Signal Processing for Steel Drum Leakage Detection Based on Time-Frequency Domain (2)
Van Persie
Chapter II Acoustic Emission Detection and Signal Processing Technology
2.1 Overview of Acoustic Emission Detection TechnologyAcoustic emission refers to the phenomenon where energy waves or transient elastic waves are generated within a material under internal or external forces. The technique that uses instruments to detect, analyze, record, and characterize acoustic emission signals in order to identify and locate the source is known as acoustic emission detection technology [1]. In this system, sensors capture mechanical vibrations from the component, convert them into digital signals, amplify them through an amplifier, and then send them to a computer for analysis via a signal acquisition card. This process allows for the extraction of key parameters that can indicate the presence and characteristics of a leak in a steel drum. The hardware setup includes a pressure sealing device, sensor, amplifier, signal acquisition card, and a computer, as illustrated in Figure 2.1. The main goal of acoustic emission detection is to extract information about the source, such as the size and nature of the leak. Due to its random and non-stationary nature, the acoustic emission signal from a leaking steel drum contains significant uncertainty, making it a complex signal to analyze.
Figure 2.1 Basic flow chart of steel drum leakage acoustic emission detection
2.2 Acoustic Emission Signal Processing TechnologyEffective signal processing methods are essential to accurately evaluate the condition of a steel drum's leakage. Traditionally, Fourier transform has been widely used in digital signal processing, allowing for the conversion of time-domain signals into frequency-domain representations by decomposing them into sine waves. However, this method lacks time localization, meaning it cannot determine when specific frequency components occur, which limits its effectiveness for non-stationary signals.
Two main approaches are used for analyzing acoustic emission signals: parameter analysis and waveform analysis. Parameter analysis involves examining characteristic parameters of the signal, while waveform analysis focuses on time-frequency processing of the received acoustic emission waveforms. Due to the difficulty in obtaining original waveforms, parameter analysis has traditionally dominated in practical applications because it is simpler, faster, and more intuitive.
Acoustic emission signals can be analyzed in both the time and frequency domains. In the time domain, deterministic signals have predictable amplitudes, whereas random signals exhibit unpredictable variations. Although statistical measures like mean and variance are often used to describe random signals in the time domain, their probability distributions are difficult to obtain. In the frequency domain, deterministic signals are typically processed using Fourier transforms, but for random signals, frequency changes over time must also be considered. Traditional Fourier analysis may not provide accurate results, so alternative methods such as power spectrum estimation and Hilbert-Huang Transform (HHT) are more suitable for analyzing non-stationary acoustic emission signals.
To address the limitations of Fourier transforms, researchers introduced the concept of instantaneous frequency. This approach, based on the Hilbert transform, allows for the calculation of the frequency of non-stationary signals at any given moment. The Hilbert-Huang Transform (HHT), consisting of empirical mode decomposition (EMD) followed by Hilbert transformation, provides a powerful tool for analyzing complex, non-linear, and non-stationary signals. This method was proposed by Huang et al. in 1998 and has since become a key technique in signal processing.
2.3 Summary of this ChapterAcoustic emission detection relies heavily on computer-based signal processing techniques. As computer hardware and software continue to evolve, so does the application of acoustic emission technology across various industries, including power, petroleum, and materials testing. Acoustic emission is a passive, non-destructive method, and the signals acquired are often complex due to factors like transmission media, amplifiers, and sensors. As a result, the actual acoustic source signal is rarely captured directly, leading to challenges in signal analysis. With the advancement of digital signal processing, acoustic emission detection has become increasingly important in industrial diagnostics and fault detection.
[1] Xie Chaoyang. Analysis of acoustic emission signal processing methods [J]. China Science and Technology Information, 2009 (05): 131-132. [2] Jiao Jingpin, He Cunfu, et al. Research progress on pipeline acoustic emission leak detection technology [J]. Nondestructive Testing, 2003 (10): 519-523. [3] Tan Xingqiang. Research on pipeline leakage acoustic emission detection system [D]. Chongqing University Boshuo Paper, 2006. [4] Tan Shanwen, Qin Shuren, et al. Filtering characteristics of Hilbert-Huang transform and its application [J]. Journal of Chongqing University (Natural Science Edition), 2004 (02). [5] Tan Shanwen. Research on multi-resolution Hilbert-Huang transform method [D]. Chongqing University, 2001. [6] Huang NE, Shen Z. The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis [M]. Proceedings of the Royal Society of London, 1998 (454): 903–995. [7] Huang NE, Long SR, Shen Z. The Mechanisms for Frequency Downshift in Nonlinear Wave Evolution [J]. Adv. App. I Mech., 1999 (32): 59–111. [8] Shuai Garden. Research on fault diagnosis and analysis technology of rolling bearings for vehicles [D]. Dalian Jiaotong University, 2008. [9] Zheng Shengfeng. Digital measurement method research and hardware design of time-frequency signals [D]. Xidian University, 2009.ã€Related Links】
Steel drum leakage detection signal processing based on time-frequency domain (1)
Steel drum leakage detection signal processing based on time-frequency domain (2)
Steel drum leakage detection signal processing based on time-frequency domain (3)
Steel drum leakage detection signal processing based on time-frequency domain (4)
Steel drum leakage detection signal processing based on time-frequency domain (5)
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