Data for: A New Intelligent Fault Identification Method Based on Transfer Locality Preserving Projection for Actual Diagnosis Scenario of Rotating Machinery

(1) The data file contains the MATLAB codes and feature data used to implement the results in "A New Intelligent Fault Identification Method Based on Transfer Locality Preserving Projection for Actual Diagnosis Scenario of Rotating Machinery". (2) Only feature data are given. The original data are provided by the following references: [1] PHM 09 Data Challenge Data. https://www.phmsociety.org/competition/PHM/09/apparatus. [2] CWRU bearing data center. http://csegroups.case.edu/bearingdatacenter/pages/12k-drive-end-bearing-fault-data [3] Eric Bechhoefer, MFPT Bearing Fault Data Sets. http://mfpt.org/fault-data-sets/. (3) The toolbox used in the codes are listed below: [1] libsvm_3.22. https://www.csie.ntu.edu.tw/~cjlin/libsvm/ [2] DeepLearnToolbox-master. https://github.com/rasmusbergpalm/DeepLearnToolbox [3] minFunc_2012. https://www.cs.ubc.ca/~schmidtm/Software/minFunc.html (4) The supported platform should have a Windows system, meanwhile the MATLAB version should be R2017b or later version (R2018a is also tested).