{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T22:36:24Z","timestamp":1723156584025},"reference-count":50,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T00:00:00Z","timestamp":1661126400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51775245"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"To avoid the potential safety hazards of electric vehicles caused by the mechanical fault deterioration of the in-wheel motor (IWM), this paper proposes an intelligent diagnosis based on double-optimized artificial hydrocarbon networks (AHNs) to identify the mechanical faults of IWM, which employs a K-means clustering and AdaBoost algorithm to solve the lower accuracy and poorer stability of traditional AHNs. Firstly, K-means clustering is used to improve the interval updating method of any adjacent AHNs molecules, and then simplify the complexity of the AHNs model. Secondly, the AdaBoost algorithm is utilized to adaptively distribute the weights for multiple weak models, then reconstitute the network structure of the AHNs. Finally, double-optimized AHNs are used to build an intelligent diagnosis system, where two cases of bearing datasets from Paderborn University and a self-made IWM test stand are processed to validate the better performance of the proposed method, especially in multiple rotating speeds and the load conditions of the IWM. The double-optimized AHNs provide a higher accuracy for identifying the mechanical faults of the IWM than the traditional AHNs, K-means-based AHNs (K-AHNs), support vector machine (SVM), and particle swarm optimization-based SVM (PSO-SVM).<\/jats:p>","DOI":"10.3390\/s22166316","type":"journal-article","created":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T03:49:56Z","timestamp":1661226596000},"page":"6316","source":"Crossref","is-referenced-by-count":5,"title":["Intelligent Diagnosis Based on Double-Optimized Artificial Hydrocarbon Networks for Mechanical Faults of In-Wheel Motor"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-0912-3413","authenticated-orcid":false,"given":"Hongtao","family":"Xue","sequence":"first","affiliation":[{"name":"School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China"}]},{"given":"Ziwei","family":"Song","sequence":"additional","affiliation":[{"name":"School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China"}]},{"given":"Meng","family":"Wu","sequence":"additional","affiliation":[{"name":"Bosch Automotive Products (Suzhou) Co., Ltd., Suzhou 215021, China"}]},{"given":"Ning","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Automotive and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China"}]},{"given":"Huaqing","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1016\/j.mechmachtheory.2019.06.018","article-title":"New teeth surface and back (TSB) modification method for transient torsional vibration suppression of planetary gear powertrain for an electric vehicle","volume":"140","author":"Wang","year":"2019","journal-title":"Mech. Mach. Theory"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5628","DOI":"10.1109\/TVT.2021.3079576","article-title":"A novel strategy of control performance improvement for six-phase permanent magnet synchronous hub motor drives of EVs under new European driving cycle","volume":"70","author":"Chen","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1109\/TFUZZ.2021.3052092","article-title":"A fuzzy system of operation safety assessment using multimodel linkage and multistage collaboration for in-wheel motor","volume":"30","author":"Xue","year":"2022","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, H., Chen, Y., Cai, Y., Chen, L., Li, Y., Sotelo, M.A., and Li, Z. (2022). SFNet-N: An improved SFNet algorithm for semantic segmentation of low-light autonomous driving road scenes. IEEE Trans. Intell. Transp. Syst., 1\u201313.","DOI":"10.1109\/TITS.2022.3177615"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.energy.2019.06.095","article-title":"The application of hybrid energy storage system with electrified continuously variable transmission in battery electric vehicle","volume":"183","author":"Ruan","year":"2019","journal-title":"Energy"},{"key":"ref_6","first-page":"1014","article-title":"Constrained stability control with optimal power management strategy for in-wheel electric vehicles","volume":"233","author":"Najjari","year":"2019","journal-title":"Proc. Inst. Mech. Eng. Part K J. Multi Body Dyn."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1049\/iet-its.2018.5065","article-title":"Coordinated control for path following of two-wheel independently actuated autonomous ground vehicle","volume":"13","author":"Tang","year":"2019","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1088\/1361-6501\/abfef5","article-title":"Intelligent fault diagnosis for rotating machinery based on potential energy feature and adaptive transfer affinity propagation clustering","volume":"32","author":"Li","year":"2021","journal-title":"Meas. Sci. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sun, W., Yao, B., Zeng, N., Chen, B., He, Y., Cao, X., and He, W. (2017). An intelligent gear fault diagnosis methodology using a complex wavelet enhanced convolutional neural network. Materials, 10.","DOI":"10.3390\/ma10070790"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"10773","DOI":"10.1007\/s00521-019-04612-z","article-title":"Intelligent bearing fault diagnosis using PCA\u2013DBN framework","volume":"32","author":"Zhu","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.knosys.2016.10.022","article-title":"A novel intelligent method for bearing fault diagnosis based on affinity propagation clustering and adaptive feature selection","volume":"116","author":"Wei","year":"2017","journal-title":"Knowl. Based Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"110360","DOI":"10.1016\/j.measurement.2021.110360","article-title":"A recursive sparse representation strategy for bearing fault diagnosis","volume":"187","author":"Han","year":"2022","journal-title":"Measurement"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"566057","DOI":"10.3389\/fgene.2020.566057","article-title":"RF-PCA: A new solution for rapid identification of breast cancer categorical data based on attribute selection and feature extraction","volume":"11","author":"Bian","year":"2020","journal-title":"Front. Genet."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1177\/1461348419849279","article-title":"Intelligent fault diagnosis of rotating machinery based on deep learning with feature selection","volume":"39","author":"Han","year":"2020","journal-title":"J. Low Freq. Noise Vib. Act. Control."},{"key":"ref_15","unstructured":"Liu, Z., Cai, Y., Wang, H., Chen, L., Gao, H., Jia, Y., and Li, Y. (2021, January 19\u201322). Robust target recognition and tracking of self-driving cars with radar and camera information fusion under severe weather conditions. Proceedings of the IEEE Transactions on Intelligent Transportation Systems, Indianapolis, IN, USA."},{"key":"ref_16","first-page":"1091548","article-title":"An improved lagrange particle swarm optimization algorithm and its application in multiple fault diagnosis","volume":"2020","author":"Lv","year":"2020","journal-title":"Shock. Vib."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4275","DOI":"10.3233\/JIFS-189688","article-title":"An intelligent fault diagnosis method based on curve segmentation and SVM for rail transit turnout","volume":"41","author":"Ji","year":"2021","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5947","DOI":"10.21595\/jve.2017.18413","article-title":"Sequential fault detection for sealed deep groove ball bearings of in-wheel motor in variable operating conditions","volume":"19","author":"Xue","year":"2018","journal-title":"J. Vibroeng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1515\/pomr-2017-0123","article-title":"Research on intelligent diagnosis method for large-scale ship engine fault in non-deterministic environment","volume":"24","author":"Feng","year":"2017","journal-title":"Pol. Marit. Res."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lin, T., Wang, H., Guo, X., Wang, P., and Song, L. (2022). A novel prediction network for remaining useful life of rotating machinery. Int. J. Adv. Manuf. Technol., 1\u201310.","DOI":"10.21203\/rs.3.rs-917030\/v1"},{"key":"ref_21","first-page":"3488","article-title":"Intelligent fault diagnosis of rotor-bearing system under varying working conditions with modified transfer convolutional neural network and thermal images","volume":"17","author":"Lin","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.ymssp.2017.09.026","article-title":"A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders","volume":"102","author":"Shao","year":"2018","journal-title":"Mech. Syst. Signal Processing"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"106796","DOI":"10.1016\/j.knosys.2021.106796","article-title":"Intelligent fault diagnosis of rotating machinery based on continuous wavelet transform-local binary convolutional neural network","volume":"216","author":"Cheng","year":"2021","journal-title":"Knowl. Based Syst."},{"key":"ref_24","first-page":"3522709","article-title":"A high-stability diagnosis model based on a multiscale feature fusion convolutional neural network","volume":"70","author":"Wang","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_25","first-page":"3516108","article-title":"Multi-task learning-based self-attention encoding atrous convolutional neural network for remaining useful life prediction","volume":"71","author":"Wang","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sun, G.-D., Wang, Y.-R., Sun, C.-F., and Jin, Q. (2019). Intelligent detection of a planetary gearbox composite fault based on adaptive separation and deep learning. Sensors, 19.","DOI":"10.3390\/s19235222"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Gong, W., Chen, H., Zhang, Z., Zhang, M., Wang, R., Guan, C., and Wang, Q. (2019). A novel deep learning method for intelligent fault diagnosis of rotating machinery based on improved CNN-SVM and multichannel data fusion. Sensors, 19.","DOI":"10.3390\/s19071693"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.ymssp.2018.03.011","article-title":"A novel tracking deep wavelet auto-encoder method for intelligent fault diagnosis of electric locomotive bearings","volume":"110","author":"Haidong","year":"2018","journal-title":"Mech. Syst. Signal Processing"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1560","DOI":"10.1177\/0954406216675896","article-title":"Bearing fault diagnosis with auto-encoder extreme learning machine: A comparative study","volume":"231","author":"Mao","year":"2017","journal-title":"Proc. Inst. Mech. Eng. Part C: J. Mech. Eng. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"114685","DOI":"10.1109\/ACCESS.2019.2935770","article-title":"Real-time diagnosis of an in-wheel motor of an electric vehicle based on dynamic Bayesian networks","volume":"7","author":"Xue","year":"2019","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2584","DOI":"10.1109\/TPEL.2020.3012964","article-title":"Active model-based fault diagnosis in reconfigurable battery systems","volume":"36","author":"Schmid","year":"2021","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"108070","DOI":"10.1016\/j.apacoust.2021.108070","article-title":"Fault diagnosis of angle grinders and electric impact drills using acoustic signals","volume":"179","author":"Glowacz","year":"2021","journal-title":"Appl. Acoust."