Analysis of Condition-based Maintenance Strategy for Stochastic Degradation Devices Under the Influence of Quantization and Measurement Errors
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摘要: 剩余寿命(RUL)预测作为开展设备视情维护(CBM)的前提条件, 已经引起学者和工程师的广泛研究. 现有基于预测信息的CBM策略侧重于设备退化过程非线性和时变不确定性的影响, 鲜有考虑带有量化误差和测量误差的寿命预测信息对维护策略的影响. 鉴于此, 提出一种考虑量化误差和测量误差影响的随机退化设备CBM策略. 首先, 构建一种带有量化误差和测量误差的非线性退化模型框架, 计算首达时间下退化设备的RUL预测信息. 其次, 以设备平均费用率为决策目标, 讨论不同误差参数对CBM策略的影响, 并求解获取设备的最优维护时机和动态检测间隔. 最后, 通过某型惯导系统的陀螺仪退化案例对所提方法的有效性进行实例验证.Abstract: As a prerequisite for implementing devices＇ condition-based maintenance (CBM), remaining useful life (RUL) prediction has garnered significant attention from both scholars and engineers. Existing CBM strategies based on predictive information primarily address the effects of nonlinearity and time-varying uncertainty in the device degradation process, while rarely considering the influence of lifetime prediction information with quantization and measurement errors on maintenance strategies. To address this issue, this paper proposes a CBM strategy for stochastic degradation devices that incorporates the influence of quantization and measurement errors. Firstly, a nonlinear degradation model framework is developed that incorporates quantization and measurement errors, enabling the calculation of RUL prediction information for degraded devices at the first hitting time. Secondly, by adopting the average cost rate of devices as the decision objective, the influence of different error parameters on the CBM strategy is investigated, and the optimal maintenance timing and dynamic inspection intervals of devices are determined through solution. Finally, the effectiveness of the proposed method is validated through a case study of gyroscope degradation in a certain type of inertial navigation system.
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图 3 不同量化误差参数下的寿命PDF
Fig. 3 The PDF of lifetime under different quantization error parameters
图 5 不同测量误差参数下的寿命PDF
Fig. 5 The PDF of lifetime under different measurement error parameters
表 1 不同量化参数对于维护决策的影响
Table 1 The influence of different quantization parameters on maintenance decisions
量化量程$q$ 最优维护时机
$t_{\varepsilon,\;e|p}^{*}$ (h)长期平均维护费用率
$C_{\varepsilon,\;e}^{*}$ (Yuan/h)$C_{\varepsilon,\;e}^{*}-C^*$
(Yuan/h)0.05 104 55.563 6 5.705 4 0.10 103 57.335 0 7.476 8 0.15 102 60.161 0 10.302 8 0.20 102 63.877 1 14.018 9 
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表 2 #1陀螺仪预测性能比较
Table 2 Comparison of predictive performance of #1 gyroscope
方法 score RMSE ${R}^2$ 本文所提方法 274.630 1 14.861 2 0.934 1 文献[31]仅考虑测量误差影响的方法 310.034 5 17.786 1 0.924 0
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表 3 #1陀螺仪维护决策结果
Table 3 The maintenance decision results of #1 gyroscope
监测时间 (h) 最优维护时机
$t_{\varepsilon,\;e|p}^{*}$ (h)长期平均维护费用率
$C_{\varepsilon,\;e}^{*}$ (Yuan/h)判别准则
KC40 128 42.852 7 1.391 2 128 134 37.323 5 1.013 4 134 139 35.990 4 0.850 5
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表 4 #2 陀螺仪预测性能比较
Table 4 Comparison of predictive performance of #2 gyroscope
方法 score RMSE ${R}^2$ 本文所提方法 309.150 7 15.062 8 0.916 2 文献[31]仅考虑测量误差影响的方法 474.061 4 17.266 7 0.903 3
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表 5 #2陀螺仪维护决策结果
Table 5 The maintenance decision results of #2 gyroscope
监测时间
(h)最优维护时机
$t_{\varepsilon,\;e|p}^{*}$ (h)长期平均维护费用率
$C_{\varepsilon,\;e}^{*}$ (Yuan/h)判别准则
$KC$40 110 48.917 4 1.586 4 110 120 44.553 6 1.181 8 120 128 39.2546 0.491 3
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