基于时间应力分析的地平仪关键部件故障预测方法研究
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摘要
地平仪是直升机的关键部件之一,其状态直接影响直升机工作的可靠性和战备完好性。在直升机工作过程中,其地平仪受振动、温度等环境应力因素的影响,故障率较高。因此,实现直升机地平仪关键部件故障的有效预测,对于尽早发现其故障苗头、减少或杜绝事故的发生具有重要意义。
研究表明:振动、温度等环境应力历程(时间应力)是导致地平仪关键部件性能退化甚至失效的直接外因,对时间应力进行分析可以有效实现其故障预测。因此,本文以直升机地平仪关键部件为对象,系统地分析时间应力诱发其故障的机理与规律,深入研究基于时间应力分析的直升机地平仪关键部件的故障预测方法。
论文的主要研究内容包括:
1.时间应力诱发地平仪关键部件故障的机理分析与建模
系统地分析了时间应力诱发地平仪关键部件故障的机理和规律;建立了基于随机Petri网的时间应力与损伤关系的动态描述模型,获得了时间应力历程与损伤间的转化关系。
2.地平仪关键部件时间应力特征提取及其损伤计算
以改进的“寿命消耗监控(Life Consumption Monitoring,LCM)技术思路为主线,研究了地平仪静态变流器时间应力数据的等级分析及特征提取方法,然后以金属-氧化物-半导体场效应晶体管(Metal-Oxide-Semiconductor Field-Effect Transitor,MOSFET)为对象,基于热载流子退化模型进行其损伤计算,将时间应力历程转化为组件的损伤信息。
3.基于损伤信息的地平仪关键部件故障预测方法
为了实现地平仪关键部件的故障预测,给出了基于损伤信息的地平仪关键部件故障预测的总体思路,首先利用当前损伤信息进行故障预测;然后以当前损伤信息为基础,同时考虑预测结果的历史变化趋势,提出了基于损伤信息的自回归滑动平均模型(Auto-regression Moving Average,ARMA)故障预测方法。验证结果表明,所提出的方法能够较准确地实现地平仪关键部件的故障预测。
4.故障预测系统设计与实验验证
以某型直升机地平仪静态变流器为对象,设计并实现了其故障预测系统,并进行了试验验证,结果表明,本文所研究方法可以较准确的实现静态变流器的故障预测,为地平仪关键部件的故障预测提供了一条切实可用的途径。
The horizon is one of the key parts of helicopter,its working state directly influences the reliability and the readiness of the helicopter. Because the horizon is affected by such environmental factors as vibration, temperature and so on while the helicopter is in working process, its failure rate is always high. Therefore, it is of great importance to achieve the key components’effective failure prognostics to discover the failure symptom as early and to prevent the occurrence of accidents.
Research indicates that environment stress course such as vibration, temperature is the direct external factor. It causes the performance of horizon’s key components degenerating or even invaliding. The fault prognostics can be achieved effectively by analysis of time stress. Therefore, the mechanism and rule that time stress induces the failure of helicopter horizon are systematically analyzed. The fault prognostics method based on time stress analysis for helicopter horizon is deeply investigated.
The main contents of the research are as follows:
1. The mechanism that time stress induces the horizon’s faults is analyzed and a relative model is built.
Firstly, the mechanism that faults are induced by time stress is analyzed in detail. Secondly, a dynamic relation description model between time stress and damage is built based on Generalized Stochastic Petri Nets (GSPN). Then the evolution relationship between time stress and component’s damage is got.
2. The time stress data feature extraction and calculating method of damage for the key components of the horizon is studied.
Based upon the improved consumption monitoring methodology (LCM), the time stress data grade analysis and feature extraction method for the static converter are studied. Then, based on hot carrier degradation (HCD) model, the MOSFET’s damage is calculated and the time stress is transformed into information of components’damage.
3. The fault prognostics method of helicopter horizon’s key components is studied based on damage information.
To achieve fault prognostics for the horizon’s key components, the overall mentality of horizon key component’s failure prognostics is given based on the damage information. Firstly, the current damage information is used to carry out the failure prognostics. Then, considering the history trend of the prognostics result, a fault prognostics method is put forward based on damage information’s Auto-regression Moving Average (ARMA) model. The experimental result shows that this method can result in accurate fault prognostics for the horizon.
4. A fault prognostics system is designed and fault prognostics technology based on time stress is validated.
In order to evaluate the efficiency of fault prognostics technology, a fault prognostics system for static converter of the horizon is built and implemented. Then, some experiment of time stress is executed on the static converter. According to the test results, the fault prognostics technology in this paper have been proved to be successful. It provides an effective and feasible way for the fault prognostics of the helicopter horizon’s key components in the actual application.
