{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T19:27:23Z","timestamp":1775503643992,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T00:00:00Z","timestamp":1727308800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["52265034"],"award-info":[{"award-number":["52265034"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The reliable operation of scroll compressors is crucial for the efficiency of rotating machinery and refrigeration systems. To address the need for efficient and accurate fault diagnosis in scroll compressor technology under varying operating states, diverse failure modes, and different operating conditions, a multi-branch convolutional neural network fault diagnosis method (SSG-Net) has been developed. This method is based on the Swin Transformer, the Global Attention Mechanism (GAM), and the ResNet architecture. Initially, the one-dimensional time-series signal is converted into a two-dimensional image using the Short-Time Fourier Transform, thereby enriching the feature set for deep learning analysis. Subsequently, the method integrates the window attention mechanism of the Swin Transformer, the 2D convolution of GAM attention, and the shallow ResNet\u2019s two-dimensional convolution feature extraction branch network. This integration further optimizes the feature extraction process, enhancing the accuracy of fault feature recognition and sensitivity to data variability. Consequently, by combining the global and local features extracted from these three branch networks, the model significantly improves feature representation capability and robustness. Finally, experimental results on scroll compressor datasets and the CWRU dataset demonstrate diagnostic accuracies of 97.44% and 99.78%, respectively. These results surpass existing comparative models and confirm the model\u2019s superior recognition precision and rapid convergence capabilities in complex fault environments.<\/jats:p>","DOI":"10.3390\/s24196237","type":"journal-article","created":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T09:46:20Z","timestamp":1727343980000},"page":"6237","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["SSG-Net: A Multi-Branch Fault Diagnosis Method for Scroll Compressors Using Swin Transformer Sliding Window, Shallow ResNet, and Global Attention Mechanism (GAM)"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7787-0593","authenticated-orcid":false,"given":"Zhiwei","family":"Xu","sequence":"first","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zezhou","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanan","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Dang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,26]]},"reference":[{"key":"ref_1","unstructured":"Hareland, M., Hoel, A., Jonsson, S., and Liang, D. (2014, January 14\u201317). Selection of Flapper Valve Steel for High Efficient Compressor. Proceedings of the International Compressor Engineering Conference, West Lafayette, IN, USA."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Dufour, D., Le Noc, L., Tremblay, B., Tremblay, M.N., G\u00e9n\u00e9reux, F., Terroux, M., Vachon, C., Wheatley, M.J., Johnston, J.M., and Wotton, M. (2021). A Bi-Spectral Microbolometer Sensor for Wildfire Measurement. Sensors, 21.","DOI":"10.3390\/s21113690"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kang, S.M., Yang, E.S., Shin, J.U., Park, J.H., Lee, S.D., Ha, J.H., Son, Y.B., and Lee, B.C. (2015, January 7\u20139). Development of High Speed Inverter Rotary Compressor for the Air-conditioning System. Proceedings of the 9th International Conference on Compressors and their Systems, London, UK.","DOI":"10.1088\/1757-899X\/90\/1\/012038"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5788","DOI":"10.1002\/er.4679","article-title":"Analyses of an integrated thermal management system for electric vehicles","volume":"43","author":"Tian","year":"2019","journal-title":"Int. J. Energy Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.ijrefrig.2017.04.026","article-title":"Analyse de la performance d\u2019un syst\u00e8me de pompe \u00e0 chaleur \u00e0 injection de vapeur pour les v\u00e9hicules \u00e9lectriques devant d\u00e9marrer sous temp\u00e9ratures froides","volume":"80","author":"Choi","year":"2017","journal-title":"Int. J. Refrig."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.ijrefrig.2023.03.008","article-title":"The influences of the oil circulation ratio on the performance of a vapor injection scroll compressor in heat pump air conditioning system intended for electrical vehicles","volume":"151","author":"Li","year":"2023","journal-title":"Int. J. Refrig."