Differgram: A convex optimization-based method for extracting optimal frequency band for fault diagnosis of rotating machinery
The extraction of fault resonance bands from a full frequency band has always stood as a classical and effective strategy for fault diagnosis in rotating machinery. Among the existing techniques for capturing fault frequency bands, methods such as fast kurtogram and its variants have been prevalent....
Uložené v:
| Vydané v: | Expert systems with applications Ročník 245; s. 123051 |
|---|---|
| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier Ltd
01.07.2024
|
| Predmet: | |
| ISSN: | 0957-4174, 1873-6793 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | The extraction of fault resonance bands from a full frequency band has always stood as a classical and effective strategy for fault diagnosis in rotating machinery. Among the existing techniques for capturing fault frequency bands, methods such as fast kurtogram and its variants have been prevalent. However, these methods encounter two main limitations. Firstly, they are subject to the constraints of index performance, potentially leading to the identification of incorrect frequency bands. Secondly, they do not fully harness the potential of healthy signals, thus hindering accurate fault frequency band localization. While approaches like SKRgram and accugram endeavor to leverage healthy signals to highlight differences between healthy and faulty signals, SKRgram falls short in thoroughly exploring the significance of healthy signals. Accugram relies on classifier accuracy to pinpoint fault frequency bands, rendering it less robust and less interpretable. To capitalize more effectively on healthy signals' potential, this study proposed a novel approach called differgram based on the convex optimization model. By inputting both healthy and faulty signals into the differgram, optimal fault frequency bands can be extracted. By solving the constructed convex optimization model, the optimal difference frequency bands between healthy and faulty signals are automatically identified. The method is substantiated through mathematical proofs and physical explanations, enhancing its interpretability. Through simulations and experimental validation, the proposed diffetergram demonstrates its efficiency in the extraction of fault frequency bands. In comparison to fast kurtogram, SKRgram, and accugram, the differgram showcases heightened robustness and noise immunity. |
|---|---|
| AbstractList | The extraction of fault resonance bands from a full frequency band has always stood as a classical and effective strategy for fault diagnosis in rotating machinery. Among the existing techniques for capturing fault frequency bands, methods such as fast kurtogram and its variants have been prevalent. However, these methods encounter two main limitations. Firstly, they are subject to the constraints of index performance, potentially leading to the identification of incorrect frequency bands. Secondly, they do not fully harness the potential of healthy signals, thus hindering accurate fault frequency band localization. While approaches like SKRgram and accugram endeavor to leverage healthy signals to highlight differences between healthy and faulty signals, SKRgram falls short in thoroughly exploring the significance of healthy signals. Accugram relies on classifier accuracy to pinpoint fault frequency bands, rendering it less robust and less interpretable. To capitalize more effectively on healthy signals' potential, this study proposed a novel approach called differgram based on the convex optimization model. By inputting both healthy and faulty signals into the differgram, optimal fault frequency bands can be extracted. By solving the constructed convex optimization model, the optimal difference frequency bands between healthy and faulty signals are automatically identified. The method is substantiated through mathematical proofs and physical explanations, enhancing its interpretability. Through simulations and experimental validation, the proposed diffetergram demonstrates its efficiency in the extraction of fault frequency bands. In comparison to fast kurtogram, SKRgram, and accugram, the differgram showcases heightened robustness and noise immunity. |
