(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:
 PHM 09 Data Challenge Data. https://www.phmsociety.org/competition/PHM/09/apparatus.
 CWRU bearing data center. http://csegroups.case.edu/bearingdatacenter/pages/12k-drive-end-bearing-fault-data
 Eric Bechhoefer, MFPT Bearing Fault Data Sets. http://mfpt.org/fault-data-sets/.
(3) The toolbox used in the codes are listed below:
 libsvm_3.22. https://www.csie.ntu.edu.tw/~cjlin/libsvm/
 DeepLearnToolbox-master. https://github.com/rasmusbergpalm/DeepLearnToolbox
 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).
First file: matrices used in rotor equations 2(e) for section 4 case study; H_VP is the modal matrix in eq. (4) augmented over 20 points that have z coordinates defined in the 20 by 1 vector z.
Second file: matrices used in the FFSMM equations (10(a)) for section 5 case study; the names of the Matlab variables follow the nomenclature of reference  (Bin Hassan, Bonello (2017) "A new modal-based approach...", Journal of Sound and Vibration).
Third file: matrices used in rotor equations 2(e) for section 6 case study; H_VP is the modal matrix in eq. (4) augmented over 20 points that have z coordinates defined in the 20 by 1 vector z.
The provided zip-file contains the vibration data and speed estimation results for the aircraft engine data set and the wind turbine gearbox data set presented in "Review and comparison of tacholess instantaneous speed estimation methods on experimental vibration data"
Please refer to this paper in case the provided data and results prove useful to your research.
- "Review and comparison of tacholess instantaneous speed estimation methods on experimental vibration data", Cedric Peeters, Quentin Leclere, Jerome Antoni, Peter Lindahl,
John Donnal, Steven Leeb, Jan Helsen, MSSP
Also please refer to following papers if the data is used:
- Surveillance 8 data: Antoni, J., Griffaton, J., André, H., Avendaño-Valencia, L. D., Bonnardot, F., Cardona-Morales, O., ... & Acuña, D. Q. (2017). Feedback on the Surveillance 8 challenge: Vibration-based diagnosis of a Safran aircraft engine. Mechanical Systems and Signal Processing, 97, 112-144.
- CMMNO 2014 data: Leclère, Q., André, H., & Antoni, J. (2016). A multi-order probabilistic approach for Instantaneous Angular Speed tracking debriefing of the CMMNO׳ 14 diagnosis contest. Mechanical Systems and Signal Processing, 81, 375-386.
Contributors:Shaw, Alexander, Hill, Tom, Neild, Simon, Friswell, Michael
Alexander D Shaw; Thomas L Hill; Simon A Neild; Michael I Friswell
Periodic responses of a structure with 3:1 internal resonance
MSSP, in press (as of 18/3/2016)