{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T02:32:35Z","timestamp":1771468355163,"version":"3.50.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T00:00:00Z","timestamp":1706486400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T00:00:00Z","timestamp":1706486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"The National Natural Science Foundation of China Key Support Project","award":["U2133202"],"award-info":[{"award-number":["U2133202"]}]},{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["52305570"],"award-info":[{"award-number":["52305570"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the Fellowship of China Postdoctoral Science Foundation","award":["2022M720955"],"award-info":[{"award-number":["2022M720955"]}]},{"name":"the Fellowship of Heilongjiang Province Postdoctoral Science Foundation","award":["LBH-Z22187"],"award-info":[{"award-number":["LBH-Z22187"]}]},{"name":"Outstanding Doctoral Dissertation Funding Project of Heilongjiang Province","award":["LJYXL2022-011"],"award-info":[{"award-number":["LJYXL2022-011"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s10845-023-02305-y","type":"journal-article","created":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T10:02:34Z","timestamp":1706522554000},"page":"1409-1427","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Differential contrast guidance for aeroengine fault diagnosis with limited data"],"prefix":"10.1007","volume":"36","author":[{"given":"Wenhui","family":"He","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9525-1168","authenticated-orcid":false,"given":"Lin","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Song","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Changsheng","family":"Tong","sequence":"additional","affiliation":[]},{"given":"Lizheng","family":"Zu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,29]]},"reference":[{"issue":"2","key":"2305_CR1","doi-asserted-by":"publisher","first-page":"168781402199691","DOI":"10.1177\/1687814021996915","volume":"13","author":"O AlShorman","year":"2021","unstructured":"AlShorman, O., Alkahatni, F., Masadeh, M., et al. (2021). Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study. Advances in Mechanical Engineering, 13(2), 1687814021996915.","journal-title":"Advances in Mechanical Engineering"},{"key":"2305_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/8843759","volume":"2020","author":"O AlShorman","year":"2020","unstructured":"AlShorman, O., Irfan, M., Saad, N., et al. (2020). A review of artificial intelligence methods for condition monitoring and fault diagnosis of rolling element bearings for induction motor. Shock and vibration, 2020, 1.","journal-title":"Shock and vibration"},{"key":"2305_CR3","first-page":"1597","volume-title":"International conference on machine learning","author":"T Chen","year":"2020","unstructured":"Chen, T., Kornblith, S., & Norouzi, M. (2020). A simple framework for contrastive learning of visual representations. International conference on machine learning (pp. 1597\u20131607). PMLR."},{"key":"2305_CR4","doi-asserted-by":"publisher","first-page":"1190","DOI":"10.1109\/ISIE51582.2022.9831617","volume-title":"2022 IEEE 31st International Symposium on Industrial Electronics (ISIE)","author":"J Chen","year":"2022","unstructured":"Chen, J., Yang, B., & Liu, R. (2022). Self-supervised Contrastive Learning Approach for Bearing Fault Diagnosis with Rare Labeled Data. 2022 IEEE 31st International Symposium on Industrial Electronics (ISIE) (pp. 1190\u20131194). IEEE."},{"key":"2305_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42401-022-00151-z","volume":"5","author":"H Dong","year":"2022","unstructured":"Dong, H., Xun, L., & Ma, W. (2022). Fault diagnosis of aeroengine fan based on generative adversarial network and acoustic features. Aerospace Systems, 5, 1\u20139.","journal-title":"Aerospace Systems"},{"key":"2305_CR6","doi-asserted-by":"publisher","first-page":"109696","DOI":"10.1016\/j.ress.2023.109696","volume":"241","author":"S Fu","year":"2023","unstructured":"Fu, S., Lin, L., Wang, Y., et al. (2023). MCA-DTCN: A novel dual-task temporal convolutional network with multi-channel attention for first prediction time detection and remaining useful life prediction. Reliability Engineering\n& System Safety, 241, 109696.","journal-title":"Reliability Engineering & System Safety"},{"key":"2305_CR7","doi-asserted-by":"publisher","first-page":"108012","DOI":"10.1016\/j.ress.2021.108012","volume":"216","author":"S Fu","year":"2021","unstructured":"Fu, S., Zhang, Y., Lin, L., et al. (2021). Deep residual LSTM with domain-invariance for remaining useful life predictionacross domains. Reliability Engineering & System Safety, 216, 108012.","journal-title":"Reliability Engineering & System Safety"},{"issue":"5","key":"2305_CR8","doi-asserted-by":"publisher","first-page":"930","DOI":"10.2514\/2.6186","volume":"19","author":"R Ganguli","year":"2003","unstructured":"Ganguli, R. (2003). Jet engine gas-path measurement filtering using center weighted idempotent median