| ArticleNumber | 123051 |
| Author | Guo, Jianchun Sun, Weifang Xiang, Jiawei Yang, Ronggang Liu, Yi |
| Author_xml | – sequence: 1 givenname: Jianchun surname: Guo fullname: Guo, Jianchun email: 15167735885@163.com organization: School of Mechanical Engineering, Guangxi University, Nanning 530004, PR China – sequence: 2 givenname: Yi surname: Liu fullname: Liu, Yi email: liuyi_aa1@163.com organization: College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035 PR China – sequence: 3 givenname: Ronggang orcidid: 0009-0004-0682-4526 surname: Yang fullname: Yang, Ronggang email: 20210057@wzu.edu.cn organization: College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035 PR China – sequence: 4 givenname: Weifang surname: Sun fullname: Sun, Weifang email: vincent_suen@126.com organization: College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035 PR China – sequence: 5 givenname: Jiawei orcidid: 0000-0003-4028-985X surname: Xiang fullname: Xiang, Jiawei email: jwxiang@wzu.edu.cn organization: College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035 PR China |
| BookMark | eNp9kL1OwzAURi1UJMrPCzD5BVKu46SOEUvFv4TEArPlONfFVWsX20DLwLOTEiYGprt850rnHJKRDx4JOWUwYcCmZ4sJpg89KaHkE1ZyqNkeGbNG8GIqJB-RMchaFBUT1QE5TGkBwASAGJOvK2ctxnnUq3M6oyb4d9zQsM5u5T51dsEXrU7Y0RXml9BRGyLFTY7aZOfnw1AvqY34-obebGmr_bCy-m2Zaef03IfkEg2WxpD1D7bS5sV5jNtjsm_1MuHJ7z0izzfXT5d3xcPj7f3l7KEwHCAXbTOVbSUryYStW2MEcsNR2hJkwzuoW0QuNDDgtmxsa1oJrOaV4VMLklvJj0gz_DUxpBTRKuPyj16v4paKgdp1VAu166h2HdXQsUfLP-g69s5x-z90MUDYS707jCoZ1_fBzkU0WXXB_Yd_A1eIkWc |
| CitedBy_id | crossref_primary_10_1016_j_eswa_2025_126947 crossref_primary_10_1088_1402_4896_ad5f64 crossref_primary_10_1109_JSEN_2024_3468335 crossref_primary_10_1007_s00170_024_14626_0 crossref_primary_10_3390_machines13080722 crossref_primary_10_1016_j_eswa_2025_126409 crossref_primary_10_1016_j_measurement_2024_115737 crossref_primary_10_1016_j_measurement_2025_118035 crossref_primary_10_1016_j_ymssp_2024_111611 crossref_primary_10_1088_1361_6501_ad894e crossref_primary_10_1109_JSEN_2024_3382809 crossref_primary_10_1016_j_apacoust_2024_109962 crossref_primary_10_1016_j_ymssp_2024_112033 |
| Cites_doi | 10.1016/j.ymssp.2005.12.002 10.1016/j.ymssp.2021.108366 10.1016/j.jsv.2016.08.026 10.1016/j.eswa.2023.120858 10.1016/j.measurement.2015.07.045 10.1016/j.knosys.2023.110984 10.1016/j.eswa.2023.119738 10.1016/j.ymssp.2009.11.011 10.1016/j.ymssp.2020.107130 10.1109/TR.2018.2882682 10.1088/0957-0233/27/12/125019 10.1016/j.eswa.2021.116290 10.1016/j.ymssp.2010.12.011 10.1016/j.jmsy.2023.07.012 10.1016/j.isatra.2023.03.026 10.1088/1361-6501/ac7bd5 10.1016/j.measurement.2020.107735 10.1016/j.ress.2023.109768 10.1016/j.ymssp.2015.04.034 10.1016/j.eswa.2023.121216 10.1016/j.triboint.2015.12.037 10.1109/TSP.2013.2265222 10.1016/j.ymssp.2010.05.018 10.1016/j.eswa.2020.114094 10.1016/j.ymssp.2021.108333 10.1016/j.aei.2023.101883 10.1109/TII.2020.2968370 10.1016/j.isatra.2019.05.007 |
| ContentType | Journal Article |
| Copyright | 2023 Elsevier Ltd |
| Copyright_xml | – notice: 2023 Elsevier Ltd |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.eswa.2023.123051 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1873-6793 |
| ExternalDocumentID | 10_1016_j_eswa_2023_123051 S0957417423035534 |
| GroupedDBID | --K --M .DC .~1 0R~ 13V 1B1 1RT 1~. 1~5 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN 9JO AAAKF AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AARIN AAXUO AAYFN ABBOA ABFNM ABMAC ABMVD ABUCO ABYKQ ACDAQ ACGFS ACHRH ACNTT ACRLP ACZNC ADBBV ADEZE ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGJBL AGUBO AGUMN AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJOXV AKRWK ALEQD ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD APLSM AXJTR BJAXD BKOJK BLXMC BNSAS CS3 DU5 EBS EFJIC EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HAMUX IHE J1W JJJVA KOM MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 RIG ROL RPZ SDF SDG SDP SDS SES SEW SPC SPCBC SSB SSD SSL SST SSV SSZ T5K TN5 ~G- 29G 9DU AAAKG AAQXK AATTM AAXKI AAYWO AAYXX ABJNI ABKBG ABUFD ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADJOM ADMUD ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EFLBG EJD FEDTE FGOYB G-2 HLZ HVGLF HZ~ LG9 LY1 LY7 M41 R2- SBC SET WUQ XPP ZMT ~HD |
