Bayesian optimization and channel-fusion-based convolutional autoencoder network for fault diagnosis of rotating machinery
•A novel convolutional network based on Bayesian optimization and channel fusion mechanism is developed.•The proposed method is applied to extract the compressed features and reconstruct the input data.•The channel fusion mechanism is newly designed to merge features of different layers for more sta...
Gespeichert in:
| Veröffentlicht in: | Engineering structures Jg. 280; S. 115708 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier Ltd
01.04.2023
|
| Schlagworte: | |
| ISSN: | 0141-0296, 1873-7323 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | •A novel convolutional network based on Bayesian optimization and channel fusion mechanism is developed.•The proposed method is applied to extract the compressed features and reconstruct the input data.•The channel fusion mechanism is newly designed to merge features of different layers for more stable feature expression.•A hybrid loss function is creatively defined through summing the error values of mean square error and cross-entropy for model training.•The Bayesian optimization scheme is introduced to optimize the model’s hyper-parameters and obtain a powerful learning ability for fault diagnosis.•A laboratory bearing dataset and an industrial bearing dataset are both used to evaluate the performance of the proposed method on fault diagnosis.
Deep learning methods are essential for the application of data driven technologies on fault diagnosis of rotating machinery. However, the generalization and performance of deep learning methods for fault diagnosis are highly dependent on the selection of hyper parameters and the design of network structure. To solve aforementioned challenge in fault diagnosis and obtain a powerful model, a convolutional network based on Bayesian optimization and channel fusion mechanism is newly developed. In the proposed network, a convolutional autoencoder network is firstly applied to extract the compressed features and reconstruct the input data. Then, the channel fusion mechanism is introduced to deduce the error of reconstructed input data. Next, a hybrid loss function is defined through summing mean square error and cross-entropy. Finally, a Bayesian optimization scheme is designed to optimize the hyper parameters of the designed network. The effectiveness of the proposed method is verified by a laboratory bearing dataset and an industrial bearing dataset, respectively. Five classical fault diagnosis methods are also tested as comparison. The experimental results indicate the proposed method can achieve the outstanding performance in fault diagnosis both in accuracy and efficiency. |
|---|---|
| AbstractList | •A novel convolutional network based on Bayesian optimization and channel fusion mechanism is developed.•The proposed method is applied to extract the compressed features and reconstruct the input data.•The channel fusion mechanism is newly designed to merge features of different layers for more stable feature expression.•A hybrid loss function is creatively defined through summing the error values of mean square error and cross-entropy for model training.•The Bayesian optimization scheme is introduced to optimize the model’s hyper-parameters and obtain a powerful learning ability for fault diagnosis.•A laboratory bearing dataset and an industrial bearing dataset are both used to evaluate the performance of the proposed method on fault diagnosis.
Deep learning methods are essential for the application of data driven technologies on fault diagnosis of rotating machinery. However, the generalization and performance of deep learning methods for fault diagnosis are highly dependent on the selection of hyper parameters and the design of network structure. To solve aforementioned challenge in fault diagnosis and obtain a powerful model, a convolutional network based on Bayesian optimization and channel fusion mechanism is newly developed. In the proposed network, a convolutional autoencoder network is firstly applied to extract the compressed features and reconstruct the input data. Then, the channel fusion mechanism is introduced to deduce the error of reconstructed input data. Next, a hybrid loss function is defined through summing mean square error and cross-entropy. Finally, a Bayesian optimization scheme is designed to optimize the hyper parameters of the designed network. The effectiveness of the proposed method is verified by a laboratory bearing dataset and an industrial bearing dataset, respectively. Five classical fault diagnosis methods are also tested as comparison. The experimental results indicate the proposed method can achieve the outstanding performance in fault diagnosis both in accuracy and efficiency. |
