In machining process, tool wear is an inevitable consequence which progresses rapidly leading to a catastrophic failure of the system and accidents. Moreover, machinery failure has become more costly and has undesirable consequences on the availability and the productivity. Consequently, developing a robust approach for monitoring tool wear condition is needed to get accurate product dimensions with high quality surface and reduced stopping time of machines. Prognostics and health management has become one of the most challenging aspects for monitoring the wear condition of cutting tools. This study focuses on the evaluation of the current health condition of cutting tools and the prediction of its remaining useful life. Indeed, the proposed method consists of the integration of complex continuous wavelet transform (CCWT) and improved extreme learning machine (IELM). In the proposed IELM, the hidden layer output matrix is given by inverting the Moore–Penrose generalized inverse. After the decomposition of the acoustic emission signals using CCWT, the nodes energy of coefficients have been taken as relevant features which are then used as inputs in IELM. The principal idea is that a non-linear regression in a feature space of high dimension is involved by the extreme learning machine to map the input data via a non-linear function for generating the degradation model. Then, the health indicator is obtained through the exploitation of the derived model which is in turn used to estimate the remaining useful life. The method was carried out on data of the real world collected during various cuts of a computer numerical controlled tool.

中文翻译：

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基于小波变换和改进极限学习机的刀具磨损状态监测

在机械加工过程中，刀具磨损是不可避免的后果，它会迅速发展，导致系统的灾难性故障和事故。此外，机器故障变得更加昂贵并且对可用性和生产率产生不良后果。因此，需要开发一种可靠的方法来监测刀具磨损状况，以获取具有高质量表面和减少机器停机时间的准确产品尺寸。预测和健康管理已成为监测切削刀具磨损状况最具挑战性的方面之一。本研究侧重于评估刀具当前的健康状况并预测其剩余使用寿命。确实，所提出的方法包括复杂连续小波变换（CCWT）和改​​进的极限学习机（IELM）的集成。在提议的 IELM 中，隐藏层输出矩阵是通过对 Moore-Penrose 广义逆求逆给出的。使用CCWT对声发射信号进行分解后，将系数的节点能量作为相关特征，然后作为IELM的输入。主要思想是极限学习机在高维特征空间中进行非线性回归，通过非线性函数映射输入数据以生成退化模型。然后，通过利用导出的模型获得健康指标，该模型又用于估计剩余使用寿命。