filters. Journal of Propulsion and Power, 19(5), 930\u2013937.","journal-title":"Journal of Propulsion and Power"},{"issue":"08","key":"2305_CR9","first-page":"2041","volume":"33","author":"JY Hong","year":"2018","unstructured":"Hong, J. Y., Wang, H. W., & Ni, X. M. (2018). Assessment of performance degradation for aero-engine based on denoising autoencoder. Journal of Aerospace Power, 33(08), 2041\u20132048.","journal-title":"Journal of Aerospace Power"},{"key":"2305_CR10","doi-asserted-by":"publisher","first-page":"109174","DOI":"10.1016\/j.ymssp.2022.109174","volume":"177","author":"R Hou","year":"2022","unstructured":"Hou, R., Chen, J., Feng, Y., et al. (2022). Contrastive-weighted self-supervised model for long-tailed data classification with vision transformer augmented[J]. Mechanical Systems and Signal Processing, 177, 109174.","journal-title":"Mechanical Systems and Signal Processing"},{"key":"2305_CR11","first-page":"1","volume-title":"2021 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","author":"C Hu","year":"2021","unstructured":"Hu, C., Wu, J., & Sun, C. (2021). Robust Supervised Contrastive Learning for Fault Diagnosis under Different Noises and Conditions. 2021 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) (pp. 1\u20136). IEEE."},{"key":"2305_CR12","unstructured":"Kang, B., Li, Y., & Xie, S. (2020). Exploring balanced feature spaces for representation learning. International Conference on Learning Representations."},{"issue":"3","key":"2305_CR13","doi-asserted-by":"publisher","first-page":"730","DOI":"10.2514\/1.B36267","volume":"33","author":"Z Li","year":"2017","unstructured":"Li, Z., Zhong, S. S., & Lin, L. (2017). Novel gas turbine fault diagnosis method based on performance deviation model. Journal of Propulsion and Power, 33(3), 730\u2013739.","journal-title":"Journal of Propulsion and Power"},{"issue":"1","key":"2305_CR14","first-page":"857","volume":"35","author":"X Liu","year":"2021","unstructured":"Liu, X., Zhang, F., Hou, Z., et al. (2021). Self-supervised learning: Generative or contrastive. IEEE Transactions on Knowledge and Data Engineering, 35(1), 857\u2013876.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"2305_CR15","doi-asserted-by":"crossref","unstructured":"Lu, L., Wang, J., Huang, W., et al. (2023). Dual contrastive learning for Semi-supervised Fault diagnosis under extremely low label Rate. IEEE Transactions on Instrumentation and Measurement.","DOI":"10.1109\/TIM.2023.3284954"},{"key":"2305_CR16","doi-asserted-by":"publisher","first-page":"1772","DOI":"10.1108\/AEAT-10-2020-0234","volume":"94","author":"D Lv","year":"2022","unstructured":"Lv, D., Wang, H., & Che, C. (2022). Semisupervised fault diagnosis of aeroengine based on denoising autoencoder and deep belief network. Aircraft Engineering and Aerospace Technology., 94, 1772.","journal-title":"Aircraft Engineering and Aerospace Technology."},{"key":"2305_CR17","volume-title":"Open-set fault diagnosis via supervised contrastive learning with negative out-of-distribution data augmentation","author":"P Peng","year":"2022","unstructured":"Peng, P., Lu, J., Xie, T., et al. (2022). Open-set fault diagnosis via supervised contrastive learning with negative out-of-distribution data augmentation. IEEE Transactions on Industrial Informatics."},{"key":"2305_CR18","doi-asserted-by":"publisher","first-page":"108397","DOI":"10.1016\/j.asoc.2021.108397","volume":"117","author":"J P\u00f6ppelbaum","year":"2022","unstructured":"P\u00f6ppelbaum, J., Chadha, G. S., & Schwung, A. (2022). Contrastive learning based self-supervised time-series analysis. Applied Soft Computing, 117, 108397.","journal-title":"Applied Soft Computing"},{"issue":"11","key":"2305_CR19","first-page":"2579","volume":"9","author":"L Van der Maaten","year":"2008","unstructured":"Van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9(11), 2579.","journal-title":"Journal of Machine Learning Research"},{"key":"2305_CR20","unstructured":"Wan, W., Chen, J., Zhou, Z., et al. (2022). Self-supervised simple siamese Framework for Fault diagnosis of rotating Machinery with unlabeled Samples. IEEE Transactions on Neural Networks and Learning Systems."},{"key":"2305_CR21","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1109\/SDPC55702.2022.9915840","volume-title":"2022 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","author":"S Xie","year":"2022","unstructured":"Xie, S., Cheng, W., & Nie, Z. (2022). Supervised Contrastive Learning with Multi-scale Attention Mechanism for Fault Diagnosis of Bearing under Variable Operating Conditions. 2022 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) (pp. 132\u2013138). IEEE."