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Sensors 2018, 18(2), 386;

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

School of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China
Key Laboratory of Noise and Vibration Research, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
Author to whom correspondence should be addressed.
Received: 25 December 2017 / Revised: 24 January 2018 / Accepted: 26 January 2018 / Published: 29 January 2018
(This article belongs to the Section Sensor Networks)
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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. View Full-Text
Keywords: crack detection; tungsten carbide anvil; acoustic emission; pattern recognition crack detection; tungsten carbide anvil; acoustic emission; pattern recognition

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Chen, B.; Wang, Y.; Yan, Z. Use of Acoustic Emission and Pattern Recognition for Crack Detection of a Large Carbide Anvil. Sensors 2018, 18, 386.

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