Clustering-based pressure vessel self-adaptive leakage detection method

Abstract

The invention provides a clustering-based pressure vessel self-adaptive leakage detection method. The method comprises the following steps of firstly, receiving an acoustic emission wave of a pressurevessel by using an acoustic emission sensor; then carrying out local mean value decomposition on a mixed observation signal, and combining a wavelet threshold to denoise the signal so as to obtain anoriginal signal of an acoustic emission signal; carrying out characteristic extraction on the signal to construct a characteristic vector set of the signal, using a weighted fuzzy C-mean value algorithm to carry out clustering analysis, and combining priori knowledge to identify a leakage mode; and finally, training a classifier by using existing various leakage mode label data and an initial clustering result, and then dynamically updating a diagnosis model by using sample information acquired in real time to construct an online leakage mode adaptive identification model. An unknown sample can be identified in real time in an online mode, and adaptive adjustment can be realized.

Description

technical field [0001] The invention belongs to the field of pressure vessel leakage detection, and relates to a pressure vessel self-adaptive leakage detection method based on clustering. Background technique [0002] Pressure vessels are widely used in my country's industrial gas storage and transportation. Due to long-term running wear and natural aging, leakage failures occur from time to time. The wall of the pressure vessel is under enormous pressure. In the event of an emergency such as leakage, the pressure difference on both sides of the wall will cause the gas to flow rapidly to the low pressure, which will not only cause a huge waste and loss of energy, but if the internal storage It is a poisonous and harmful gas, and it will also threaten the safety of personnel. [0003] For the gas leakage detection of pressure vessels, the existing detection technology usually requires manual intervention, the detection time is long, and the detection accuracy is low, which ...

AI Technical Summary

Problems solved by technology

The wall of the pressure vessel is under enormous pressure. In the event of an emergency such as leakage, the pressure difference on both sides of the wall will cause the gas to flow rapidly to the low pressure, which will not only cause a huge waste and loss of energy, but if the internal storage It is poisonous and harmful gas, and it will threaten the safety of personnel

[0003] For the gas leakage detection of pressure vessels, the existing detection technology usually requires manual intervention, the detection time is long, and the detection accuracy is low, which cannot meet the real-time detection of pressure vessels.

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