| ID | FETCH-LOGICAL-c300t-b869b494917f5bcc7e3c3e9f20983d05bee37a0103f28fbcb901534c36f093f93 |
| ISICitedReferencesCount | 15 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001155989000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0957-4174 |
| IngestDate | Sat Nov 29 07:09:12 EST 2025 Tue Nov 18 22:43:31 EST 2025 Sat Apr 13 16:35:12 EDT 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Fault diagnosis Fault frequency band extraction The importance of health signals Differgram |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c300t-b869b494917f5bcc7e3c3e9f20983d05bee37a0103f28fbcb901534c36f093f93 |
| ORCID | 0000-0003-4028-985X 0009-0004-0682-4526 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_eswa_2023_123051 crossref_primary_10_1016_j_eswa_2023_123051 elsevier_sciencedirect_doi_10_1016_j_eswa_2023_123051 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-07-01 2024-07-00 |
| PublicationDateYYYYMMDD | 2024-07-01 |
| PublicationDate_xml | – month: 07 year: 2024 text: 2024-07-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationTitle | Expert systems with applications |
| PublicationYear | 2024 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Guo, Si, Liu, Li, Li, Xiang (b0175) 2022; 33 Hou, Wang, Xia, Peng, Tsui (b0155) 2023; 191 Xiang, Zhong, Gao (b0170) 2015; 75 Antoni (b0035) 2007; 21 Cui, Tian, Wei, Liu (b0075) 2024; 238 Chen, Shao, Xiao, Yan, Cai, Liu (b0125) 2023; 198 Gu, Yang, Liu, Hao (b0055) 2016; 27 Hou, Wang, Kong, Liu, Peng, Tsui (b0160) 2022; 174 Kumar, Gandhi, Zhou, Kumar, Xiang (b0030) 2020; 158 55(August 2022), 101883. https://doi.org/10.1016/j.aei.2023.101883. Guo, Si, Xiang (b0165) 2022; 196 Li, J., Huang, R., Chen, Z., He, G., Gryllias, K. C., & Li, W. (2023). Deep continual transfer learning with dynamic weight aggregation for fault diagnosis of industrial streaming data under varying working conditions. Barszcz, Jabłoński (b0040) 2011; 25 Lin, Shao, Zhou, Cai, Liu (b0130) 2023; 230 (April 2021), 108333. https://doi.org/10.1016/j.ymssp.2021.108333. Ma, S., Han, Q., & Chu, F. (2023). Sparse representation learning for fault feature extraction and diagnosis of rotating machinery. Rai, Upadhyay (b0025) 2016; 96 , Shao, X., & Kim, C. (2024). Adaptive multi-scale attention convolution neural network for cross-domain fault diagnosis. Gilles (b0060) 2013; 61 Kumar, A., Tang, H., Vashishtha, G., & Xiang, J. (2022). Noise subtraction and marginal enhanced square envelope spectrum (MESES) for the identification of bearing defects in centrifugal and axial pump. Gao, Xiang, Hou, Tang, Zhong, Ye (b0185) 2021; 147 Su, Wang, Zhu, Zhang, Guo (b0045) 2010; 24 Antoni (b0050) 2016; 74 Miao, Y., Wang, J., Zhang, B., & Li, H. (2022). Practical framework of Gini index in the application of machinery fault feature extraction. Wang, Han, Chu, Feng (b0145) 2016; 385 (May 2021), 108366. https://doi.org/10.1016/j.ymssp.2021.108366. 221(October 2021), 119738. https://doi.org/10.1016/j.eswa.2023.119738. Gao, Liu, Xiang (b0110) 2020; 16 Kumar, P., & Hati, A. S. (2022). Dilated convolutional neural network based model for bearing faults and broken rotor bar detection in squirrel cage induction motors. Vashishtha, Chauhan, Kumar, Kumar, Zimroz, Kumar (b0095) 2023; 280 (October 2021), 120858. https://doi.org/10.1016/j.eswa.2023.120858. 191(October 2021), 116290. https://doi.org/10.1016/j.eswa.2021.116290. Yan, Shao, Min, Peng, Cai, Liu (b0085) 2023; 236 Xia, Shao, Williams, Lu, Shu, de Silva (b0115) 2021; 215 Kumar, A., Kumar, R., Tang, H., & Xiang, J. (2024). A comprehensive study on developing an intelligent framework for identification and quantitative evaluation of the bearing defect size. 242(October 2023), 109768. https://doi.org/10.1016/j.ress.2023.109768. Kumar, Vashishtha, Gandhi, Tang, Xiang (b0090) 2021; 104 Matania, Bachar, Khemani, Das, Azarian, Bortman (b0120) 2023; 56 Guo, Si, Xiang (b0015) 2023; 138 (October 2021), 119738. https://doi.org/10.1016/j.eswa.2023.119738. Lei, Lin, He, Zi (b0065) 2011; 25 Surucu, O., Gadsden, S. A., & Yawney, J. (2023). Condition Monitoring using Machine Learning: A Review of Theory, Applications, and Recent Advances. 