Identification of optimal features is necessary for the decision-making models such as the artificial neural network to achieve effective and robust on-line monitoring of cutting tool condition. Most feature selection strategies proposed in the literature are for pattern recognition or classification problems, and not suitable for prognostic problems. This paper applies three parameter suitability metrics introduced in previous similar studies for failure-time analysis and modifies them for failure-process analysis which allows for the unit-wise variation of the component in a population. The suitability of a feature used for cutting tool condition monitoring is determined by its fitness value calculated based on the three metrics. Two types of recurrent neural network are employed to analyze the prognostics ability of the features extracted from multi-sensor signals (acoustics emission, motor current, and vibration) collected from a milling machine under various operating conditions. The analysis results validate that the fitness value of a feature can depict its prognostic ability. It is found that adding more features which share abundant information does not increase the prediction performance but increases the burden on the decision-marking models. In addition, adding features with low fitness values may even deteriorate the prediction.

对于诸如神经网络之类的决策模型而言，最佳特征的识别是必要的，以实现对刀具状态的有效而强大的在线监控。文献中提出的大多数特征选择策略都是针对模式识别或分类问题，而不适合于预后问题。本文将先前类似研究中引入的三个参数适用性度量标准用于故障时间分析，并对其进行修改以进行故障过程分析，以允许总体中组件的单位变化。用于刀具状态监控的特征的适用性取决于根据三个度量标准计算出的适合度。两种类型的递归神经网络用于分析从铣刨机在各种运行条件下收集的多传感器信号（声学发射，电动机电流和振动）提取的特征的预测能力。分析结果验证了特征的适合度值可以描述其预后能力。发现增加共享大量信息的特征不会增加预测性能，但会增加决策标记模型的负担。另外，添加具有较低适应性值的特征甚至可能会使预测变差。分析结果验证了特征的适合度值可以描述其预后能力。发现增加共享大量信息的特征不会增加预测性能，但会增加决策标记模型的负担。另外，添加具有较低适应性值的特征甚至可能会使预测变差。分析结果验证了特征的适合度值可以描述其预后能力。发现增加共享大量信息的特征不会增加预测性能，但会增加决策标记模型的负担。另外，添加具有较低适应性值的特征甚至可能会使预测变差。