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1007\/s10973-022-11816-4","article-title":"Investigation of the unsteady characteristic in a scroll compressor of a heat pump system for electric vehicles","volume":"148","author":"Peng","year":"2022","journal-title":"J. Therm. Anal. Calorim."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"13086","DOI":"10.1109\/ACCESS.2020.2966582","article-title":"Fault diagnosis forrolling bearings based on composite multiscale fine-sorteddispersion entropy and SVM with hybrid mutation SCA-HHO algorithm optimization","volume":"8","author":"Fu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.robot.2011.10.003","article-title":"hierarchical multiple-model approach for detection and isolation of robotic actuator faults Robot","volume":"60","author":"Hsiao","year":"2012","journal-title":"Robot. Auton. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"tdac036","DOI":"10.1093\/tse\/tdac036","article-title":"Data-driven technology of fault diagnosis in railway point machines: Review and challenges","volume":"4","author":"Hu","year":"2022","journal-title":"Transp. Saf. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3757","DOI":"10.1109\/TIE.2015.2417501","article-title":"A survey of fault diagnosis and fault-tolerant techniques Part I: Fault diagnosis with model-based and signal-based approaches","volume":"62","author":"Gao","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chen, X., and He, K. (2021, January 19\u201325). Exploring simple Siamese representation learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4436","DOI":"10.1177\/0142331219860279","article-title":"A hybrid approach for measuring the vibrational trend of hydroelectric unit with enhanced multi-scale chaotic series analysis and optimized least squares support vector machine","volume":"41","author":"Fu","year":"2019","journal-title":"Trans. Inst. Meas. Control."},{"key":"ref_14","first-page":"117","article-title":"Fault diagnosis of railway point machine based on improved time-domain multiscale dispersion entropy and support vector machine","volume":"51","author":"Cao","year":"2023","journal-title":"Acta Electron. Sin."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"115102","DOI":"10.1016\/j.enconman.2021.115102","article-title":"Integrated framework of extreme learning machine (ELM) based on improved atom search optimization for short-term wind speed prediction Energy Convers","volume":"252","author":"Hua","year":"2022","journal-title":"Energy Convers. Manag."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"36266","DOI":"10.1109\/ACCESS.2019.2904145","article-title":"A Hybrid Feature Extraction Method with Regularized Extreme Learning Machine for Brain Tumor Classification","volume":"7","author":"Gumaei","year":"2019","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lu, J., Qian, W., Li, S., and Cui, R. (2021). Enhanced K-nearest neighbor for intelligent fault diagnosis of rotating machinery. Appl. Sci., 11.","DOI":"10.3390\/app11030919"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3459665","article-title":"K-Nearest Neighbour Classifiers\u2014A Tutorial","volume":"54","author":"Cunningham","year":"2021","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"107526","DOI":"10.1016\/j.patcog.2020.107526","article-title":"Taherim A generalized weighted distance k-Nearest Neighbor for multi-label problems","volume":"114","author":"Rastinn","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5024","DOI":"10.1109\/JSEN.2018.2830109","article-title":"Fault Feature Selection and Diagnosis of Rolling Bearings Based on EEMD and Optimized Elman AdaBoost Algorithm","volume":"18","author":"Fu","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"109100","DOI":"10.1016\/j.measurement.2021.109100","article-title":"Bearing fault diagnosis based on EMD and improved Chebyshev distance in SDP image","volume":"176","author":"Sun","year":"2021","journal-title":"Measurement"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1016\/j.measurement.2013.08.021","article-title":"Multi-fault diagnosis study on roller bearing based on multi-kernel support vector machine with chaotic particle swarm optimization","volume":"47","author":"Chen","year":"2014","journal-title":"Measurement"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2909","DOI":"10.1016\/j.neucom.2008.06.033","article-title":"Fault diagnosis of power electronic system based on fault gradation and neural network group","volume":"72","author":"Ma","year":"2009","journal-title":"Neurocomputing"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.ymssp.2018.02.016","article-title":"Artificial intelligence for fault diagnosis of rotating machinery: A review","volume":"108","author":"Liu","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"109253","DOI":"10.1016\/j.ress.2023.109253","article-title":"Digital twin-assisted multiscale residual-self-attention feature fusion network for hypersonic flight vehicle fault diagnosis","volume":"235","author":"Dong","year":"2023","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"972","DOI":"10.23919\/cje.2022.00.229","article-title":"Vibration Signal-Based Fault Diagnosis of Railway Point Machines via Double-Scale CNN","volume":"32","author":"Chen","year":"2023","journal-title":"Chin. J. Electron."