| ArticleNumber | 115708 |
| Author | Zhou, A. Zou, L. Zhuang, K.J. Hu, J. |
| Author_xml | – sequence: 1 givenname: L. surname: Zou fullname: Zou, L. organization: School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, China – sequence: 2 givenname: K.J. surname: Zhuang fullname: Zhuang, K.J. organization: School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, China – sequence: 3 givenname: A. surname: Zhou fullname: Zhou, A. organization: School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen, China – sequence: 4 givenname: J. surname: Hu fullname: Hu, J. email: junhu22@whut.edu.cn organization: School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan, China |
| BookMark | eNqNkMtOwzAQRS1UJNrCN-AfSLGTJk4WLErFS6rEBtbRxB63Lqld2U5R-_WkFLFgA6uRruZc6Z4RGVhnkZBrziac8eJmPUG7DNF3Mk5SlmYTznPByjMy5KXIEpGl2YAMGZ_yhKVVcUFGIawZY2lZsiE53MEegwFL3TaajTlANM5SsIrKFViLbaK70EdJAwH70Nmda7vjE7QUuujQSqfQU4vxw_l3qp2nGro2UmVgaV0wgTpNvYt9tV3SDciVsej3l-RcQxvw6vuOydvD_ev8KVm8PD7PZ4tEZjyPiZRYoWhSpWSJUGqd5XkhVK6mSiktUIgqhUYjcqjKokh104DAAqaNAF6xJhuT21Ov9C4Ej7qWJn7NjB5MW3NWH0XW6_pHZH0UWZ9E9rz4xW-92YDf_4OcnUjs5-0M-jpI0_tCZTz2v8qZPzs-AVHDm84 |
| CitedBy_id | crossref_primary_10_1108_EC_10_2024_0937 crossref_primary_10_1080_10589759_2024_2328750 crossref_primary_10_3390_s24030890 crossref_primary_10_1016_j_marpetgeo_2024_107010 crossref_primary_10_3390_aerospace10100826 crossref_primary_10_1016_j_jpowsour_2024_235198 crossref_primary_10_1016_j_measurement_2023_113513 crossref_primary_10_1109_JSEN_2024_3422222 crossref_primary_10_1016_j_asoc_2024_111493 crossref_primary_10_1177_01423312241280994 crossref_primary_10_3390_jmse13091638 crossref_primary_10_1016_j_aei_2024_102573 crossref_primary_10_1016_j_mechmat_2024_105109 crossref_primary_10_3390_s25123577 crossref_primary_10_1088_1361_6501_ad0e9d crossref_primary_10_3390_electronics12132826 crossref_primary_10_1051_ijmqe_2024004 crossref_primary_10_1088_1361_6501_ad9857 crossref_primary_10_3390_electronics14173455 crossref_primary_10_1007_s11071_025_11722_y crossref_primary_10_1016_j_isatra_2023_04_033 crossref_primary_10_3390_machines12120838 crossref_primary_10_1016_j_oceaneng_2024_117392 crossref_primary_10_1177_14759217251316623 crossref_primary_10_1088_1361_6501_ad356e crossref_primary_10_1016_j_neucom_2025_129588 crossref_primary_10_1088_1361_6501_ad03b3 crossref_primary_10_1016_j_knosys_2023_110795 crossref_primary_10_1088_2631_8695_ade4f3 |
| Cites_doi | 10.1016/j.enconman.2019.111793 10.1109/TIE.2018.2864702 10.1109/ACCESS.2019.2953490 10.1016/j.measurement.2020.108839 10.1109/TIM.2021.3125975 10.1088/1361-665X/abc66b 10.3390/s20174965 10.1109/TIE.2020.2970677 10.1016/j.inffus.2021.03.008 10.1109/TTE.2021.3082146 10.1016/j.measurement.2021.109864 10.1016/j.measurement.2020.108636 10.1016/j.ymssp.2020.107174 10.1109/TIP.2010.2068555 10.1109/JESTPE.2020.3017923 10.1016/j.measurement.2020.107880 10.1109/TIE.2020.2994867 10.1016/j.knosys.2021.106796 10.1109/TIE.2020.2978690 10.1016/j.measurement.2020.108654 10.1016/j.measurement.2020.108469 10.1006/mssp.2002.1482 10.1177/00368504211026110 10.1016/j.anucene.2020.107934 10.1109/ACCESS.2021.3058577 10.1016/j.measurement.2020.108653 10.1109/TR.2018.2882682 10.1016/j.measurement.2021.109899 10.1016/j.ymssp.2021.107996 10.1016/j.neucom.2021.01.001 10.1109/TPEL.2021.3073774 10.1177/14759217221122266 10.1016/j.measurement.2021.109116 10.1016/j.jprocont.2021.01.009 |
| ContentType | Journal Article |
| Copyright | 2023 Elsevier Ltd |
| Copyright_xml | – notice: 2023 Elsevier Ltd |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.engstruct.2023.115708 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1873-7323 |
| ExternalDocumentID | 10_1016_j_engstruct_2023_115708 S0141029623001220 |
