航空发动机转子轴承运行可靠性评估方法

陈保家; 邱光银; 肖文荣; 田红亮; 陈法法; 赵春华; 刘芙蓉

doi:10.7652/xjtuxb201810006

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航空发动机转子轴承运行可靠性评估方法

更新时间：2025-07-09

- 航空发动机转子轴承运行可靠性评估方法
- An Evaluation Method of Operational Reliability for Aero-Engine Rotor Bearings
- 西安交通大学学报 2018年52卷第10期页码：41-48
- 作者机构：
  
  三峡大学湖北省水电机械设备设计与维护重点实验室,湖北,宜昌,443002
- 作者简介：
- 基金信息：
  
  : 2018-02-02。作者简介: 陈保家(1977-),男,副教授;肖文荣(通信作者),男,讲师。基金项目: 国家自然科学基金资助项目(51775307,51606255);湖北省自然科学基金资助项目(2018CFB671)
- DOI：10.7652/xjtuxb201810006
  中图分类号： TH113.1
- 网络首发：2018-10-10，
  
  纸质出版：2018
- 稿件说明：
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陈保家, 邱光银, 肖文荣, 等. 航空发动机转子轴承运行可靠性评估方法[J]. 西安交通大学学报, 2018,52(10):41-48.

An Evaluation Method of Operational Reliability for Aero-Engine Rotor Bearings[J]. 2018, 52(10): 41-48.
陈保家, 邱光银, 肖文荣, 等. 航空发动机转子轴承运行可靠性评估方法[J]. 西安交通大学学报, 2018,52(10):41-48. DOI： 10.7652/xjtuxb201810006.

An Evaluation Method of Operational Reliability for Aero-Engine Rotor Bearings[J]. 2018, 52(10): 41-48. DOI： 10.7652/xjtuxb201810006.

摘要

为了提高少失效或零失效数据条件下的航空发动机轴承运行可靠性评估精度和可信性

提出了一种基于比例协变量模型(PCM)和Logistic回归模型(LRM)混合的可靠性评估方法。首先对轴承运行过程中的监测数据进行信号分析

提取状态特征指标

结合失效阈值确定设备状态

利用LRM先求解轴承的初始可靠性

进一步求解出轴承的初始故障率和基本协变量函数; 然后与PCM相结合

通过响应协变量和基本协变量函数对系统故障率函数进行不断更新

动态揭示状态监测数据与可靠性的映射关系; 最后利用更新后的故障率函数对航空发动机轴承进行运行可靠性评估。试验结果表明:该方法不需要人为确定基本协变量函数

避免了主观选择差异带入的估计偏差

所确定的寿命误差在5%以内

为少失效或无失效条件下的运行可靠性评估提供了一种新的手段。

Abstract

A new reliability assessment method is proposed to improve the precision and credibility of reliability evaluation for aero-engine rotor bearings under the circumstances of less failure or zero failure data

and the method bases on a hybrid model of proportional covariate model(PCM)and Logistic regression model(LRM). The salient features reflecting equipment degradation process are extracted and selected from existing monitoring data by signal processing technology. These features are used as input of the LRM

and the equipment state data defined by the failure threshold are taken as output of the LRM. The initial reliability is estimated by LRM

and then the system hazard rate function is updated based on the response variables and the basic association variable function through combining with PCM. The mapping relationship between the monitoring data and the bearing reliability is dynamically revealed. Finally

the operational reliability of the aircraft engine bearings is successfully estimated using the updated hazard rate function. Case studies show that the method passes the process of the proportional factor decision between covariate and hazard rate

and avoids the influence coming from the subjective deviation. Its determined life error lies within 5%. It provides a new method for reliability estimation under sparse or zero failure data conditions.

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references

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