0278-6125&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0278-6125&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0278-6125&client=summon |