A novel ultrasonic inspection method of the heat exchangers based on circumferential waves and deep neural networks

超声波传感器 声学 超声波检测 波形 连续波 压电 连续小波变换 热交换器 无损检测 压电传感器 材料科学 灵敏度(控制系统) 小波变换 小波 计算机科学 光学 人工智能 物理 电子工程 离散小波变换 工程类 电信 机械工程 量子力学 激光器 雷达
作者
Azamatjon Kakhramon ugli Malikov,Younho Cho,Young H. Kim,Jeongnam Kim,Hyung-Kyu Kim
出处
期刊:Science Progress [SAGE Publishing]
卷期号:106 (1) 被引量:10
标识
DOI:10.1177/00368504221146081
摘要

The heat exchanger (HE) is an important component of almost every energy generation system. Periodic inspection of the HEs is particularly important to keep high efficiency of the entire system. In this paper, a novel ultrasonic water immersion inspection method is presented based on circumferential wave (CW) propagation to detect defective HE. Thin patch-type piezoelectric elements with multiple resonance frequencies were adopted for the ultrasonic inspection of narrow-spaced HE in an immersion test. Water-filled HE was used to simulate defective HE because water is the most reliable indicator of the defect. The HE will leak water no matter what the defect pattern is. Furthermore, continuous wavelet transform (CWT) was used to investigate the received CW, and inverse CWT was applied to separate frequency bands corresponding to the thickness and lateral resonance modes of the piezoelectric element. Different arrangements of intact and leaky HE were tested with several pairs of thin piezoelectric patch probes in various instrumental setups. Also, direct waveforms in the water without HE were used as reference signals, to indicate instrumental gain and probe sensitivity. Moreover, all filtered CW corresponding to resonance modes together with the direct waveforms in the water were used to train the deep neural networks (DNNs). As a result, an automatic HE state classification method was obtained, and the accuracy of the applied DNN was estimated as 99.99%.
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