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

Published: 22 Oct 2019 | Version 1 | DOI: 10.17632/z4s9bx4wrn.1
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Description of this data

(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).

Experiment data files

This data is associated with the following publication:

A new intelligent fault identification method based on transfer locality preserving projection for actual diagnosis scenario of rotating machinery

Published in: Mechanical Systems and Signal Processing

Latest version

  • Version 1

    2019-10-22

    Published: 2019-10-22

    DOI: 10.17632/z4s9bx4wrn.1

    Cite this dataset

    Xu, Minqiang (2019), “Data for: A New Intelligent Fault Identification Method Based on Transfer Locality Preserving Projection for Actual Diagnosis Scenario of Rotating Machinery”, Mendeley Data, v1 http://dx.doi.org/10.17632/z4s9bx4wrn.1

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System Fault Diagnosis

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The files associated with this dataset are licensed under a Attribution-NonCommercial 3.0 Unported licence.

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You are free to adapt, copy or redistribute the material, providing you attribute appropriately and do not use the material for commercial purposes.

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