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A Novel Local Time-Frequency Domain Feature Extraction Method for Tool Condition Monitoring Using S-Transform and Genetic Algorithm

机译：一种新的局部时频域特征提取方法，用于使用S转换和遗传算法进行刀具状况监测

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This paper investigates an online effective method for tool condition monitoring. Acoustic emission signal of a system which is acquired by a sensor mounted to the spindle of the milling machining center is used as the fault indicator because it is easily to be installed, inexpensive and practical for use in industrial environment. Time-frequency analysis is selected for signal processing step based on its ability to reveal time and frequency variant characteristics of faulty signal. S-transform is used as a powerful time-frequency method for this purpose. Because of the high dimension of the time-frequency results, it is desirable to use a local region of interest in time-frequency domain instead of using the entire information, for fast and accurate monitoring and detection when any abnormal/fault operating condition might occur. Such a strategy also helps to reduce the computation cost which is necessary for online applications and improves the interpretation resolution for law quality signals. An optimization method based on genetic algorithm is used for finding the most discriminative local area as the region of interest in time-frequency domain. For feature generation step, a correlation coefficient between each signal and the healthy signal is assigned to the signal using a 2-D correlation analysis. Curve fitting approach is then used to determine a function to approximate the fault value based on the correlation coefficients. Experimental results based on a milling machine under different operating conditions show that this method has a high accuracy for fault detection. It is also concluded that the accuracy of the local feature extraction is higher than the conventional ways.
机译：本文调查了工具状况监测的在线有效方法。由安装到铣削加工中心主轴的传感器获取的系统的声发射信号用作故障指示器，因为它很容易安装，便宜且实用用于工业环境。基于其揭示故障信号的时间和频率变化特性的能力，选择时间频率分析。 S转换用作此目的的强大时频方法。由于时间频率结果的高度，期望在时频域中使用局部感兴趣区域而不是使用整个信息，以便快速准确地监测和检测，何时可能发生任何异常/故障操作条件。这种策略还有助于降低在线应用程序所需的计算成本，并改善了法律质量信号的解释分辨率。基于遗传算法的优化方法用于查找最判别的局域区域作为时频域中的感兴趣区域。对于特征生成步骤，使用2-D相关分析将每个信号与健康信号之间的相关系数分配给信号。然后用于基于相关系数确定近似故障值的曲线拟合方法。在不同操作条件下基于铣床的实验结果表明，该方法具有高精度的故障检测。还得出结论，局部特征提取的准确性高于传统方式。

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