基于小波包能谱的磨损监测信号敏感特征提取

Weiwei Duan, W. Dai, Shih-Mine Guo, Wei Shi, Tong Li
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引用次数: 0

摘要

刀具状态监测是制造过程质量改进的关键问题,刀具状态信息的来源众多。力信号、振动信号和声发射信号被广泛认为是识别刀具磨损状况的有效手段,但仍然难以避免信息冗余的困境。因此,为了提取刀具磨损的有效信息,本文提出了一种基于小波包能谱的铣削过程敏感频段识别方法。首先,提出小波包将振动信号分解成多个频段;此外,还提出了小波奇异熵来选择合适的分解参数,从而有效地提取弱振动信号。然后,从分解的频带中获得能量信息作为特征参数。然后利用Pearson相关分析识别刀具磨损敏感频段。最后,利用PHM2010数据集验证了所提方法的可行性和有效性,结果表明所提方法在刀具磨损敏感频段识别中的适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sensitive Features Extraction of Wear Monitoring Signals Based on Wavelet Packet Energy Spectrum
Tool condition monitoring is an essential issue in manufacturing process quality improvement, and there exist numerous sources of tool condition information. Force signals, vibration signals and acoustic emission signals are widely considered to be effective for identifying tool wear conditions, but the dilemma of redundant information is still hardly avoided. Therefore, to extract effective information of tool wear, this paper proposes a method to identify sensitive frequency band in the milling process based on wavelet packet energy spectrum. First, wavelet packet is proposed to decompose the vibration signal into multiple frequency bands. In addition, wavelet singular entropy is proposed to select appropriate decomposition parameters as well, so that weak vibration signals can be effectively extracted. Subsequently, the energy information is obtained from the decomposed frequency bands as characteristic parameters. Then identify the frequency bands sensitive to tool wear with Pearson correlation analysis. Finally, PHM2010 datasets are used to verify the feasibility and effectiveness of the proposed method, and the results demonstrate the applicability of the proposed method in practice for sensitive frequency band identification of tool wear.
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