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"107968","DOI":"10.1016\/j.engappai.2024.107968","article-title":"Global wavelet-integrated residual frequency attention regularized network for hypersonic flight vehicle fault diagnosis with imbalanced data","volume":"132","author":"Dong","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"6339","DOI":"10.1109\/TNNLS.2021.3135877","article-title":"Intelligent fault diagnosis of gearbox under variable working conditions with adaptive intraclass and interclass convolutional neural network","volume":"34","author":"Zhao","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"110071","DOI":"10.1016\/j.ymssp.2022.110071","article-title":"Few-shot learning under domain shift: Attentional contrastive calibrated transformer of time series for fault diagnosis under sharp speed variation","volume":"189","author":"Liu","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"102469","DOI":"10.1016\/j.aei.2024.102469","article-title":"A new feature boosting based continual learning method for bearing fault diagnosis with incremental fault types","volume":"61","author":"He","year":"2024","journal-title":"Adv. Eng. Inform."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.measurement.2017.03.001","article-title":"Vibration based diagnostic of cracks in hybrid ball bearings","volume":"108","author":"Seimert","year":"2017","journal-title":"Measurement"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"(2021). Yang Z, Gjorgjevikj D, Long J, Zi Y, Zhang S, Li C Sparse autoencoder-based multi-head deep neural networks for machinery fault diagnostics with detection of novelties. Chin. J. Mech. Eng., 34, 54.","DOI":"10.1186\/s10033-021-00569-0"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1593","DOI":"10.1016\/j.eswa.2007.08.072","article-title":"A new approach to intelligent fault diagnosis of rotating machinery","volume":"35","author":"Lei","year":"2008","journal-title":"Expert Syst. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"102487","DOI":"10.1016\/j.aei.2024.102487","article-title":"Local maximum instantaneous extraction transform based on extended autocorrelation function for bearing fault diagnosis","volume":"61","author":"Liu","year":"2024","journal-title":"Adv. Eng. Inform."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"102459","DOI":"10.1016\/j.aei.2024.102459","article-title":"Fault diagnosis study of hydraulic pump based on improved symplectic geometry reconstruction data enhancement method","volume":"61","author":"Liu","year":"2024","journal-title":"Adv. Eng. Inform."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"015107","DOI":"10.1088\/1361-6501\/acf874","article-title":"Intelligent prediction of rolling bearing remaining useful life based on probabilistic DeepAR-Transformer model","volume":"35","author":"Deng","year":"2023","journal-title":"Meas. Sci. Technol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3701","DOI":"10.1007\/s00170-021-07385-9","article-title":"New domain adaptation method in shallow and deep layers of the CNN for bearing fault diagnosis under different working conditions","volume":"124","author":"Jin","year":"2021","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Taylor, L., and Nitschke, G. (2018, January 18\u201321). Improving deep learning with generic data augmentation. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, Bengaluru, India.","DOI":"10.1109\/SSCI.2018.8628742"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1613\/jair.1.11192","article-title":"SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary","volume":"61","author":"Fernandez","year":"2018","journal-title":"J. Artif. Intell. Res."},{"key":"ref_40","unstructured":"He, H., Bai, Y., Garcia, E.A., and Li, S. (2008, January 1\u20138). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. Proceedings of the IEEE International Joint Conference on Neural Networks, Hong Kong, China."