动态概率模型跟踪性能退化的实时可靠性评估方法

华成 1; 张庆 1; 徐光华 1; 2; 谢俊 1; 李淑智 1

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动态概率模型跟踪性能退化的实时可靠性评估方法

更新时间：2025-07-09

- 动态概率模型跟踪性能退化的实时可靠性评估方法
- Real-Time Reliability Assessment Method Based on Performance Degradation Tracked by Dynamic Probability Model
- 西安交通大学学报 2010年44卷第1期页码：46-50
- 作者机构：
  
  西安交通大学机械工程学院,西安,710049
- 作者简介：
- 基金信息：
  
  : 2009-06-16. 作者简介: 华成(1972-),男,博士生;张庆(联系人),男,讲师. 基金项目: 国家高技术研究发展计划资助项目(2007AA04Z432);苏州市工业科技攻关计划资助项目(SG0729)
- DOI：
  中图分类号： TB114.3
- 网络首发：2010-01-10，
  
  纸质出版：2010
- 稿件说明：
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华成 1, 张庆 1, 徐光华 1, 等. 动态概率模型跟踪性能退化的实时可靠性评估方法[J]. 西安交通大学学报, 2010,44(1):46-50.

Real-Time Reliability Assessment Method Based on Performance Degradation Tracked by Dynamic Probability Model[J]. 2010, 44(1): 46-50.
华成 1, 张庆 1, 徐光华 1, 等. 动态概率模型跟踪性能退化的实时可靠性评估方法[J]. 西安交通大学学报, 2010,44(1):46-50. DOI：

Real-Time Reliability Assessment Method Based on Performance Degradation Tracked by Dynamic Probability Model[J]. 2010, 44(1): 46-50. DOI：

摘要

针对实时可靠性评估中先验信息难以获取、分布假设不符合实际情况的问题

提出一种基于动态概率模型的实时可靠性评估方法.采用Parzen核密度估计构造滑动概率神经网络

使用滑移时间窗进行统计样本的动态选择

以非参数方式实时估计性能退化数据的条件概率分布

将超过失效阈值的分布函数值作为可靠性指标

来实现个体设备无先验信息条件下的可靠性评估.通过对高压水除鳞泵和加热炉风机失效过程数据的分析

验证了该方法的可行性和实用性.

Abstract

In the process of real-time reliability assessment

acquisition of prior information is difficult and distribution hypothesis does not always conform to the actual situation

thus a real-time reliability assessment method based on dynamic probability model is presented. Taking nonparametric kernel estimation method

a moving probability neural networking is constructed

and the sliding time-window technique is used to pick statistical samples respectively and then conditional probability distribution of performance degradation data is estimated. The distribution value of performance degradation data more than failure threshold is regarded as the reliability indicator. The individual equipment reliability assessment can be accomplished without any prior information. The analysis of data from high pressure water descaling pump and heating furnace fan in the process of failure verifies the feasibility and practicability.

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references

KIM Y S, KOLARIK W J. Real-time conditional reliability prediction from on-line tool performance data [J]. International Journal of Production Research, 1992, 30(8): 1831-1844.

周东华,徐正国.工程系统的实时可靠性评估与预测技术 [J]. 空间控制技术与应用,2008,34(4):3-9.

ZHOU Donghua, XU Zhengguo. A survey on real-time reliability evaluation and prediction techniques for engineering systems [J]. Aerospace Control and Application, 2008,34(4):3-9.

GEBRAEEL N Z, LAWLEY M A, LI R, et al. Residual-life distributions from component degradation signals: a Bayesian approach [J]. IIE Transactions, 2005, 37:543-557.

DONG M, HE D. A segmental hidden semi-Markov model(HSMM)-based diagnostics and prognostics framework and methodology [J]. Mechanical System and Signal Processing, 2007, 21: 2248-2266.

LU H, KOLARIK W J, LU S S. Real-time performance reliability prediction [J]. IEEE Transactions on Reliability, 2001, 50(4): 353-357.

赵建印. 基于性能退化数据的可靠性建模与应用研究[D]. 长沙: 国防科技大学信息系统与管理学院, 2005.

张庆,徐光华.基于滑动概率神经网络的早期故障诊断[J].西安交通大学学报,2006,40(9):1036-1040.

ZHANG Qing, XU Guanghua. Incipient fault diagnosis based on moving probabilistic neural network [J]. Journal of Journal of Xi'an Jiaotong University,2006,40(9):1036-1040.

PARZEN E. On estimation of a probability density function and mode [J]. Annals of Mathematical Statistics, 1962, 33(8):1065-1076.

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