TOOL WEAR MONITORING BASED ON WAVELET TRANSFORM OF RAW ACOUSTIC EMISSION SIGNAL
TOOL WEAR MONITORING BASED ON WAVELET TRANSFORM OF RAW ACOUSTIC EMISSION SIGNAL
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发表时间：
2010
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通讯作者：
K. Jemielniak;J. Kossakowska
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文献类型：
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作者：
K. Jemielniak;J. Kossakowska
摘要 本文提出了一种新的有效方法，用于评估在 Inconel 625 粗车削时从原始 AE 信号的小波系数中提取的信号特征的相关性。使用 22 个不同的小波从带通信号中自动提取了几个有意义的信号特征，并将其用于刀具状态监测。以工具状态评估的准确性作为选择最具指示性的小波以及离散小波变换(DWT)和小波包变换(WPT)分解程度的主要标准。
S u m m a r y The paper presents a new efficient method of evaluation of relevancy of signal features extracted from the wavelet coefficients of raw AE signal while rough turning of Inconel 625. Several meaningful signal features were automatically extracted from band pass signals using 22 different wavelets and used for tool condition monitoring. Accuracy of tool condition evaluation was employed as main criterion for selection of the most indicative wavelets and decomposition level of Discreet Wavelet Transform (DWT) and Wavelet Packet Transform (WPT).