236(April 2023), 121216. https://doi.org/10.1016/j.eswa.2023.121216. Xiao, Shao, Feng, Han, Wan, Liu (b0140) 2023; 70 (April 2020), 114094. https://doi.org/10.1016/j.eswa.2020.114094. Wang, Lei, Li, Li (b0190) 2020; 69 Dibaj, A., Ettefagh, M. M., Hassannejad, R., & Ehghaghi, M. B. (2021). A hybrid fine-tuned VMD and CNN scheme for untrained compound fault diagnosis of rotating machinery with unequal-severity faults. Liu, Jin, Zuo, Peng (b0150) 2019; 95 Xiao (10.1016/j.eswa.2023.123051_b0140) 2023; 70 Su (10.1016/j.eswa.2023.123051_b0045) 2010; 24 10.1016/j.eswa.2023.123051_b0080 10.1016/j.eswa.2023.123051_b0180 Antoni (10.1016/j.eswa.2023.123051_b0035) 2007; 21 Matania (10.1016/j.eswa.2023.123051_b0120) 2023; 56 10.1016/j.eswa.2023.123051_b0105 Chen (10.1016/j.eswa.2023.123051_b0125) 2023; 198 Hou (10.1016/j.eswa.2023.123051_b0155) 2023; 191 Barszcz (10.1016/j.eswa.2023.123051_b0040) 2011; 25 10.1016/j.eswa.2023.123051_b0005 10.1016/j.eswa.2023.123051_b0020 Rai (10.1016/j.eswa.2023.123051_b0025) 2016; 96 Gu (10.1016/j.eswa.2023.123051_b0055) 2016; 27 10.1016/j.eswa.2023.123051_b0100 Xia (10.1016/j.eswa.2023.123051_b0115) 2021; 215 Lin (10.1016/j.eswa.2023.123051_b0130) 2023; 230 Guo (10.1016/j.eswa.2023.123051_b0175) 2022; 33 Gao (10.1016/j.eswa.2023.123051_b0110) 2020; 16 Gao (10.1016/j.eswa.2023.123051_b0185) 2021; 147 Antoni (10.1016/j.eswa.2023.123051_b0050) 2016; 74 10.1016/j.eswa.2023.123051_b0070 Gilles (10.1016/j.eswa.2023.123051_b0060) 2013; 61 Yan (10.1016/j.eswa.2023.123051_b0085) 2023; 236 Hou (10.1016/j.eswa.2023.123051_b0160) 2022; 174 Vashishtha (10.1016/j.eswa.2023.123051_b0095) 2023; 280 Kumar (10.1016/j.eswa.2023.123051_b0030) 2020; 158 Guo (10.1016/j.eswa.2023.123051_b0165) 2022; 196 Cui (10.1016/j.eswa.2023.123051_b0075) 2024; 238 Liu (10.1016/j.eswa.2023.123051_b0150) 2019; 95 Guo (10.1016/j.eswa.2023.123051_b0015) 2023; 138 10.1016/j.eswa.2023.123051_b0135 Wang (10.1016/j.eswa.2023.123051_b0190) 2020; 69 10.1016/j.eswa.2023.123051_b0010 Kumar (10.1016/j.eswa.2023.123051_b0090) 2021; 104 Xiang (10.1016/j.eswa.2023.123051_b0170) 2015; 75 Lei (10.1016/j.eswa.2023.123051_b0065) 2011; 25 Wang (10.1016/j.eswa.2023.123051_b0145) 2016; 385 10.1016/j.eswa.2023.123051_b0195 |
| References_xml | – reference: Shao, X., & Kim, C. (2024). Adaptive multi-scale attention convolution neural network for cross-domain fault diagnosis. – volume: 75 start-page: 180 year: 2015 end-page: 191 ident: b0170 article-title: Rolling element bearing fault detection using PPCA and spectral kurtosis publication-title: Measurement – reference: Miao, Y., Wang, J., Zhang, B., & Li, H. (2022). Practical framework of Gini index in the application of machinery fault feature extraction. – reference: Li, J., Huang, R., Chen, Z., He, G., Gryllias, K. C., & Li, W. (2023). Deep continual transfer learning with dynamic weight aggregation for fault diagnosis of industrial streaming data under varying working conditions. – volume: 385 start-page: 330 year: 2016 end-page: 349 ident: b0145 article-title: A new SKRgram based demodulation technique for planet bearing fault detection publication-title: Journal of Sound and Vibration – volume: 21 start-page: 108 year: 2007 end-page: 124 ident: b0035 article-title: Fast computation of the kurtogram for the detection of transient faults publication-title: Mechanical Systems and Signal Processing – volume: 138 start-page: 546 year: 2023 end-page: 561 ident: b0015 article-title: Cycle kurtosis entropy guided symplectic geometry mode decomposition for detecting faults in rotating machinery publication-title: ISA Transactions – volume: 56 year: 2023 ident: b0120 article-title: One-fault-shot learning for fault severity estimation of gears that addresses differences between simulation and experimental signals and transfer function effects publication-title: Advanced Engineering Informatics – reference: (October 2021), 120858. https://doi.org/10.1016/j.eswa.2023.120858. – reference: (October 2021), 119738. https://doi.org/10.1016/j.eswa.2023.119738. – reference: , 191(October 2021), 116290. https://doi.org/10.1016/j.eswa.2021.116290. – reference: Dibaj, A., Ettefagh, M. M., Hassannejad, R., & Ehghaghi, M. B. (2021). A hybrid fine-tuned VMD and CNN scheme for untrained compound fault diagnosis of rotating machinery with unequal-severity faults. – reference: Surucu, O., Gadsden, S. A., & Yawney, J. (2023). Condition