Investigation of Effectiveness of Statistical Methods to Gas Turbine Vibration Diagnostics and Synthesis of SVM Based Approach
Gas turbine is used for generating electricity since 1939. Gas turbine is a kind of internal combustion engine(IC) which it compress and mix the air and fuel for combustion. Thus, hot gases that produce after the combustion will spin the turbine to generate power. Gas turbine is one of the most w...
Saved in:
| Main Author: | |
|---|---|
| Format: | Final Year Project |
| Language: | English |
| Published: |
IRC
2017
|
| Subjects: | |
| Online Access: | http://utpedia.utp.edu.my/17930/1/Dissertation%2818180%29.pdf http://utpedia.utp.edu.my/17930/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Gas turbine is used for generating electricity since 1939. Gas turbine is a kind
of internal combustion engine(IC) which it compress and mix the air and fuel for
combustion. Thus, hot gases that produce after the combustion will spin the turbine to
generate power. Gas turbine is one of the most widely used technology in power
generation.
This project aims to explore different kind of statistical method in vibration
analysis this understand the theory behind each method which have potential to develop
Support Vector Machine (SVM) based turbine vibration diagnostic model. The
effectiveness of statistical methods in vibration analysis will be evaluated. Lastly, the
statistical analysis results will be used as input to develop SVM based turbine vibration
diagnostic model.
In this paper, the evaluation of effectiveness on statistical vibration analysis
methods will be done by observing the trend of the analysis result plots. Once the
completed the evaluation, the results from the high effectiveness methods will be used
as input to train the SVM model. The SVM model will be applied to estimate the
instantaneous Remaining Useful Life percentage. As a result, two statistical vibration
analysis methods: Feature Extraction and Empirical Mode Decomposition, are effective
enough to show the failure trend clearly. The SVM based diagnostic model is able to
estimate the Remaining Useful Life percentage with above 60% accuracy.
Analysis of the final results show that this SVM based diagnostic model is
comparable to other publication, which the SVM diagnostic model has the possibility
to overestimate the result due to insufficient of available vibration data for model
training. |
|---|