| GroupedDBID | --K --M -~X .~1 0R~ 1B1 1~. 1~5 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAXUO ABFNM ABJNI ABMAC ABQEM ABQYD ABYKQ ACDAQ ACGFS ACIWK ACLVX ACRLP ACSBN ADBBV ADEZE ADTZH AEBSH AECPX AEKER AENEX AFKWA AFRAH AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AIEXJ AIKHN AITUG AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ATOGT AXJTR BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EO8 EO9 EP2 EP3 FDB FIRID FNPLU FYGXN G-Q GBLVA IHE IMUCA J1W JJJVA KOM LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 RNS ROL RPZ SCC SDF SDG SDP SES SEW SPC SPCBC SSE SST SSZ T5K TN5 XPP ZMT ~02 ~G- 29G 9DU AAQXK AATTM AAXKI AAYWO AAYXX ABEFU ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADMUD ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AI. AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EJD FEDTE FGOYB G-2 HVGLF HZ~ R2- SET VH1 WUQ ZY4 ~HD |
| ID | FETCH-LOGICAL-c315t-cce9e7b2ddc8ea8ff35567d5d4dddf7e7792abfee1a98662fbba7e6a4b7a190b3 |
| ISICitedReferencesCount | 34 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000929756500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0141-0296 |
| IngestDate | Sat Nov 29 07:21:53 EST 2025 Tue Nov 18 22:51:14 EST 2025 Fri Feb 23 02:34:37 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Fault diagnosis Hybrid loss function Convolutional network Channel fusion mechanism Bayesian optimization |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c315t-cce9e7b2ddc8ea8ff35567d5d4dddf7e7792abfee1a98662fbba7e6a4b7a190b3 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_engstruct_2023_115708 crossref_primary_10_1016_j_engstruct_2023_115708 elsevier_sciencedirect_doi_10_1016_j_engstruct_2023_115708 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-04-01 2023-04-00 |
| PublicationDateYYYYMMDD | 2023-04-01 |
| PublicationDate_xml | – month: 04 year: 2023 text: 2023-04-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationTitle | Engineering structures |
| PublicationYear | 2023 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Shao, Lin, Zhang, Galar, Kumar (b0130) 2021; 74 Lee, Shin (b0160) 2021; 68 Snoek, Larochelle, Adams (b0190) 2012; 25 Zhang (b0015) 2021; 104 Wang, Zhao, Xiang, Li, Zhu (b0060) 2021; 183 Wang, Huang, Wang (b0100) 2021; 173 Ruan, Wang, Li, Qin, Tang, Wanga (b0010) 2021 Lee, Jeong, Koo, Ban, Kim (b0075) 2021; 68 Li, Zou, Jiang, Zhou (b0120) 2019; 7 Zhang, Jiang, Chen (b0165) 2021; 68 Hang (b0150) 2021; 36 Xiao, Xue, Li, Yang (b0045) 2021; 23(8) Xiong (b0025) 2020; 20 Wu, Jin, Li, Wang (b0090) 2021; 172 Wang, Peng, Ayodeji, Xia, Wang, Li (b0135) 2021; 151 Chen, Qi, Wang, Cui, Wu, Yang (b0140) 2021; 176 Cheng, Lin, Wu, Zhu, Shao (b0185) 2021; 216 Shi, He, Wang (b0105) 2021; 173 Huang, Zhang, Tang, Zhao, Lu (b0155) 2021 Zheng, Zhou, Yang, Liu, Liu, Zhang (b0040) 2021; 101 Zhong, Qu, Fang, Li, Wang (b0080) 2021; 436 Sun (b0085) 2021; 70 Wang, Lei, Li, Li (b0035) 2020; 69 Han, Zhu (b0005) 2021; 7 Ahmadi, Poure, Saadate, Arab Khaburi (b0030) 2021; 9 Wei, Zhang, Shang, Gu (b0095) 2021; 183 Sha, Radzienski, Soman, Wandowski, Cao, Ostachowicz (b0175) 2021; 30 Shi, Peng, Peng, Zhang, Goebel, Wu (b0020) 2022; 162 Xu, Chatterton, Pennacchi (b0055) 2021; 148 Sbert, Ancuti, Ancuti, Poch, Chen, Vila (b0180) 2021; 9 Liu, Hsaio, Tu (b0065) 2019; 66 Han, Yang, Lee (b0170) Feb 2011; 20 Zhou (b0050) 2021; 173 Zhou, Li, Tian, Jiang (b0195) 2020; 161 Y. L. He, K. Li, N. Zhang, Y. Xu, and Q. X. Zhu, Fault diagnosis using improved discrimination locality preserving projections integrated with sparse autoencoder, IEEE Trans Instrum Meas, vol. 70, 2021. Zheng, Li, Chen (b0110) 2002; 16 Li Zou, Heung Fai Lam, Jun Hu, Adaptive resize-residual deep neural network for fault diagnosis of rotating machinery, Struct Health Monitor 2022: 14759217221122266. Liu, Yang, Wang, Zhang (b0070) 2021 Chen, Chen, Wu, Cheng, Lin (b0200) 2019; 198 He, Wu, Gu, Jin, Ma, Qu (b0145) 2021; 173 Xiao (10.1016/j.engstruct.2023.115708_b0045) 2021; 23(8) Sha (10.1016/j.engstruct.2023.115708_b0175) 2021; 30 Shi (10.1016/j.engstruct.2023.115708_b0105) 2021; 173 Hang (10.1016/j.engstruct.2023.115708_b0150) 2021; 36 Wang (10.1016/j.engstruct.2023.115708_b0035) 2020; 69 Shao (10.1016/j.engstruct.2023.115708_b0130) 2021; 74 Chen (10.1016/j.engstruct.2023.115708_b0140) 2021; 176 Han (10.1016/j.engstruct.2023.115708_b0005) 2021; 7 Wang (10.1016/j.engstruct.2023.115708_b0135) 2021; 151 Wang (10.1016/j.engstruct.2023.115708_b0060) 2021; 183 Snoek (10.1016/j.engstruct.2023.115708_b0190) 2012; 25 Xu (10.1016/j.engstruct.2023.115708_b0055) 2021; 148 10.1016/j.engstruct.2023.115708_b0115 Lee (10.1016/j.engstruct.2023.115708_b0160) 2021; 68 Ahmadi (10.1016/j.engstruct.2023.115708_b0030) 2021; 9 Xiong (10.1016/j.engstruct.2023.115708_b0025) 2020; 20 Zheng (10.1016/j.engstruct.2023.115708_b0040) 2021; 101 Zhou (10.1016/j.engstruct.2023.115708_b0050) 2021; 173 He (10.1016/j.engstruct.2023.115708_b0145) 2021; 173 Ruan (10.1016/j.engstruct.2023.115708_b0010) 2021 Lee (10.1016/j.engstruct.2023.115708_b0075) 2021; 68 Wei (10.1016/j.engstruct.2023.115708_b0095) 2021; 183 Zhang (10.1016/j.engstruct.2023.115708_b0165) 2021; 68 Han (10.1016/j.engstruct.2023.115708_b0170) 2011; 20 Sun (10.1016/j.engstruct.2023.115708_b0085) 2021; 70 Zhong (10.1016/j.engstruct.2023.115708_b0080) 2021; 436 Wang (10.1016/j.engstruct.2023.115708_b0100) 2021; 173 Wu (10.1016/j.engstruct.2023.115708_b0090) 2021; 172 Zhou (10.1016/j.engstruct.2023.115708_b0195) 2020; 161 Liu (10.1016/j.engstruct.2023.115708_b0065) 2019; 66 Huang (10.1016/j.engstruct.2023.115708_b0155) 2021 10.1016/j.engstruct.2023.115708_b0125 Li (10.1016/j.engstruct.2023.115708_b0120) 2019; 7 Sbert (10.1016/j.engstruct.2023.115708_b0180) 2021; 9 Zheng (10.1016/j.engstruct.2023.115708_b0110) 2002; 16 Zhang (10.1016/j.engstruct.2023.115708_b0015) 2021; 104 Liu (10.1016/j.engstruct.2023.115708_b0070) 2021 Cheng (10.1016/j.engstruct.2023.115708_b0185) 2021; 216 Shi (10.1016/j.engstruct.2023.115708_b0020) 2022; 162 Chen (10.1016/j.engstruct.2023.115708_b0200) 2019; 198 |
| References_xml | – volume: 23(8) year: 2021 ident: b0045 article-title: Low-pass filtering empirical wavelet transform machine learning based fault diagnosis for combined fault of wind turbines publication-title: Entropy (Basel) – year: 2021 ident: b0155 article-title: A novel fault diagnosis method based on CNN and LSTM and its application in fault diagnosis for complex systems publication-title: Artif Intell Rev – volume: 183 year: 2021 ident: b0095 article-title: Extreme learning Machine-based classifier for fault diagnosis of rotating Machinery using a residual network and continuous wavelet transform publication-title: Measurement – volume: 66 start-page: 4788 year: 2019 end-page: 4797 ident: b0065 article-title: Time series classification with multivariate convolutional neural network publication-title: IEEE Trans Ind Electron – volume: 173 year: 2021 ident: b0105 article-title: An LSTM-based severity evaluation method for intermittent open faults of an electrical connector under a shock test publication-title: Measurement – volume: 9 start-page: 4676 year: 2021 end-page: 4686 ident: b0030 article-title: Open-switch and open-clamping diode fault diagnosis for single-phase five-level neutral-point-clamped inverters publication-title: IEEE J Emerg Select Top Power Electron – volume: 9 start-page: 28785 year: 2021 end-page: 28796 ident: b0180 article-title: Histogram ordering publication-title: IEEE Access – volume: 436 start-page: 74 year: 2021 end-page: 91 ident: b0080 article-title: The intermittent fault diagnosis of analog circuits based on EEMD-DBN publication-title: Neurocomputing – volume: 74 start-page: 65 year: 2021 end-page: 76 ident: b0130 article-title: A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance publication-title: Inform Fusion – volume: 101 start-page: 68 year: 2021 end-page: 77 ident: b0040 article-title: Multivariate/minor fault diagnosis with severity level based on Bayesian decision theory and multidimensional RBC publication-title: J Process Control – volume: 162 year: 2022 ident: b0020 article-title: Planetary gearbox fault diagnosis using bidirectional-convolutional LSTM networks publication-title: Mech Syst Signal Process – volume: 68 start-page: 1581 year: 2021 end-page: 1590 ident: b0160 article-title: Detection and assessment of i&c cable faults using time-frequency R-CNN-based reflectometry publication-title: IEEE Trans Ind Electron – volume: 70 start-page: 1 year: 2021 end-page: 12 ident: b0085 article-title: Fault diagnosis of conventional circuit breaker contact system based on time-frequency analysis and improved AlexNet publication-title: IEEE Trans Instrum Meas – volume: 36 start-page: 11124 year: 2021 end-page: 11134 ident: b0150 article-title: Integration of interturn fault diagnosis and torque ripple minimization control for direct-torque-controlled SPMSM drive system publication-title: IEEE Trans Power Electron – volume: 16 start-page: 447 year: 2002 end-page: 457 ident: b0110 article-title: Gear fault diagnosis based on continuous wavelet transform publication-title: Mech Syst Sig Process – volume: 25 year: 2012 ident: b0190 article-title: Practical bayesian optimization of machine learning algorithms publication-title: Adv Neural Inf Proces Syst – volume: 20 start-page: 506 year: Feb 2011 end-page: 512 ident: b0170 article-title: A novel 3-D color histogram equalization method with uniform 1-D gray scale histogram publication-title: IEEE Trans Image Process – volume: 68 start-page: 3445 year: 2021 end-page: 3453 ident: b0075 article-title: Attention recurrent neural network-based severity estimation method for interturn short-circuit fault in permanent magnet synchronous machines publication-title: IEEE Trans Ind Electron – volume: 20 start-page: 4965 year: 2020 ident: b0025 article-title: A novel end-to-end fault diagnosis approach for rolling bearings by integrating wavelet packet transform into convolutional neural network structures publication-title: Sensors – volume: 173 year: 2021 ident: b0050 article-title: Fault feature extraction for rolling bearings based on parameter-adaptive variational mode decomposition and multi-point optimal minimum entropy deconvolution publication-title: Measurement – volume: 172 year: 2021 ident: b0090 article-title: A novel method for simultaneous-fault diagnosis based on between-class learning publication-title: Measurement – volume: 7 start-page: 165710 year: 2019 end-page: 165723 ident: b0120 article-title: Fault diagnosis of rotating machinery based on combination of deep belief network and one-dimensional convolutional neural network publication-title: IEEE Access – volume: 69 start-page: 401 year: 2020 end-page: 412 ident: b0035 article-title: A hybrid prognostics approach for estimating remaining useful life of rolling element bearings publication-title: IEEE Trans Reliab – volume: 68 start-page: 6257 year: 2021 end-page: 6266 ident: b0165 article-title: Multiple-model-based diagnosis of multiple faults with high-speed train applications using second-level adaptation publication-title: IEEE Trans Ind Electron – reference: Y. L. He, K. Li, N. Zhang, Y. Xu, and Q. X. Zhu, Fault diagnosis using improved discrimination locality preserving projections integrated with sparse autoencoder, IEEE Trans Instrum Meas, vol. 70, 2021. – volume: 151 year: 2021 ident: b0135 article-title: Advanced fault diagnosis method for nuclear power plant based on convolutional gated recurrent network and enhanced particle swarm optimization publication-title: Ann Nucl Energy – start-page: 1 year: 2021 ident: b0010 article-title: A relation-based semi-supervised method for gearbox fault diagnosis with limited labeled samples publication-title: IEEE Trans Instrum Meas – volume: 30 start-page: pp year: 2021 ident: b0175 article-title: Delamination imaging in laminated composite plates using 2D wavelet analysis of guided wavefields publication-title: Smart Mater Struct – volume: 104 year: 2021 ident: b0015 article-title: Compressor fault diagnosis system based on PCA-PSO-LSSVM algorithm publication-title: Sci Prog – volume: 173 year: 2021 ident: b0145 article-title: Rolling bearing fault diagnosis based on composite multiscale permutation entropy and reverse cognitive fruit fly optimization algorithm – extreme learning machine publication-title: Measurement – volume: 161 year: 2020 ident: b0195 article-title: A novel method based on nonlinear auto-regression neural network and convolutional neural network for imbalanced fault diagnosis of rotating machinery publication-title: Measurement – volume: 183 year: 2021 ident: b0060 article-title: Information interval spectrum: a novel methodology for rolling-element bearing diagnosis publication-title: Measurement – volume: 148 year: 2021 ident: b0055 article-title: Rolling element bearing diagnosis based on singular value decomposition and composite squared envelope spectrum publication-title: Mech Syst Sig Process – year: 2021 ident: b0070 article-title: Acoustic emission analysis for wind turbine blade bearing fault detection under time-varying low-speed and heavy blade load conditions publication-title: IEEE Trans Ind Appl – volume: 216 year: 2021 ident: b0185 article-title: Intelligent fault diagnosis of rotating machinery based on continuous wavelet transform-local binary convolutional neural network publication-title: Knowl-Based Syst – volume: 7 start-page: 2881 year: 2021 end-page: 2891 ident: b0005 article-title: Virtual current coefficients based power transistors fault diagnosis for small power EV-SRM drives publication-title: IEEE Trans Transp Electrif – volume: 176 year: 2021 ident: b0140 article-title: Fault diagnosis of rolling bearing using marine predators