},{"issue":"15","key":"2305_CR22","doi-asserted-by":"publisher","first-page":"2796","DOI":"10.3390\/math10152796","volume":"10","author":"Z Yan","year":"2022","unstructured":"Yan, Z., Liu, H., & SMoCo. (2022). A powerful and efficient method based on self-supervised learning for Fault diagnosis of Aero-Engine Bearing under Limited Data. Mathematics, 10(15), 2796.","journal-title":"Mathematics"},{"key":"2305_CR23","doi-asserted-by":"publisher","first-page":"111564","DOI":"10.1016\/j.measurement.2022.111564","volume":"199","author":"T Yang","year":"2022","unstructured":"Yang, T., Tang, T., Wang, J., et al. (2022). A novel cross-domain fault diagnosis method based on model Agnostic meta-learning. Measurement, 199, 111564.","journal-title":"Measurement"},{"key":"2305_CR24","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.neucom.2021.12.094","volume":"476","author":"B You","year":"2022","unstructured":"You, B., Arenz, O., Chen, Y., et al. (2022). Integrating contrastive learning with dynamic models for reinforcement learning from images. Neurocomputing, 476, 102\u2013114.","journal-title":"Neurocomputing"},{"issue":"5","key":"2305_CR25","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.2514\/2.6050","volume":"18","author":"M Zedda","year":"2002","unstructured":"Zedda, M., & Singh, R. (2002). Gas turbine engine and sensor fault diagnosis using optimization techniques. Journal of Propulsion and Power, 18(5), 1019\u20131025.","journal-title":"Journal of Propulsion and Power"},{"key":"2305_CR26","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.isprsjprs.2022.07.013","volume":"191","author":"Q Zeng","year":"2022","unstructured":"Zeng, Q., & Geng, J. (2022). Task-specific contrastive learning for few-shot remote sensing image scene classification. ISPRS Journal of Photogrammetry and Remote Sensing, 191, 143\u2013154.","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"key":"2305_CR27","doi-asserted-by":"publisher","first-page":"107311","DOI":"10.1016\/j.ast.2021.107311","volume":"121","author":"YP Zhao","year":"2022","unstructured":"Zhao, Y. P., & Chen, Y. B. (2022). Extreme learning machine based transfer learning for aero engine fault diagnosis. Aerospace Science and Technology, 121, 107311.","journal-title":"Aerospace Science and Technology"},{"key":"2305_CR28","doi-asserted-by":"publisher","first-page":"101535","DOI":"10.1016\/j.aei.2022.101535","volume":"51","author":"M Zhao","year":"2022","unstructured":"Zhao, M., Fu, X., Zhang, Y., et al. (2022). Highly imbalanced fault diagnosis of mechanical systems based on wavelet packet distortion and convolutional neural networks. Advanced Engineering Informatics, 51, 101535.","journal-title":"Advanced Engineering Informatics"},{"issue":"10","key":"2305_CR29","doi-asserted-by":"publisher","first-page":"10573","DOI":"10.1109\/TIE.2022.3140403","volume":"69","author":"T Zhang","year":"2022","unstructured":"Zhang, T., Chen, J., He, S., et al. (2022). Prior knowledge-augmented self-supervised feature learning for few-shot intelligent fault diagnosis of machines. IEEE Transactions on Industrial Electronics, 69(10), 10573\u201310584.","journal-title":"IEEE Transactions on Industrial Electronics"},{"key":"2305_CR30","doi-asserted-by":"publisher","first-page":"29857","DOI":"10.1109\/ACCESS.2020.2972859","volume":"8","author":"S Zhang","year":"2020","unstructured":"Zhang, S., Zhang, S., Wang, B., et al. (2020). Deep learning algorithms for bearing fault diagnostics\u2014A comprehensive review. IEEE Access, 8, 29857\u201329881.","journal-title":"IEEE Access"},{"key":"2305_CR31","doi-asserted-by":"publisher","first-page":"109437","DOI":"10.1016\/j.knosys.2022.109437","volume":"252","author":"J Zhang","year":"2022","unstructured":"Zhang, J., Zou, J., Su, Z., et al. (2022). A class-aware supervised contrastive learning framework for imbalanced fault diagnosis. Knowledge-Based Systems, 252, 109437.","journal-title":"Knowledge-Based Systems"},{"key":"2305_CR32","doi-asserted-by":"publisher","first-page":"3116","DOI":"10.1002\/qre.3113","volume":"38","author":"S Zhong","year":"2022","unstructured":"Zhong, S., Liu, D., Lin, L., et al. (2022). CAE-WANN: A novel anomaly detection method for gas turbines via search space extension. Quality and Reliability Engineering International, 38, 3116.","journal-title":"Quality and Reliability Engineering International"},{"key":"2305_CR33","doi-asserted-by":"publisher","first-page":"111433","DOI":"10.1016\/j.measurement.2022.111433","volume":"199","author":"B Zhong","year":"2022","unstructured":"Zhong, B., Zhao, M., Zhong, S., et al. (2022). Mechanical compound fault diagnosis via suppressing intra-class dispersions: A deep Progressive shrinkage perspective. Measurement, 199, 111433.","journal-title":"Measurement"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-023-02305-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-023-02305-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-023-02305-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T22:29:26Z","timestamp":1738621766000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-023-02305-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,29]]},"references-count":33,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["2305"],"URL":"https:\/\/doi.org\/10.1007\/s10845-023-02305-y","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,29]]},"assertion":[{"value":"22 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}