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首页> 外文期刊>Journal of Intelligent Manufacturing >Monitoring of a machining process using kernel principal component analysis and kernel density estimation
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Monitoring of a machining process using kernel principal component analysis and kernel density estimation

机译：使用内核主成分分析和内核密度估计监控加工过程

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摘要

Tool wear is one of the consequences of a machining process. Excessive tool wear can lead to poor surface finish, and result in a defective product. It can also lead to premature tool failure, and may result in process downtime and damaged components. With this in mind, it has long been desired to monitor tool wear/tool condition. Kernel principal component analysis (KPCA) is proposed as an effective and efficient method for monitoring the tool condition in a machining process. The KPCA-based method may be used to identify faults (abnormalities) in a process through the fusion of multi-sensor signals. The method employs a control chart monitoring approach that uses Hotelling's T-2-statistic and Q-statistic to identify the faults in conjunction with control limits, which are computed by kernel density estimation (KDE). KDE is a non-parametric technique to approximate a probability density function. Four performance metrics, abnormality detection rate, false detection rate, detection delay, and prediction accuracy, are employed to test the reliability of the monitoring system and are used to compare the KPCA-based method with PCA-based method. Application of the proposed monitoring system to experimental data shows that the KPCA based method can effectively monitor the tool wear.
机译：工具磨损是加工过程的后果之一。过度的工具磨损可导致表面光洁度差，并导致产品有缺陷。它还可能导致过早的工具故障，可能导致过程停机和损坏的组件。考虑到这一点，它长期以来要监控工具磨损/刀具条件。建议内核主成分分析（KPCA）作为监测加工过程中工具条件的有效和有效的方法。基于KPCA的方法可用于通过多传感器信号的熔合来识别过程中的故障（异常）。该方法采用控制图表监测方法，该方法使用Hotelling的T-2统计和Q型Q型来识别与通过内核密度估计（KDE）计算的控制限制的故障。 KDE是一种非参数化技术，用于近似概率密度函数。使用四种性能度量，异常检测率，假检测率，检测延迟和预测精度来测试监控系统的可靠性，并用于比较基于PCA的方法的基于KPCA的方法。所提出的监测系统在实验数据中的应用表明，基于KPCA的方法可以有效地监测工具磨损。

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