Remaining Useful Lifetime Prediction Method of Controlled Systems Considering Performance Degradation of Actuator
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摘要: 工程控制系统在运行过程中，由于内外部应力的综合作用以及外部环境等的影响，其部件性能将逐渐退化，最终会导致控制系统失效.然而，由于控制系统中闭环反馈的作用，系统的输出残差可能仍在较小范围内变动，使得早期性能退化这种微小故障难以被检测到，呈现隐含退化的特点.现有文献中，针对此类在闭环反馈控制作用下部件存在隐含退化过程的控制系统剩余寿命（Remaining useful lifetime，RUL）预测问题，鲜有研究.为此，本文针对一类仅考虑执行器性能退化的确定闭环控制系统，提出一种基于解析模型的剩余寿命预测方法.该方法首先基于权值优选粒子滤波算法，利用系统的监测数据在线估计出执行器的隐含退化量，然后在每一个预测时刻通过蒙特卡洛（Monte Carlo，MC）仿真计算得到合理的失效阈值，建立基于该失效阈值的系统失效判断准则，最后将隐含退化量的估计值代入退化模型中外推出剩余寿命分布.惯性平台稳定回路控制系统的仿真实验结果验证了该方法的可行性、有效性.Abstract: When engineering controlled system is operating, the performance of its components will degrade gradually due to the combined effects of internal and external stress, environment and so on, which will eventually lead to the failure of the controlled system. However, due to the closed-loop feedback in the controlled system, the output residual may still change in a small range, making such incipient fault of performance degradation difficult to detect and show a characteristic of hidden. In view of the existing literatures, the researches are still scarce which are on the remaining useful lifetime (RUL) prediction of the controlled system with hidden degradation process under the closed-loop feedback control. To this end, this paper proposes a prediction method of RUL for a class of deterministic closed-loop controlled systems only considering actuator performance degradation, which is based on analytic model. Firstly, the algorithm of weight selected particle filter is used to estimate the hidden variable of the actuator, using the monitoring data of the system on-line. Then, Monte Carlo (MC) simulation is used to obtain a reasonable failure threshold at each predicting moment and a failure criterion is established based on it. Finally, the estimation of the hidden degradation variable is brought into the degradation model to extrapolate the distribution of RUL. The simulation results of stabilization loop controlled system in inertial platform show that the proposed method is feasible and effective.
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Key words:
- Life prediction /
- particle filter /
- controlled system /
- performance degradation /
- reliability /
- stabilization loop
1) 本文责任编委 钟麦英 -
图 1 考虑执行器性能退化的闭环控制系统失效过程
Fig. 1 Failure process of closed-loop controlled system considering performance degradation of an actuator
图 2 变失效阈值和固定失效阈值的对比
Fig. 2 Comparison of variable failure threshold and fixed failure threshold
图 5 惯性平台稳定回路控制系统仿真
Fig. 5 Simulation of stabilization loop controlled system for inertial platform
表 1 惯性平台稳定回路模型
Table 1 Stabilization loop model in inertial platform
系统参数 $ J=0.83\, {\rm kg}\cdot {\rm m}^{2} $ $ L_{{\rm m}} =2.7\, {\rm mH} $ $ R_{{\rm m}} =3.6\, \Omega $ $ K_{{\rm m}} =0.407\, {{\rm N}\cdot {\rm m}}/{{\rm A}} $ $ K_{{\rm e}} =0.478\, {{\rm V}}/({\rm rad}\cdot {\rm s}) $ $ \left| u \right|_{\max } =80\, {\rm V} $ 控制器参数 $ K_{{\rm P}} =10.54 $ $ T_{{\rm I}} =15.58 $ $ T_{{\rm D}} =15.37 $ 初始状态量 $ x_{1} (0)=0 $ $ x_{2} (0)=0 $ $ x_{3} (0)=0 $ $ x_{4} (0)=0.407 $ $ y(0)=0 $ 过程噪声参数 $ q_{1} =0.2 $ $ q_{2} ={\rm 0.00008} $ $ q_{3} ={\rm 0.0000000003} $ 退化过程参数 $ \lambda =-0.000257 $ $ \sigma_{{\rm B}} =0.0000002 $ 
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表 2 基于固定阈值不同时刻预测结果对比
Table 2 Comparison of prediction results based on fixed threshold at different times
预测时刻 10 s 20 s 30 s 40 s 失效阈值 0.12205 0.12205 0.12205 0.12205 真实剩余寿命 36.2 26.2 16.2 6.2 剩余阈值寿命 36.1 26.1 16.1 6.1 $ MSE_{K_{{\rm m}} } $ 5.6135 10.9825 7.6921 3.1061 $ MSE_{e} $ 5.5925 10.5087 7.2746 2.8566
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表 3 基于变阈值不同时刻预测结果对比
Table 3 Comparison of prediction results based on variable threshold at different times
预测时刻 10 s 20 s 30 s 40 s 失效阈值 0.16321 0.17461 0.13787 0.12205 真实剩余寿命 36.2 26.2 16.2 6.2 剩余阈值寿命 33.4 22.9 15.5 6.1 $ MSE_{K_{{\rm m}} } $ 6.7076 6.2822 5.9862 3.1061 $ MSE_{e} $ 8.1668 7.8860 4.0403 2.8566
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