algorithm-based support vector machine and topology learning and out-of-sample embedding publication-title: Measurement – reference: Li Zou, Heung Fai Lam, Jun Hu, Adaptive resize-residual deep neural network for fault diagnosis of rotating machinery, Struct Health Monitor 2022: 14759217221122266. – volume: 173 year: 2021 ident: b0100 article-title: Fault diagnosis of planetary gearbox using multi-criteria feature selection and heterogeneous ensemble learning classification publication-title: Measurement – volume: 198 year: 2019 ident: b0200 article-title: Deep residual network based fault detection and diagnosis of photovoltaic arrays using current-voltage curves and ambient conditions publication-title: Energ Conver Manage – volume: 198 year: 2019 ident: 10.1016/j.engstruct.2023.115708_b0200 article-title: Deep residual network based fault detection and diagnosis of photovoltaic arrays using current-voltage curves and ambient conditions publication-title: Energ Conver Manage doi: 10.1016/j.enconman.2019.111793 – volume: 66 start-page: 4788 issue: 6 year: 2019 ident: 10.1016/j.engstruct.2023.115708_b0065 article-title: Time series classification with multivariate convolutional neural network publication-title: IEEE Trans Ind Electron doi: 10.1109/TIE.2018.2864702 – volume: 7 start-page: 165710 year: 2019 ident: 10.1016/j.engstruct.2023.115708_b0120 article-title: Fault diagnosis of rotating machinery based on combination of deep belief network and one-dimensional convolutional neural network publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2953490 – volume: 172 year: 2021 ident: 10.1016/j.engstruct.2023.115708_b0090 article-title: A novel method for simultaneous-fault diagnosis based on between-class learning publication-title: Measurement doi: 10.1016/j.measurement.2020.108839 – ident: 10.1016/j.engstruct.2023.115708_b0125 doi: 10.1109/TIM.2021.3125975 – volume: 30 start-page: pp issue: 1 year: 2021 ident: 10.1016/j.engstruct.2023.115708_b0175 article-title: Delamination imaging in laminated composite plates using 2D wavelet analysis of guided wavefields publication-title: Smart Mater Struct doi: 10.1088/1361-665X/abc66b – start-page: 1 year: 2021 ident: 10.1016/j.engstruct.2023.115708_b0010 article-title: A relation-based semi-supervised method for gearbox fault diagnosis with limited labeled samples publication-title: IEEE Trans Instrum Meas – volume: 20 start-page: 4965 issue: 17 year: 2020 ident: 10.1016/j.engstruct.2023.115708_b0025 article-title: A novel end-to-end fault diagnosis approach for rolling bearings by integrating wavelet packet transform into convolutional neural network structures publication-title: Sensors doi: 10.3390/s20174965 – volume: 68 start-page: 1581 issue: 2 year: 2021 ident: 10.1016/j.engstruct.2023.115708_b0160 article-title: Detection and assessment of i&c cable faults using time-frequency R-CNN-based reflectometry publication-title: IEEE Trans Ind Electron doi: 10.1109/TIE.2020.2970677 – volume: 74 start-page: 65 year: 2021 ident: 10.1016/j.engstruct.2023.115708_b0130 article-title: A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance publication-title: Inform Fusion doi: 10.1016/j.inffus.2021.03.008 – volume: 7 start-page: 2881 issue: 4 year: 2021 ident: 10.1016/j.engstruct.2023.115708_b0005 article-title: Virtual current coefficients based power transistors fault diagnosis for small power EV-SRM drives publication-title: IEEE Trans Transp Electrif doi: 10.1109/TTE.2021.3082146 – volume: 183 year: 2021 ident: 10.1016/j.engstruct.2023.115708_b0095 article-title: Extreme learning Machine-based classifier for fault diagnosis of rotating Machinery using a residual network and continuous wavelet transform publication-title: Measurement doi: 10.1016/j.measurement.2021.109864 – volume: 173 year: 2021 ident: 10.1016/j.engstruct.2023.115708_b0145 article-title: Rolling bearing fault diagnosis based on composite multiscale permutation entropy and reverse cognitive fruit fly optimization algorithm – extreme learning machine publication-title: Measurement doi: 10.1016/j.measurement.2020.108636 – volume: 148 year: 2021 ident: 10.1016/j.engstruct.2023.115708_b0055 article-title: Rolling element bearing diagnosis based on singular value decomposition and composite squared envelope spectrum publication-title: Mech Syst Sig Process doi: 10.1016/j.ymssp.2020.107174 – volume: 20 start-page: 506 issue: 2 year: 2011 ident: 10.1016/j.engstruct.2023.115708_b0170 article-title: A novel 3-D color histogram equalization method with uniform 1-D gray scale histogram publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2010.2068555 – volume: 9 start-page: 4676 issue: 4 year: 2021 ident: 10.1016/j.engstruct.2023.115708_b0030 article-title: Open-switch and open-clamping diode fault diagnosis for single-phase five-level neutral-point-clamped inverters publication-title: IEEE J Emerg Select Top Power Electron doi: 10.1109/JESTPE.2020.3017923 – volume: 70 start-page: 1 year: 2021 ident: 10.1016/j.engstruct.2023.115708_b0085 article-title: Fault diagnosis of conventional circuit breaker contact system based on time-frequency analysis and improved AlexNet publication-title: IEEE Trans Instrum Meas – volume: 161 year: 2020 ident: 10.1016/j.engstruct.2023.115708_b0195 article-title: A novel method based on nonlinear auto-regression neural network and convolutional neural network for imbalanced fault diagnosis of rotating machinery publication-title: Measurement doi: 10.1016/j.measurement.2020.107880 – volume: 25 year: 2012 ident: 10.1016/j.engstruct.2023.115708_b0190 article-title: Practical bayesian optimization of machine learning algorithms publication-title: Adv Neural Inf Proces Syst – volume: 68 start-page: 6257 issue: 7 year: 2021 ident: 10.1016/j.engstruct.2023.115708_b0165 article-title: Multiple-model-based diagnosis of multiple faults with high-speed train applications using second-level adaptation publication-title: IEEE Trans Ind Electron doi: 10.1109/TIE.2020.2994867 – volume: 23(8) year: 2021 ident: 10.1016/j.engstruct.2023.115708_b0045 article-title: Low-pass filtering empirical wavelet transform machine learning based fault diagnosis for combined fault of wind turbines publication-title: Entropy (Basel) – volume: 216 year: 2021 ident: 10.1016/j.engstruct.2023.115708_b0185 article-title: Intelligent fault diagnosis of rotating machinery based on continuous wavelet transform-local binary convolutional neural network publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2021.106796 – volume: 68 start-page: 3445 issue: 4 year: 2021 ident: 10.1016/j.engstruct.2023.115708_b0075 article-title: Attention recurrent neural network-based severity estimation method for interturn short-circuit fault in permanent magnet synchronous machines publication-title: IEEE Trans Ind Electron doi: 10.1109/TIE.2020.2978690 – year: 2021 ident: 10.1016/j.engstruct.2023.115708_b0155 article-title: A novel fault diagnosis method based on CNN and LSTM and its application in fault diagnosis for complex systems publication-title: Artif Intell Rev – volume: 173 year: 2021 ident: 10.1016/j.engstruct.2023.115708_b0100 article-title: Fault diagnosis of planetary gearbox using multi-criteria feature selection and heterogeneous ensemble learning classification publication-title: Measurement doi: 10.1016/j.measurement.2020.108654 – volume: 173 year: 2021 ident: 10.1016/j.engstruct.2023.115708_b0050 article-title: Fault feature extraction for rolling bearings based on parameter-adaptive variational mode decomposition and multi-point optimal minimum entropy deconvolution publication-title: Measurement doi: 10.1016/j.measurement.2020.108469 – volume: 16 start-page: 447 issue: 2–3 year: 2002 ident: 10.1016/j.engstruct.2023.115708_b0110 article-title: Gear fault diagnosis based on continuous wavelet transform publication-title: Mech Syst Sig Process doi: 10.1006/mssp.2002.1482 – volume: 104 issue: 3 year: 2021 ident: 10.1016/j.engstruct.2023.115708_b0015 article-title: Compressor fault diagnosis system based on PCA-PSO-LSSVM algorithm publication-title: Sci Prog doi: 10.1177/00368504211026110 – volume: 151 year: 2021 ident: 10.1016/j.engstruct.2023.115708_b0135 article-title: Advanced fault diagnosis method for nuclear power plant based on convolutional gated recurrent network and enhanced particle swarm optimization publication-title: Ann Nucl Energy doi: 10.1016/j.anucene.2020.107934 – volume: 9 start-page: 28785 year: 2021 ident: 10.1016/j.engstruct.2023.115708_b0180 article-title: Histogram ordering publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3058577 – volume: 173 year: 2021 ident: 10.1016/j.engstruct.2023.115708_b0105 article-title: An LSTM-based severity evaluation method for intermittent open faults of an electrical connector under a shock test publication-title: Measurement doi: 10.1016/j.measurement.2020.108653 – volume: 69 start-page: 401 issue: 1 year: 2020 ident: 