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Demo Files for Predictive Maintenance

version (600 KB) by Akira Agata
Demo files for predictive maintenance (PdM)


Updated 20 Mar 2018

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Rare events prediction in complex technical systems has been very interesting and critical issue for many industrial and commercial fields due to huge increase of sensors and rapid growth of Internet of Things (IoT). To detect anomalies and foresee machine failure during normal operation, various types of Predictive Maintenance (PdM) techniques have been studied. Among these techniques, unsupervised anomaly detection methods for multi-dimensional data set would be of more interest in many practical cases. So, in this demo, I have selected following three typical methods.
1. Htelling's T-square method
2. Gaussian mixture model
3. One-class SVM
To emulate a realistic situation, in this demo, I will use the dataset provided by C-MAPSST (Commercial Modular Aero-Propulsion SystemSimulation) [1, 2].
[1] A. Saxena, K. Goebel, D. Simon and N. Eklund, "Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation," International Conference on Prognostics and Health Management, (2008).
[2] Turbofan Engine Degradation Simulation Data Set,

Comments and Ratings (7)

This work is incredible.

Liang Duan

Pa One

Fantastic implementation. I am trying to implement to actual aircraft data. But the functions do not work on my MATLAB versions (2011A). Could you please guide for older MATLAB versions?

marwen Amir

good job , but i really didn't anderstand the use of algorithmes ,what are our outputs ,also is there a documentation about this algorithms ,and as i said good work bro ;)

liming wu



- Updated the link of the Turbofan Engine Degradation Simulation Data Set
- Updated the table in the summary section of Demo0_PreProcessing.m

Update demo scripts.

MATLAB Release Compatibility
Created with R2017a
Compatible with any release
Platform Compatibility
Windows macOS Linux