Use of Acoustic Emission and Pattern Recognition for Crack Detection of a Large Carbide Anvil

Abstract

Large-volume cubic high-pressure apparatus is commonly used to produce synthetic diamond. Due to the high pressure, high temperature and alternative stresses in practical production, cracks often occur in the carbide anvil, thereby resulting in significant economic losses or even casualties. Conventional methods are unsuitable for crack detection of the carbide anvil. This paper is concerned with acoustic emission-based crack detection of carbide anvils, regarded as a pattern recognition problem; this is achieved using a microphone, with methods including sound pulse detection, feature extraction, feature optimization and classifier design. Through analyzing the characteristics of background noise, the cracked sound pulses are separated accurately from the originally continuous signal. Subsequently, three different kinds of features including a zero-crossing rate, sound pressure levels, and linear prediction cepstrum coefficients are presented for characterizing the cracked sound pulses. The original high-dimensional features are adaptively optimized using principal component analysis. A hybrid framework of a support vector machine with k nearest neighbors is designed to recognize the cracked sound pulses. Finally, experiments are conducted in a practical diamond workshop to validate the feasibility and efficiency of the proposed method.

Damage Analysis of Composite CFRP Tubes Using Acoustic Emission Monitoring and Pattern Recognition Approach

“…For gaining a more detailed insight into the damage monitoring process within the meaning of AE source characterization, it is favorable to incorporate an adequate signal analysis tool. The supervised/unsupervised pattern recognition (UPR) approach [ 16 ] has become a very suitable and promising approach to tackle a wide variety of problems such as fatigue tests [ 17 ], structure health monitoring [ 18 ], and condition assessment of pressure vessels [ 19 ] and pressure components in operation [ 20 ]. Numerous studies [ 21 , 22 , 23 , 24 , 25 , 26 ] have been conducted in order to assess characteristic features of the AE transients originating from various failure mechanism in the CFRP composites such as matrix cracking, delamination, fiber break, and debonding (see Table 1 for further explanation).…”

Feasibility Study of Real-Time Monitoring of Pin Connection Wear Using Acoustic Emission

“…With the recent advances in structural health monitoring (SHM) [2][3][4][5][6][7][8] and nondestructive evaluation (NDE) [9,10], many monitoring and evaluation methods, such as modal characteristics-based methods [11][12][13], active sensing [14][15][16][17][18][19][20], electromechanical impedance [21][22][23][24][25], ultrasonic guided wave [26], active thermography [26][27][28] and vibrothermography [29,30], among others, are available. Among them, acoustic emission technique [31][32][33][34][35][36][37] is an effective nondestructive technique that can characterize the wear process [38,39], and it has been widely used in civil engineering [40][41][42][43], mechanical engineering [44][45][46][47]<...>…”

Activation Function Dynamic Averaging as a Technique for Nonlinear 2D Data Denoising in Distributed Acoustic Sensors

“…The authors of [22] used DAS to study the spectral composition of self-oscillations of aircraft structures. Similar problems frequently arise in various fields of science and technology and are often solved by acoustic emission sensors [23][24][25]. However, this type of sensor has its own limitations.…”