Gas face seal status estimation based on acoustic emission monitoring and support vector machine regression

Abstract

The difficulty of knowing the real-time status of gas face seals is the main cause of common problems, including sudden failure, ineffective diagnosis, and unpredictability of service life. This study analyzed the acoustic emission signals generated from experiments, uncovering their features in terms of the frequency distribution, periodic fluctuations, and the behaviors during different operation phases. A new vectorization procedure was designed according to the knowledge of informative acoustic emission features. Based on the vectorization procedure, a support vector machine regression method was applied to develop models predicting the eccentric load on the stator of the seal. Cross-validation was conducted to evaluate the regression performance and search for a proper kernel scale. This study found the informative features of acoustic emissions at different timescales and during different seal operation phases, and particularly the great informative potential of certain segments of the starting and stopping phases. The vectorization and support vector machine regression were shown to be effective in estimating the loads in experiments with cross-validation. Thus, a method for estimating the status of gas face seals based on acoustic emission monitoring was established.