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In order to overcome these problems, in this paper an improved multi-linear subspace (MLS) method and locally linear embedding (LLE) are integrated (MLSLLE) to extract significant features. To obtain more information, first it is suggested that multiple sensors should be used to sample the vibration signal of a machine from different positions; then, these data are projected into different subspaces, where each sample is represented as a tensor form, respectively; finally, higher-order singular value decomposition and LLE are introduced to extract significant features. Thus a fault diagnosis method is proposed based on MLSLLE and support vector machines. The advantages of the proposed fault diagnosis method are validated by two real bearing data sets. <\/jats:p>","DOI":"10.1177\/0142331217739688","type":"journal-article","created":{"date-parts":[[2018,5,24]],"date-time":"2018-05-24T19:28:47Z","timestamp":1527190127000},"page":"4014-4026","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Machinery fault diagnosis via an improved multi-linear subspace and locally linear embedding"],"prefix":"10.1177","volume":"40","author":[{"given":"Yansheng","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin Institute of Technology, Harbin, P. R. China"},{"name":"School of Electrical and Information Engineering, Northeast Petroleum University, Daqing, P. R. 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