Comparison of classification model for steam trap valve opening sound

Abstract

This research aims to create models for steam trap valve opening sound classification by using two classification methods including support vector machine (SVM) and long short-term memory (LSTM). This study employs five feature extraction methods including zero-crossing rate, spectral centroid, Mel-frequency cepstral coefficient, spectral rolloff, and short-term Fourier transform. The results show that F1 score of SVM and LSTM are equivalent with a value of 66.67%. However, SVM provides higher precision than LSTM with value of 63.64% and 52.94%, respectively. In addition, LSTM gives higher recall than SVM with value of 90.00% and 70.00%, respectively.