Identifying acoustic signature of inflow control valve’s condition Using distributed acoustic sensors
2019
In this paper, we present a novel method to identify the
acoustic signature of Inflow Control Valve’s conditions and
classify them. The proposed method consists of three stages:
preprocessing sounds data, acoustic feature extraction and
multi-class classification. In the preprocessing stage, we applied power normalisation to smooth the acoustic signals and
then fed the normalized acoustic data into feature extraction
algorithms. We analysed the series of acoustic features in
time domain, frequency domain and also in an unsupervised
feature extraction algorithm. In time domain, we performed
an extensive feature statistic analysis by comparing six audio
features and selected the best one. In frequency domain, the
features from wavelet transform was extracted. In addition,
acoustic data is converted to frequency domain by applying
short time Fourier transform and its output fed into Principal Component Analysis algorithm. Our proposed method
combined all extracted features from different methods and
composed the novel feature set. In the last, two classification algorithms, Artificial Neural Network and support
Vector Machine, are implemented to test and validate the
novel feature set. We evaluated our method by performing
an experiment on seven real word datasets and experimental
results demonstrated its superior performance compared to
other method.
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