Monitoring using Machine Learning: A Review of Theory, Applications, and Recent Advances. – volume: 215 year: 2021 ident: b0115 article-title: Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning publication-title: Reliability Engineering and System Safety – volume: 25 start-page: 431 year: 2011 end-page: 451 ident: b0040 article-title: A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram publication-title: Mechanical Systems and Signal Processing – volume: 27 year: 2016 ident: b0055 article-title: Rolling element bearing faults diagnosis based on kurtogram and frequency domain correlated kurtosis publication-title: Measurement Science and Technology – volume: 95 start-page: 346 year: 2019 end-page: 357 ident: b0150 article-title: ACCUGRAM: A novel approach based on classification to frequency band selection for rotating machinery fault diagnosis publication-title: ISA Transactions – volume: 238 year: 2024 ident: b0075 article-title: A self-attention based contrastive learning method for bearing fault diagnosis publication-title: Expert Systems with Applications – volume: 33 year: 2022 ident: b0175 article-title: FEM simulation-determined band pass filter method with continuously changed bandwidth for fault detection in axial publication-title: Measurement Science and Technology – reference: (April 2020), 114094. https://doi.org/10.1016/j.eswa.2020.114094. – volume: 158 year: 2020 ident: b0030 article-title: Latest developments in gear defect diagnosis and prognosis: A review publication-title: Measurement – reference: , – reference: (May 2021), 108366. https://doi.org/10.1016/j.ymssp.2021.108366. – reference: Kumar, A., Kumar, R., Tang, H., & Xiang, J. (2024). A comprehensive study on developing an intelligent framework for identification and quantitative evaluation of the bearing defect size. – reference: (April 2021), 108333. https://doi.org/10.1016/j.ymssp.2021.108333. – reference: Kumar, P., & Hati, A. S. (2022). Dilated convolutional neural network based model for bearing faults and broken rotor bar detection in squirrel cage induction motors. – volume: 236 year: 2023 ident: b0085 article-title: FGDAE: A new machinery anomaly detection method towards complex operating conditions publication-title: Reliability Engineering and System Safety – volume: 74 start-page: 73 year: 2016 end-page: 94 ident: b0050 article-title: The infogram: Entropic evidence of the signature of repetitive transients publication-title: Mechanical Systems and Signal Processing – volume: 174 year: 2022 ident: b0160 article-title: Understanding importance of positive and negative signs of optimized weights used in the sum of weighted normalized Fourier spectrum/envelope spectrum for machine condition monitoring publication-title: Mechanical Systems and Signal Processing – volume: 16 start-page: 4961 year: 2020 end-page: 4971 ident: b0110 article-title: FEM Simulation-Based Generative Adversarial Networks to Detect Bearing Faults publication-title: IEEE Transactions on Industrial Informatics – volume: 69 start-page: 401 year: 2020 end-page: 412 ident: b0190 article-title: A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings publication-title: IEEE Transactions on Reliability – reference: , 242(October 2023), 109768. https://doi.org/10.1016/j.ress.2023.109768. – reference: Kumar, A., Tang, H., Vashishtha, G., & Xiang, J. (2022). Noise subtraction and marginal enhanced square envelope spectrum (MESES) for the identification of bearing defects in centrifugal and axial pump. – volume: 196 year: 2022 ident: b0165 article-title: A compound fault diagnosis method of rolling bearing based on wavelet scattering transform and improved soft threshold denoising algorithm publication-title: Measurement – reference: , 221(October 2021), 119738. https://doi.org/10.1016/j.eswa.2023.119738. – volume: 191 year: 2023 ident: b0155 article-title: Difference mode decomposition for adaptive signal decomposition publication-title: Mechanical Systems and Signal Processing – volume: 104 year: 2021 ident: b0090 article-title: Tacho-less sparse CNN to detect defects in rotor-bearing systems at varying speed publication-title: Engineering Applications of Artificial Intelligence – volume: 147 year: 2021 ident: b0185 