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"9586","DOI":"10.1109\/TII.2022.3231414","article-title":"Source-free adaptation diagnosis for rotating machinery","volume":"19","author":"Jiao","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"107539","DOI":"10.1016\/j.measurement.2020.107539","article-title":"A simple data augmentation algorithm and a self-adaptive convolutional architecture for few-shot fault diagnosis under different working conditions","volume":"156","author":"Hu","year":"2020","journal-title":"Measurement"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"9747","DOI":"10.1109\/TIE.2019.2953010","article-title":"A Polynomial kernel induced distance metric to improve deep transfer learning for fault diagnosis of machines","volume":"67","author":"Yang","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"11649","DOI":"10.1109\/TIE.2022.3229344","article-title":"Self-training reinforced adversarial adaptation for machine fault diagnosis","volume":"70","author":"Jiao","year":"2022","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4681","DOI":"10.1109\/TII.2019.2943898","article-title":"Deep residual shrinkage networks for fault diagnosis","volume":"16","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"10130","DOI":"10.1109\/TIE.2020.3028821","article-title":"A small sample focused intelligent fault diagnosis scheme of machines via multimodules learning with gradient penalized generative adversarial networks","volume":"68","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1016\/j.neucom.2018.10.109","article-title":"Data augmentation in fault diagnosis based on the Wasserstein generative adversarial network with gradient penalty","volume":"396","author":"Gao","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"110936","DOI":"10.1016\/j.ymssp.2023.110936","article-title":"Bayesian variational transformer: A generalizable model for rotating machinery fault diagnosis","volume":"207","author":"Xiao","year":"2024","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"101890","DOI":"10.1016\/j.aei.2023.101890","article-title":"You can get smaller: A lightweight self-activation convolution unit modified by transformer for fault diagnosis","volume":"55","author":"Fang","year":"2023","journal-title":"Adv. Eng. Inform."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"102075","DOI":"10.1016\/j.aei.2023.102075","article-title":"Fault transfer diagnosis of rolling bearings across multiple working conditions via subdomain adaptation and improved vision transformer network","volume":"57","author":"Liang","year":"2023","journal-title":"Adv. Eng. Inform."},{"key":"ref_52","first-page":"3169528","article-title":"Signal-transformer: A robust and interpretable method for rotating machinery intelligent fault diagnosis under variable operating conditions","volume":"71","author":"Tang","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"108616","DOI":"10.1016\/j.ymssp.2021.108616","article-title":"A novel time\u2013frequency Transformer based on self\u2013attention mechanism and its application in fault diagnosis of rolling bearings","volume":"168","author":"Ding","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"102559","DOI":"10.1016\/j.aei.2024.102559","article-title":"Adaptive thresholding and coordinate attention-based tree-inspired network for aero-engine bearing health monitoring under strong noise","volume":"61","author":"Zhao","year":"2024","journal-title":"Adv. Eng. Inform."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3427","DOI":"10.1007\/s00371-023-03043-1","article-title":"Senet: Spatial information enhancement for semantic segmentation neural networks","volume":"40","author":"Huang","year":"2023","journal-title":"Vis. Comput."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"17431","DOI":"10.1109\/JSEN.2021.3062442","article-title":"AVNC: Attention-based VGG-style network for COVID-19 diagnosis by CBAM","volume":"22","author":"Wang","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_57","unstructured":"Park, J., Woo, S., Lee, J.-Y., and Kweon, I.S. (2018). Bam: Bottleneck attention module. arXiv."},{"key":"ref_58","unstructured":"Liu, Y., Shao, Z., and Hoffmann, N. (2021). Global attention mechanism: Retain information to enhance channel-spatial interactions. arXiv."},{"key":"ref_59","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/19\/6237\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:04:08Z","timestamp":1760112248000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/19\/6237"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,26]]},"references-count":59,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["s24196237"],"URL":"https:\/\/doi.org\/10.3390\/s24196237","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,26]]}}}