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4503613","DOI":"10.1109\/TIM.2021.3065438","article-title":"YOLOv4-5D: An effective and efficient object detector for autonomous driving","volume":"70","author":"Cai","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3137","DOI":"10.1109\/TIE.2016.2519325","article-title":"An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data","volume":"63","author":"Lei","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, W., Wang, M., Li, J., Song, L., and Hao, Y. (2019). A novel signal separation method based on improved sparse non-negative matrix factorization. Entropy, 21.","DOI":"10.3390\/e21050445"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"101321","DOI":"10.1016\/j.aei.2021.101321","article-title":"Automatic representation and detection of fault bearings in in-wheel motors under variable load conditions","volume":"49","author":"Wang","year":"2021","journal-title":"Adv. Eng. Inform."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/j.isatra.2021.03.015","article-title":"Intelligent diagnosis of mechanical faults of in-wheel motor based on improved artificial hydrocarbon networks","volume":"120","author":"Xue","year":"2022","journal-title":"ISA Trans."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ponce, H., Miralles-Pechu\u00e1n, L., and Mart\u00ednez-Villase\u00f1or, M. (2016). A flexible approach for human activity recognition using artificial hydrocarbon networks. Sensors, 16.","DOI":"10.3390\/s16111715"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ponce, H., Martinez-Villasenor, M., and Miralles-Pechuan, L. (2016). A novel wearable sensor-based human activity recognition approach using artificial hydrocarbon networks. Sensors, 16.","DOI":"10.3390\/s16071033"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ponce, H. (2016). A novel artificial hydrocarbon networks based value function approximation in hierarchical reinforcement learning. Mexican International Conference on Artificial Intelligence, Springer.","DOI":"10.1007\/978-3-319-62428-0_18"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"107043","DOI":"10.1016\/j.ymssp.2020.107043","article-title":"A multi-stage semi-supervised learning approach for intelligent fault diagnosis of rolling bearing using data augmentation and metric learning","volume":"146","author":"Yu","year":"2021","journal-title":"Mech. Syst. Signal Processing"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.infrared.2016.12.003","article-title":"Diagnosis of the three-phase induction motor using thermal imaging","volume":"81","author":"Glowacz","year":"2017","journal-title":"Infrared Phys. Technol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"103631","DOI":"10.1016\/j.engappai.2020.103631","article-title":"Fault diagnosis using novel AdaBoost based discriminant locality preserving projection with resamples","volume":"91","author":"He","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_44","first-page":"239","article-title":"Dissolved gas analysis for transformer fault based on learning spiking neural P system with belief AdaBoost","volume":"16","author":"Zhang","year":"2021","journal-title":"Int. J. Unconv. Comput."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"108718","DOI":"10.1016\/j.measurement.2020.108718","article-title":"Motor fault diagnosis using attention mechanism and improved adaboost driven by multi-sensor information","volume":"170","author":"Long","year":"2021","journal-title":"Measurement"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Lessmeier, C., Kimotho, J., Zimmer, D., and Sextro, W. (2016, January 5\u20137). Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification. Proceedings of the European Conference of the Prognostics and Health Management Society (PHM Society), Bilbao, Spain.","DOI":"10.36001\/phme.2016.v3i1.1577"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.renene.2015.12.010","article-title":"Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals","volume":"89","author":"Chen","year":"2016","journal-title":"Renew. Energy"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"114002","DOI":"10.1088\/1361-6501\/ac8275","article-title":"Diagnosis method based on hidden Markov model and Weibull mixture model for mechanical faults of in-wheel motor","volume":"33","author":"Xue","year":"2022","journal-title":"Meas. Sci. Technol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2135","DOI":"10.21595\/jve.2016.16712","article-title":"A fuzzy diagnosis of multi-fault state based on information fusion from multiple sensors","volume":"18","author":"Xue","year":"2016","journal-title":"J. Vibroeng."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.neucom.2018.05.002","article-title":"A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing","volume":"313","author":"Yan","year":"2018","journal-title":"Neurocomputing"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/16\/6316\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,4]],"date-time":"2024-08-04T07:48:15Z","timestamp":1722757695000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/16\/6316"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,22]]},"references-count":50,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["s22166316"],"URL":"https:\/\/doi.org\/10.3390\/s22166316","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,8,22]]}}}