The aim of this Master Thesis is to investigate if and how acoustic signals can be used for state monitoring of operating hydro power machinery.
In the past decades the manpower in Vattenfall AB’s hydro power facilities has decreased, and with that also the ability of the human hearing to detect irregular sounds in the power plant. There is a need to investigate whether acoustic signals carry information that can be extracted by a sound monitoring system and how such a system could be constructed.
The methods used in this Master Thesis are based on time-frequency analysis and pattern recognition theory. Several algorithms for extracting spectral features were implemented in MATLAB. One method for supervised pattern recognition, the knearest neighbor classifier (k-nn) and two methods for unsupervised pattern recognition, the k-means clustering algorithm and the Density based scan algorithm (dbscan) were implemented in MATLAB for classification and pattern recognition of the calculated spectral features.
Acoustic signals from operating hydro power machinery was recorded at slightly different operating conditions and the implemented methods were applied to the recorded signals. The result showed that the time-frequency analysis and pattern recognition theory could be used to extract information regarding the state of operating hydro power machinery. The most discriminating spectral feature was the Renyi Entropy number. The k-nn classified the different recordings with a 0.9929 success rate.
Source: Lulea University of Technology
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