CNC machine tool's wear diagnostic and prognostic by using dynamic Bayesian networks
CNC machine tool's wear diagnostic and prognostic by using dynamic Bayesian networks
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DOI：
10.1016/j.ymssp.2011.10.018
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发表时间：
2012-04-01
影响因子：
8.4
通讯作者：
Zerhouni, N.
中科院分区：
文献类型：
--
作者：
Tobon-Mejia, D. A.;Medjaher, K.;Zerhouni, N.
工业系统中关键部件的故障可能对可用性、生产力、安全性和环境产生负面影响。为了避免这种情况，可以通过使用监测数据执行在线系统诊断和预测，不断评估物理系统的健康状况，特别是其关键组件的健康状况。本文对计算机数控机床的健康状况评估及其剩余使用寿命的估计作出了贡献。所提出的方法依赖于两个主要阶段：离线阶段和在线阶段。在第一阶段，对传感器提供的原始数据进行处理以提取可靠的特征。后者被用作学习算法的输入，以生成代表切削刀具磨损行为的模型。然后，在第二阶段（即评估阶段），利用构建的模型来识别工具的当前健康状态，预测其RUL和相关的置信界限。将该方法应用于数控刀具多次切削过程中采集的状态监测数据的基准测试。最后给出了仿真结果并进行了讨论。(C) 2011 Elsevier Ltd.版权所有。
The failure of critical components in industrial systems may have negative consequences on the availability, the productivity, the security and the environment. To avoid such situations, the health condition of the physical system, and particularly of its critical components, can be constantly assessed by using the monitoring data to perform on-line system diagnostics and prognostics.The present paper is a contribution on the assessment of the health condition of a computer numerical control (CNC) tool machine and the estimation of its remaining useful life (RUL). The proposed method relies on two main phases: an off-line phase and an on-line phase. During the first phase, the raw data provided by the sensors are processed to extract reliable features. These latter are used as inputs of learning algorithms in order to generate the models that represent the wear's behavior of the cutting tool. Then, in the second phase, which is an assessment one, the constructed models are exploited to identify the tool's current health state, predict its RUL and the associated confidence bounds. The proposed method is applied on a benchmark of condition monitoring data gathered during several cuts of a CNC tool. Simulation results are obtained and discussed at the end of the paper. (C) 2011 Elsevier Ltd. All rights reserved.