基于随机滤波理论的剩余寿命预测模型是基于状态的维修的重要组成部分. 首先根据设备磨损过程，建立了磨损、金属粒子浓度和剩余寿命三者的函数关系. 进而针对滤波模型基于油液信息进行预测时的局限性，建立了基于油液浓度梯度特征的滤波模型. 此模型无需对监测信息中的换油影响进行线性回归处理，从而减少了误差，并以金属浓度梯度特征建模，完善了状态信息与剩余寿命之间的负相关关系. 然后设计了极大似然参数估计方法，在参数估计过程中考虑了截尾数据对估计值的影响. 最后以某型自行火炮发动机的油液光谱分析数据为例，实现了发动机的剩余寿命预测，结果表明了该模型的可行性和有效性.
Abstract
Residual useful life prediction model based on stochastic filtering is an important part of condition based maintenance. Firstly according to the component wearing process, the functions among wear, metal concentration and residual life were established. Secondly, to the restriction of filtering model when using lubricant analysis data, a filtering model based on metal concentration gradient in lubricant was built up. This prediction model does not need to deal with the oil changes by linear regression, which could influence the lubricant data; thereby some calculation errors are avoided. Another advantage of the model is that the negative correlation between condition data and residual life is more perfect owing to the adoption of metal concentration gradient. Thirdly, a maximum likelihood parameter estimation method was designed, which had considered the truncated data. Finally we took the oil spectral analysis data of a certain artillery engine as an example to carry out the residual useful life prediction of the engine. Results show that the model is practicable and effective.
关键词
剩余寿命 /
滤波模型 /
金属浓度梯度 /
状态信息 /
参数估计
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Key words
residual useful life /
filtering model /
metal concentration gradient /
condition information /
parameter estimation
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中图分类号：
TH17
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脚注
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基金
总装重点预研基金（9140A27020308JB34）
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