10.1016/j.engstruct.2023.115708_b0035 article-title: A hybrid prognostics approach for estimating remaining useful life of rolling element bearings publication-title: IEEE Trans Reliab doi: 10.1109/TR.2018.2882682 – volume: 183 year: 2021 ident: 10.1016/j.engstruct.2023.115708_b0060 article-title: Information interval spectrum: a novel methodology for rolling-element bearing diagnosis publication-title: Measurement doi: 10.1016/j.measurement.2021.109899 – volume: 162 year: 2022 ident: 10.1016/j.engstruct.2023.115708_b0020 article-title: Planetary gearbox fault diagnosis using bidirectional-convolutional LSTM networks publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2021.107996 – volume: 436 start-page: 74 year: 2021 ident: 10.1016/j.engstruct.2023.115708_b0080 article-title: The intermittent fault diagnosis of analog circuits based on EEMD-DBN publication-title: Neurocomputing doi: 10.1016/j.neucom.2021.01.001 – year: 2021 ident: 10.1016/j.engstruct.2023.115708_b0070 article-title: Acoustic emission analysis for wind turbine blade bearing fault detection under time-varying low-speed and heavy blade load conditions publication-title: IEEE Trans Ind Appl – volume: 36 start-page: 11124 issue: 10 year: 2021 ident: 10.1016/j.engstruct.2023.115708_b0150 article-title: Integration of interturn fault diagnosis and torque ripple minimization control for direct-torque-controlled SPMSM drive system publication-title: IEEE Trans Power Electron doi: 10.1109/TPEL.2021.3073774 – ident: 10.1016/j.engstruct.2023.115708_b0115 doi: 10.1177/14759217221122266 – volume: 176 year: 2021 ident: 10.1016/j.engstruct.2023.115708_b0140 article-title: Fault diagnosis of rolling bearing using marine predators algorithm-based support vector machine and topology learning and out-of-sample embedding publication-title: Measurement doi: 10.1016/j.measurement.2021.109116 – volume: 101 start-page: 68 year: 2021 ident: 10.1016/j.engstruct.2023.115708_b0040 article-title: Multivariate/minor fault diagnosis with severity level based on Bayesian decision theory and multidimensional RBC publication-title: J Process Control doi: 10.1016/j.jprocont.2021.01.009 |
| SSID | ssj0002880 |
| Score | 2.526513 |
| Snippet | •A novel convolutional network based on Bayesian optimization and channel fusion mechanism is developed.•The proposed method is applied to extract the... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 115708 |
| SubjectTerms | Bayesian optimization Channel fusion mechanism Convolutional network Fault diagnosis Hybrid loss function |
| Title | Bayesian optimization and channel-fusion-based convolutional autoencoder network for fault diagnosis of rotating machinery |
| URI | https://dx.doi.org/10.1016/j.engstruct.2023.115708 |
| Volume | 280 |
| WOSCitedRecordID | wos000929756500001&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 customDbUrl: eissn: 1873-7323 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002880 issn: 0141-0296 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwELaWLQc4IJ6ivOQDt8pRYm9ip7cFFUFBFRIFrbhETmwXqm1SLZuq5cBvZ_zIo7BSQYhLFFlxnHi-2OPJN58Reh5nSiiTpiTNRUlmSiWkpIkiKeMzmbFK5E6J6dM7fnAgFov8_WTyo8uFOVvyuhbn5_npfzU1lIGxbersX5i7vykUwDkYHY5gdjj-keFfyAvtMiMbGA1OQpqlT1_7Ii2rhZjWhsiIncCUo52H57GyAe26sdKWVmGi9gxxR0Q0sl2ubaDW8vK8hMmqsX_x66OdE8fH_CW3eqRzuOM1atvVQFf83LQuIBANces2BK7fRvujUn_dPBrQ52AXjYMVlI04Li6C1mXRDJQlH9RMSEzzIIntB2LBGeHM5yJ3IzX1mz79Nur7AMRxpOsj_0aRbTuyOkKxGCa6nn74wbFboUFYf9lfi_E1tEU5oHWKtuZv9hb7_VxOhdt7r3_CSwzBjc1t9m9GPsvhbXQrLDbw3IPkDpro-i66OTLNPfS9gwsewwUDXPAmuOBLcMEjuOAAFwxwwQ4uuIcLbgzu4IJ7uNxHH1_tHb58TcJ-HKRiSbomVaVzzUuqVCW0FMaAr5pxlSr4xJXhmvOcytJonchcZBk1ZSm5zuSs5BL8zpI9QNO6qfVDhMFLlapSlEubCS7iUhrGUg2-fqWk5GwbZV0vFlUQq7d7piyLjpV4XPTdX9juL3z3b6O4r3jq9VqurrLbmakIbqd3JwvA11WVH_1L5cfoxvCRPEFTuEA_Rders_XXb6tnAYs_AXJPtAQ |
| 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=Bayesian+optimization+and+channel-fusion-based+convolutional+autoencoder+network+for+fault+diagnosis+of+rotating+machinery&rft.jtitle=Engineering+structures&rft.au=Zou%2C+L.&rft.au=Zhuang%2C+K.J.&rft.au=Zhou%2C+A.&rft.au=Hu%2C+J.&rft.date=2023-04-01&rft.pub=Elsevier+Ltd&rft.issn=0141-0296&rft.eissn=1873-7323&rft.volume=280&rft_id=info:doi/10.1016%2Fj.engstruct.2023.115708&rft.externalDocID=S0141029623001220 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0141-0296&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0141-0296&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0141-0296&client=summon |