研究表明:振动、温度等环境应力历程(时间应力)是导致地平仪关键部件性能退化甚至失效的直接外因,对时间应力进行分析可以有效实现其故障预测。因此,本文以直升机地平仪关键部件为对象,系统地分析时间应力诱发其故障的机理与规律,深入研究基于时间应力分析的直升机地平仪关键部件的故障预测方法。
论文的主要研究内容包括:
1.时间应力诱发地平仪关键部件故障的机理分析与建模
系统地分析了时间应力诱发地平仪关键部件故障的机理和规律;建立了基于随机Petri网的时间应力与损伤关系的动态描述模型,获得了时间应力历程与损伤间的转化关系。
2.地平仪关键部件时间应力特征提取及其损伤计算
以改进的“寿命消耗监控(Life Consumption Monitoring,LCM)技术思路为主线,研究了地平仪静态变流器时间应力数据的等级分析及特征提取方法,然后以金属-氧化物-半导体场效应晶体管(Metal-Oxide-Semiconductor Field-Effect Transitor,MOSFET)为对象,基于热载流子退化模型进行其损伤计算,将时间应力历程转化为组件的损伤信息。
3.基于损伤信息的地平仪关键部件故障预测方法
为了实现地平仪关键部件的故障预测,给出了基于损伤信息的地平仪关键部件故障预测的总体思路,首先利用当前损伤信息进行故障预测;然后以当前损伤信息为基础,同时考虑预测结果的历史变化趋势,提出了基于损伤信息的自回归滑动平均模型(Auto-regression Moving Average,ARMA)故障预测方法。验证结果表明,所提出的方法能够较准确地实现地平仪关键部件的故障预测。
4.故障预测系统设计与实验验证
以某型直升机地平仪静态变流器为对象,设计并实现了其故障预测系统,并进行了试验验证,结果表明,本文所研究方法可以较准确的实现静态变流器的故障预测,为地平仪关键部件的故障预测提供了一条切实可用的途径。
The horizon is one of the key parts of helicopter,its working state directly influences the reliability and the readiness of the helicopter. Because the horizon is affected by such environmental factors as vibration, temperature and so on while the helicopter is in working process, its failure rate is always high. Therefore, it is of great importance to achieve the key components’effective failure prognostics to discover the failure symptom as early and to prevent the occurrence of accidents.
Research indicates that environment stress course such as vibration, temperature is the direct external factor. It causes the performance of horizon’s key components degenerating or even invaliding. The fault prognostics can be achieved effectively by analysis of time stress. Therefore, the mechanism and rule that time stress induces the failure of helicopter horizon are systematically analyzed. The fault prognostics method based on time stress analysis for helicopter horizon is deeply investigated.
The main contents of the research are as follows:
1. The mechanism that time stress induces the horizon’s faults is analyzed and a relative model is built.
Firstly, the mechanism that faults are induced by time stress is analyzed in detail. Secondly, a dynamic relation description model between time stress and damage is built based on Generalized Stochastic Petri Nets (GSPN). Then the evolution relationship between time stress and component’s damage is got.
2. The time stress data feature extraction and calculating method of damage for the key components of the horizon is studied.
Based upon the improved consumption monitoring methodology (LCM), the time stress data grade analysis and feature extraction method for the static converter are studied. Then, based on hot carrier degradation (HCD) model, the MOSFET’s damage is calculated and the time stress is transformed into information of components’damage.
3. The fault prognostics method of helicopter horizon’s key components is studied based on damage information.
To achieve fault prognostics for the horizon’s key components, the overall mentality of horizon key component’s failure prognostics is given based on the damage information. Firstly, the current damage information is used to carry out the failure prognostics. Then, considering the history trend of the prognostics result, a fault prognostics method is put forward based on damage information’s Auto-regression Moving Average (ARMA) model. The experimental result shows that this method can result in accurate fault prognostics for the horizon.
4. A fault prognostics system is designed and fault prognostics technology based on time stress is validated.
In order to evaluate the efficiency of fault prognostics technology, a fault prognostics system for static converter of the horizon is built and implemented. Then, some experiment of time stress is executed on the static converter. According to the test results, the fault prognostics technology in this paper have been proved to be successful. It provides an effective and feasible way for the fault prognostics of the helicopter horizon’s key components in the actual application.
引文
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[2]曾声奎, Pecht M,吴际.故障预测与健康管理(PHM)技术的现状与发展[J].航空学报, 2005,5: 626~632.
[3]张宝珍,曾天翔.先进的故障预测与状态管理技术[J].测控技术, 2003,11: 4~6.
[4]徐萍,康锐.预测与状态管理系统(PHM)技术研究[J].测控技术, 2004,12: 58~60.
[5] Kalgren P W, Byington C S, Roemer M J, et al. Defining PHM-a lexical evolution of maintenance and logistics[A]. Systems Readiness Technology Conference[C], 2006,9: 353~358.
[6] Orsagh R F, Brown D W, Kalgren P W, et al. Prognostic health management for avionic systems[A]. Proceedings of the IEEE Aerospace Conference, Big Sky, Montana[C], USA. 2006: 1~7.
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