article-title: Method using L-kurtosis and enhanced clustering-based segmentation to detect faults in axial piston pumps publication-title: Mechanical Systems and Signal Processing – volume: 70 start-page: 186 year: 2023 end-page: 201 ident: b0140 article-title: Towards trustworthy rotating machinery fault diagnosis via attention uncertainty in transformer publication-title: Journal of Manufacturing Systems – volume: 25 start-page: 1738 year: 2011 end-page: 1749 ident: b0065 article-title: Application of an improved kurtogram method for fault diagnosis of rolling element bearings publication-title: Mechanical Systems and Signal Processing – reference: , 236(April 2023), 121216. https://doi.org/10.1016/j.eswa.2023.121216. – volume: 230 year: 2023 ident: b0130 article-title: Generalized MAML for few-shot cross-domain fault diagnosis of bearing driven by heterogeneous signals publication-title: Expert Systems with Applications – volume: 24 start-page: 1458 year: 2010 end-page: 1472 ident: b0045 article-title: Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement publication-title: Mechanical Systems and Signal Processing – reference: Ma, S., Han, Q., & Chu, F. (2023). Sparse representation learning for fault feature extraction and diagnosis of rotating machinery. – volume: 61 start-page: 3999 year: 2013 end-page: 4010 ident: b0060 article-title: Empirical wavelet transform publication-title: IEEE Transactions on Signal Processing – volume: 198 year: 2023 ident: b0125 article-title: Collaborative fault diagnosis of rotating machinery via dual adversarial guided unsupervised multi-domain adaptation network publication-title: Mechanical Systems and Signal Processing – volume: 96 start-page: 289 year: 2016 end-page: 306 ident: b0025 article-title: A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings publication-title: Tribology International – reference: , 55(August 2022), 101883. https://doi.org/10.1016/j.aei.2023.101883. – volume: 280 year: 2023 ident: b0095 article-title: Intelligent fault diagnosis of worm gearbox based on adaptive CNN using amended gorilla troop optimization with quantum gate mutation strategy publication-title: Knowledge-Based Systems – volume: 21 start-page: 108 issue: 1 year: 2007 ident: 10.1016/j.eswa.2023.123051_b0035 article-title: Fast computation of the kurtogram for the detection of transient faults publication-title: Mechanical Systems and Signal Processing doi: 10.1016/j.ymssp.2005.12.002 – volume: 236 issue: April year: 2023 ident: 10.1016/j.eswa.2023.123051_b0085 article-title: FGDAE: A new machinery anomaly detection method towards complex operating conditions publication-title: Reliability Engineering and System Safety – ident: 10.1016/j.eswa.2023.123051_b0180 doi: 10.1016/j.ymssp.2021.108366 – volume: 56 issue: March year: 2023 ident: 10.1016/j.eswa.2023.123051_b0120 article-title: One-fault-shot learning for fault severity estimation of gears that addresses differences between simulation and experimental signals and transfer function effects publication-title: Advanced Engineering Informatics – volume: 385 start-page: 330 year: 2016 ident: 10.1016/j.eswa.2023.123051_b0145 article-title: A new SKRgram based demodulation technique for planet bearing fault detection publication-title: Journal of Sound and Vibration doi: 10.1016/j.jsv.2016.08.026 – ident: 10.1016/j.eswa.2023.123051_b0005 doi: 10.1016/j.eswa.2023.120858 – volume: 75 start-page: 180 year: 2015 ident: 10.1016/j.eswa.2023.123051_b0170 article-title: Rolling element bearing fault detection using PPCA and spectral kurtosis publication-title: Measurement doi: 10.1016/j.measurement.2015.07.045 – volume: 238 issue: PA year: 2024 ident: 10.1016/j.eswa.2023.123051_b0075 article-title: A self-attention based contrastive learning method for bearing fault diagnosis publication-title: Expert Systems with Applications – volume: 280 year: 2023 ident: 10.1016/j.eswa.2023.123051_b0095 article-title: Intelligent fault diagnosis of worm gearbox based on adaptive CNN using amended gorilla troop optimization with quantum gate mutation strategy publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2023.110984 – ident: 10.1016/j.eswa.2023.123051_b0020 doi: 10.1016/j.eswa.2023.119738 – volume: 24 start-page: 1458 issue: 5 year: 