{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T16:20:00Z","timestamp":1764433200362,"version":"build-2065373602"},"reference-count":20,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,1,4]],"date-time":"2019-01-04T00:00:00Z","timestamp":1546560000000},"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":["61473192","U1401240","61075086"],"award-info":[{"award-number":["61473192","U1401240","61075086"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003399","name":"Science and Technology Commission of Shanghai Municipality","doi-asserted-by":"publisher","award":["17441901000"],"award-info":[{"award-number":["17441901000"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In order to realize automation of the pollutant emission tests of vehicles, a pedal robot is designed instead of a human-driven vehicle. Sometimes, the actual time-speed curve of the vehicle will deviate from the upper or lower limit of the worldwide light-duty test cycle (WLTC) target curve, which will cause a fault. In this paper, a new fault diagnosis method is proposed and applied to the pedal robot. Since principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and Autoencoder cannot extract feature information adequately when they are used alone, three types of feature components extracted by PCA, t-SNE, and Autoencoder are fused to form a nine-dimensional feature set. Then, the feature set is reduced into three-dimensional space via Treelet Transform. Finally, the fault samples are classified by Gaussian process classifier. Compared with the methods using only one algorithm to extract features, the proposed method has the minimum standard deviation, 0.0078, and almost the maximum accuracy, 98.17%. The accuracy of the proposed method is only 0.24% lower than that without Treelet Transform, but the processing time is 6.73% less than that without Treelet Transform. These indicate that the multi-features fusion model and Treelet Transform method is quite effective. Therefore, the proposed method is quite helpful for fault diagnosis of the pedal robot.<\/jats:p>","DOI":"10.3390\/s19010163","type":"journal-article","created":{"date-parts":[[2019,1,4]],"date-time":"2019-01-04T11:34:26Z","timestamp":1546601666000},"page":"163","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Multi-Features Fusion for Fault Diagnosis of Pedal Robot Using Time-Speed Signals"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0206-8047","authenticated-orcid":false,"given":"Yuhao","family":"Zhu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zeyu","family":"Fu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuang","family":"Fu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Chen","sequence":"additional","affiliation":[{"name":"Shanghai Engineering Research Center of Civil Aircraft Health Monitoring, Shanghai Aircraft Customer Service Co., Ltd., Shanghai 200241, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1301-9870","authenticated-orcid":false,"given":"Qi","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,4]]},"reference":[{"key":"ref_1","unstructured":"(2019, January 02). Limits and Measurement Methods for Emissions from Light-Duty Vehicles. Available online: https:\/\/www.chinesestandard.net\/PDF\/English.aspx\/GB18352.6-2016."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chen, S., Qi, Z., Chen, D.H., Guo, L.F., and Peng, W.W. (2017). Investigation of Bayesian network for reliability analysis and fault diagnosis of complex systems with real case applications. Adv. Mech. Eng., 9.","DOI":"10.1177\/1687814017728853"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1108\/AEAT-05-2014-0057","article-title":"Aircraft ice accretion prediction using neural network and wavelet packet transform","volume":"88","author":"Chang","year":"2016","journal-title":"Aircr. Eng. Aerosp. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1108\/EC-01-2016-0005","article-title":"Fault diagnosis for rolling bearing based on sift-kpca and svm","volume":"34","author":"Cheng","year":"2017","journal-title":"Eng. Comput."},{"key":"ref_5","first-page":"181","article-title":"Review of techniques for fault diagnosis in damaged structure and engineering system","volume":"3","author":"Thatoi","year":"2012","journal-title":"Adv. Mech. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1108\/03321641211200518","article-title":"Classification of power quality disturbances using wavelet packet energy and multiclass support vector machine","volume":"31","author":"Zhang","year":"2013","journal-title":"Int. J. Comput. Math. Electr. Electron. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1162\/jocn.1991.3.1.71","article-title":"Eigenfaces for recognition","volume":"3","author":"Turk","year":"1991","journal-title":"J. Cognit. Neurosci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2797","DOI":"10.1002\/aic.690421011","article-title":"Identification of faulty sensors using principal component analysis","volume":"42","author":"Dunia","year":"2010","journal-title":"AIChE J."},{"key":"ref_9","first-page":"2579","article-title":"Visualizing Data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_10","unstructured":"Linderman, G.C., and Steinerberger, S. (2017). Clustering with t-SNE, Provably. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Review: Deep learning","volume":"521","author":"Lecun","year":"2015","journal-title":"Nature"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1093\/nsr\/nwx110","article-title":"Deep learning for natural language processing: Advantages and challenges","volume":"5","author":"Li","year":"2018","journal-title":"Natl. Sci. Rev."},{"key":"ref_13","first-page":"3371","article-title":"Manzagol, stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion","volume":"11","author":"Vincent","year":"2010","journal-title":"Mach. Learn. Res."},{"key":"ref_14","unstructured":"Akshay, S., Maneet, S., Richa, S., and Mayank, V. (2018). Residual codean autoencoder for facial attribute analysis. Pattern Recognit. Lett., in press."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4257","DOI":"10.1109\/TVT.2014.2361250","article-title":"Treelet-based clustered compressive data aggregation for wireless sensor networks","volume":"64","author":"Zhao","year":"2015","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1017\/S1368980015000294","article-title":"A treelet transform analysis to relate nutrient patterns to the risk of hormonal receptor-defined breast cancer in the european prospective investigation into cancer and nutrition (epic)","volume":"19","author":"Assi","year":"2016","journal-title":"Public Health Nutr."},{"key":"ref_17","first-page":"514","article-title":"Application of plurigaussian simulation to delineate the layout of alteration domains in Sungun copper deposit","volume":"5","author":"Hassan","year":"2013","journal-title":"Cent. Eur. J. Geosci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1038\/nature14541","article-title":"Probabilistic machine learning and artificial intelligence","volume":"521","author":"Ghahramani","year":"2015","journal-title":"Nature"},{"key":"ref_19","first-page":"366","article-title":"KNN fault detection based on principle component research","volume":"32","author":"Yuan","year":"2018","journal-title":"J. Shenyang Univ. Chem. Technol."},{"key":"ref_20","unstructured":"Kohavi, R. (1995, January 20\u201325). A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. Proceedings of the International Joint Conference on Artificial Intelligence, Montreal, QC, Canada."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/1\/163\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:23:36Z","timestamp":1760185416000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/1\/163"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,4]]},"references-count":20,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,1]]}},"alternative-id":["s19010163"],"URL":"https:\/\/doi.org\/10.3390\/s19010163","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,1,4]]}}}