Operating Condition Information-Based Reliability Prediction of Cutting Tool

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Operating Condition Information-Based Reliability Prediction of Cutting Tool

更新时间：2025-07-09

- Operating Condition Information-Based Reliability Prediction of Cutting Tool
- Vol. 44, Issue 9, Pages: 74-77+121(2010)
- 作者机构：
  
  西安交通大学机械制造系统工程国家重点实验室,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TG659;TP31
- Online First：10 September 2010，
  
  Published：2010
- 稿件说明：
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Operating Condition Information-Based Reliability Prediction of Cutting Tool[J]. 2010, 44(9): 74-77+121.
DOI：

Operating Condition Information-Based Reliability Prediction of Cutting Tool[J]. 2010, 44(9): 74-77+121. DOI：

摘要

针对数控机床类退化失效型设备

提出了一种基于设备运行状态信息的可靠性预测方法

主要包括状态特征指标选取、瞬时可靠度计算以及神经网络预测模型的建立和应用.其中

瞬时可靠度计算是准确预测的关键

结合Bayes方法和KM估计器思想提出的基于状态特征指标比例关联关系的瞬时可靠度算法简单高效.针对刀具加工过程中的磨损量时变数据

以可靠度为评价标准

正确预测出了刀具的失效时间

该过程表明设备状态信息用于可靠性预测的可行性和有效性

是未来可靠性发展的一个重要方向.

Abstract

For the reliability prediction to equipment

such as machine tools

whose failure is mainly due to degradation

a forecasting method based on operating condition information is proposed

which includes extraction of condition characteristic index

computation of instantaneous reliability

construction and application of the neural network prediction model. The key issue of prediction accuracy is the computation of instantaneous reliability. A novel approach incorporating Bayes theorem and Kaplan-Meier(KM)estimator principle is employed to calculate the instantaneous reliability. According to the time-varying data of the tool wear

the trained network is available for forecasting the accurate failure time judged by the criterion of reliability. The results show the feasibility and effectiveness to predict reliability by operating condition information.

关键词

Keywords

references

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CAO H, CHEN X, ZI Y, et al. End milling tool breakage detection using lifting scheme and Mahalanobis distance [J]. International Journal of Machine Tools and Manufacture,2008,48(2):141-151.

HENG A, TAN A, MATHEW J, et al. Intelligent condition-based prediction of machinery reliability [J]. Mechanical Systems and Signal Processing,2009,23(5):1600-1614.

YI J, WANG Q, ZHAO D, et al. BP neural network prediction-based variable-period sampling approach for networked control systems [J]. Applied Mathematics and Computation, 2007,185(2):976-988.

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KAPLAN E L, MEIER P. Nonparametric estimation from incomplete observations [J]. Journal of the American Statistical Association, 1958,53(2):457-481.

SICK B. On-line and indirect tool wear monitoring in turning with artificial neural networks: a review of more than a decade of research [J]. Mechanical Systems and Signal Processing, 2002,16(4):487-546.

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