2010 ident: 10.1016/j.eswa.2023.123051_b0045 article-title: Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement publication-title: Mechanical Systems and Signal Processing doi: 10.1016/j.ymssp.2009.11.011 – volume: 147 year: 2021 ident: 10.1016/j.eswa.2023.123051_b0185 article-title: Method using L-kurtosis and enhanced clustering-based segmentation to detect faults in axial piston pumps publication-title: Mechanical Systems and Signal Processing doi: 10.1016/j.ymssp.2020.107130 – volume: 69 start-page: 401 issue: 1 year: 2020 ident: 10.1016/j.eswa.2023.123051_b0190 article-title: A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings publication-title: IEEE Transactions on Reliability doi: 10.1109/TR.2018.2882682 – volume: 27 issue: 12 year: 2016 ident: 10.1016/j.eswa.2023.123051_b0055 article-title: Rolling element bearing faults diagnosis based on kurtogram and frequency domain correlated kurtosis publication-title: Measurement Science and Technology doi: 10.1088/0957-0233/27/12/125019 – ident: 10.1016/j.eswa.2023.123051_b0100 doi: 10.1016/j.eswa.2021.116290 – volume: 198 issue: April year: 2023 ident: 10.1016/j.eswa.2023.123051_b0125 article-title: Collaborative fault diagnosis of rotating machinery via dual adversarial guided unsupervised multi-domain adaptation network publication-title: Mechanical Systems and Signal Processing – volume: 215 issue: July year: 2021 ident: 10.1016/j.eswa.2023.123051_b0115 article-title: Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning publication-title: Reliability Engineering and System Safety – volume: 196 issue: March year: 2022 ident: 10.1016/j.eswa.2023.123051_b0165 article-title: A compound fault diagnosis method of rolling bearing based on wavelet scattering transform and improved soft threshold denoising algorithm publication-title: Measurement – volume: 25 start-page: 1738 issue: 5 year: 2011 ident: 10.1016/j.eswa.2023.123051_b0065 article-title: Application of an improved kurtogram method for fault diagnosis of rolling element bearings publication-title: Mechanical Systems and Signal Processing doi: 10.1016/j.ymssp.2010.12.011 – volume: 70 start-page: 186 issue: July year: 2023 ident: 10.1016/j.eswa.2023.123051_b0140 article-title: Towards trustworthy rotating machinery fault diagnosis via attention uncertainty in transformer publication-title: Journal of Manufacturing Systems doi: 10.1016/j.jmsy.2023.07.012 – volume: 191 issue: February year: 2023 ident: 10.1016/j.eswa.2023.123051_b0155 article-title: Difference mode decomposition for adaptive signal decomposition publication-title: Mechanical Systems and Signal Processing – volume: 138 start-page: 546 year: 2023 ident: 10.1016/j.eswa.2023.123051_b0015 article-title: Cycle kurtosis entropy guided symplectic geometry mode decomposition for detecting faults in rotating machinery publication-title: ISA Transactions doi: 10.1016/j.isatra.2023.03.026 – volume: 33 issue: 10 year: 2022 ident: 10.1016/j.eswa.2023.123051_b0175 article-title: FEM simulation-determined band pass filter method with continuously changed bandwidth for fault detection in axial publication-title: Measurement Science and Technology doi: 10.1088/1361-6501/ac7bd5 – volume: 158 year: 2020 ident: 10.1016/j.eswa.2023.123051_b0030 article-title: Latest developments in gear defect diagnosis and prognosis: A review publication-title: Measurement doi: 10.1016/j.measurement.2020.107735 – volume: 104 issue: July year: 2021 ident: 10.1016/j.eswa.2023.123051_b0090 article-title: Tacho-less sparse CNN to detect defects in rotor-bearing systems at varying speed publication-title: Engineering Applications of Artificial Intelligence – ident: 10.1016/j.eswa.2023.123051_b0080 doi: 10.1016/j.ress.2023.109768 – volume: 174 issue: March year: 2022 ident: 10.1016/j.eswa.2023.123051_b0160 article-title: Understanding importance of positive and negative signs of optimized weights used in the sum of weighted normalized Fourier spectrum/envelope spectrum for machine condition monitoring publication-title: Mechanical Systems and Signal Processing – volume: 74 start-page: 73 year: 2016 ident: 10.1016/j.eswa.2023.123051_b0050 article-title: The infogram: Entropic evidence of the signature of repetitive transients publication-title: Mechanical