Objective The performance diagnosis of ultrasonic flowmeter for natural gas metering mainly adopts the diagnostic method of setting parameter threshold value, which has some problems such as false alarm and missing alarm. This study establishes a set of ultrasonic flowmeter health state system diagnosis method based on generated adjoint neural network (GAN) and high-dimensional nonlinear unsupervised learning.
Methods GAN is used to learn, generate and expand the original data, so as to ensure the data basis of ultrasonic flowmeter health state diagnosis and modeling. Health state parameters of ultrasonic flowmeter during operation are extracted and time sequence analysis is carried out. Based on high-dimensional nonlinear unsupervised clustering learning method and combined with ultrasonic flowmeter failure mode analysis, the online health status diagnosis of ultrasonic flowmeter equipment was carried out.
Results The fault data set was generated for verification based on the mechanism of ultrasonic flowmeter and the actual data collected on site.
Conclusion The method can accurately identify the current state of ultrasonic flowmeter, significantly solve the problems of high false alarm rate and missalarm rate of traditional threshold method, and support the unified management and development of ultrasonic flowmeter health diagnosis.
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