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.
Highlights
The mechanical face seal is a commonly used type of shaft end seal
In previous studies,[9] we investigated acoustic emission (AE) generated by a gas face seal, and validated that AEs contain abundant information, allowing for a certain degree of interpretation
The AEs generated from experiments on a gas face seal test rig under different operating conditions were analyzed
Summary
The mechanical face seal is a commonly used type of shaft end seal. As a kind of noncontacting mechanical face seal, the gas face seal is intended to work with faces that are totally separated by gas. In the work being presented, the AEs generated from experiments on a gas face seal test rig were first studied in terms of their frequency distribution, periodic fluctuations, and the behaviors during different operation phases. Because AE monitoring typically has a high sample rate, the features of every individual AE wave (typically the above-mentioned frequency distribution) and the fluctuations with dynamic excitation appeared at distinguishable timescales, which contain their respective information. To consider both timescales, band filters were applied at 170 6 40 kHz, 280 6 40 kHz, and 490 6 40 kHz. the time series of RMS before. The filtered RMS in the proximity of 280 and 490 kHz had a normal level of approximately 10 to 200 mV
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