Systems and Signal Processing doi: 10.1016/j.ymssp.2015.04.034 – ident: 10.1016/j.eswa.2023.123051_b0070 doi: 10.1016/j.eswa.2023.121216 – volume: 96 start-page: 289 year: 2016 ident: 10.1016/j.eswa.2023.123051_b0025 article-title: A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings publication-title: Tribology International doi: 10.1016/j.triboint.2015.12.037 – volume: 61 start-page: 3999 issue: 16 year: 2013 ident: 10.1016/j.eswa.2023.123051_b0060 article-title: Empirical wavelet transform publication-title: IEEE Transactions on Signal Processing doi: 10.1109/TSP.2013.2265222 – volume: 25 start-page: 431 issue: 1 year: 2011 ident: 10.1016/j.eswa.2023.123051_b0040 article-title: A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram publication-title: Mechanical Systems and Signal Processing doi: 10.1016/j.ymssp.2010.05.018 – ident: 10.1016/j.eswa.2023.123051_b0010 doi: 10.1016/j.eswa.2020.114094 – ident: 10.1016/j.eswa.2023.123051_b0105 doi: 10.1016/j.eswa.2023.119738 – ident: 10.1016/j.eswa.2023.123051_b0195 doi: 10.1016/j.ymssp.2021.108333 – ident: 10.1016/j.eswa.2023.123051_b0135 doi: 10.1016/j.aei.2023.101883 – volume: 16 start-page: 4961 issue: 7 year: 2020 ident: 10.1016/j.eswa.2023.123051_b0110 article-title: FEM Simulation-Based Generative Adversarial Networks to Detect Bearing Faults publication-title: IEEE Transactions on Industrial Informatics doi: 10.1109/TII.2020.2968370 – volume: 95 start-page: 346 year: 2019 ident: 10.1016/j.eswa.2023.123051_b0150 article-title: ACCUGRAM: A novel approach based on classification to frequency band selection for rotating machinery fault diagnosis publication-title: ISA Transactions doi: 10.1016/j.isatra.2019.05.007 – volume: 230 issue: March year: 2023 ident: 10.1016/j.eswa.2023.123051_b0130 article-title: Generalized MAML for few-shot cross-domain fault diagnosis of bearing driven by heterogeneous signals publication-title: Expert Systems with Applications |
| SSID | ssj0017007 |
| Score | 2.510917 |
| Snippet | The extraction of fault resonance bands from a full frequency band has always stood as a classical and effective strategy for fault diagnosis in rotating... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 123051 |
| SubjectTerms | Differgram Fault diagnosis Fault frequency band extraction The importance of health signals |
| Title | Differgram: A convex optimization-based method for extracting optimal frequency band for fault diagnosis of rotating machinery |
| URI | https://dx.doi.org/10.1016/j.eswa.2023.123051 |
| Volume | 245 |
| WOSCitedRecordID | wos001155989000001&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: 1873-6793 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017007 issn: 0957-4174 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LbxMxELZCyoELb0R5yQdu0VabfdnuLYIi4FAhVERuq7XXLqnS3SrdLemFn8JvxePXhhQqeuCyila2s8p8Gc-Ov_kGodc1V0WdUxbRghP9gsKziBEmI15RRpKCVtPcNpsgh4d0PmefRqOfvhbmYkmahq7X7Oy_mlrf08aG0tkbmDssqm_oz9ro-qrNrq__ZPi3puUJkK5s1bnhla8nrfYNp67oMoK9q3bdo63o97oz5VJAgYaBUNa4sizrywmH5DqMUlW_7CBdC-w8K2SyauEsX087NazMrQprI6TcObloX0i3cWQe2D-9PQCCDMu3fmAJLXqzRSyCb3LZ7c9tc3xcuU3XHGkZ5_lVLpS_6zIZSRZYr0NKUsNlarv2eO-cZPmGf9X7bGwFaq-4fpuFONmT599BTypJ94bBv-tsb-1_gZXoCW8nJaxRwhqlXeMW2klIzugY7cw-HMw_hnMqEtuCfP_krizLMgi3n-TPoc9GOHN0H9117yF4ZvHzAI1k8xDd8z0-sHP5j9CPAU77eIYtmPBVMGELJqxhggcwYQcmHMCEAUxmlAETDmDCrcIeTDiA6TH68u7g6M37yPXsiEQax13EacE4KB5Nicq5EESmIpVMJTGjaR3nXMqUVNBcRCVUccEhHk0zkRYqZqli6RM0btpGPkUYtAprHV8BNyJLkqoSRHCaxaQg07zK6S6a-p-zFE7QHvqqLMu_G3IXTcKcMyvncu3o3FupdAGpDTRLDbpr5j270bc8R3eGf8MLNO5WvXyJbouLbnG-euUQ9wuB77Ct |
| 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=Differgram%3A+A+convex+optimization-based+method+for+extracting+optimal+frequency+band+for+fault+diagnosis+of+rotating+machinery&rft.jtitle=Expert+systems+with+applications&rft.au=Guo%2C+Jianchun&rft.au=Liu%2C+Yi&rft.au=Yang%2C+Ronggang&rft.au=Sun%2C+Weifang&rft.date=2024-07-01&rft.issn=0957-4174&rft.volume=245&rft.spage=123051&rft_id=info:doi/10.1016%2Fj.eswa.2023.123051&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_eswa_2023_123051 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0957-4174&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0957-4174&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0957-4174&client=summon |