Reliability assessment and lifetime prediction for train traction system considering multiple dependent components
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摘要:
牵引系统作为城市轨道交通列车的核心，其可靠性对保障列车的运行安全具有重要意义。针对牵引系统结构复杂且失效模式多样的问题，开展可靠性评估与寿命预测研究。着重围绕牵引系统中的关键元件：牵引电机和绝缘栅双极晶体管(IGBT)，构建牵引电机退磁故障和IGBT键合线失效的性能退化模型，采用融合失效机理的维纳过程描述2个元件的性能退化过程，并采用Copula函数描述两者的相关性。对于离线可靠性评估，采用贝叶斯马尔可夫链蒙特卡罗方法进行未知参数估计；对于在线剩余使用寿命预测，采用贝叶斯与期望最大相融合的算法更新模型中的未知参数。基于牵引系统的性能退化试验数据，验证所提模型和算法，结果表明：考虑牵引电机和IGBT 2个元件相关的可靠性模型能够精准实现可靠性评估，采用贝叶斯与期望最大相融合的参数更新算法可有效提升寿命预测精度。
Abstract:The traction system, serving as the power core of urban rail transit trains, plays a crucial role in ensuring the safe operation of the trains. Reliability assessment and lifespan prediction are investigated in order to tackle the difficulties brought about by the traction system’s intricate structure and numerous failure types. The physics of failure model for motor demagnetization and insulated gate bipolar transistor (IGBT) are constructed. The degradation processes for those performance indicators are described by the Wiener process fusing failure mechanism while considering unit-to-unit variability. The Copula function is used to describe the dependent relationship between performance indicators. As for off-line parameter estimation, the Bayesian Markov chain Monte Carlo method estimates unknown parameters. As for online remaining useful life prediction, the algorithm combining Bayesian and expectation-maximization is implemented to update unknown parameters. The proposed model and algorithm are validated by the degradation data of the traction system. The results indicate that the reliability model considering the dependent relationship between the motor and IGBT improves the accuracy of reliability assessment. The remaining useful life prediction accuracy is improved by the parameter updating approach that combines Bayesian and expectation-maximization.
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Key words:
- train traction system /
- Wiener process /
- Copula function /
- reliability /
- lifetime prediction
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图 4 不同模型得到的牵引系统可靠度随时间变化曲线
Figure 4. The reliability curves of traction system based on different models
图 5 实际退化轨迹与估计退化轨迹对比
Figure 5. The comparison between the true degradation path and estimated degradation path
图 6 估计剩余使用寿命与实际剩余使用寿命的概率密度分布对比
Figure 6. The comparisons between the estimated PDF of RUL and true RUL
表 1 模型5未知参数估计结果
Table 1. Unknown parameter estimation results for model 5
牵引电机/IGBT ${\eta _{\mu }}$ $\sigma _{\mu }^2$ $\sigma^2$ 牵引电机 1.1249 0.4582 0.2179 IGBT 1.0950 0.1431 0.1121 注：相关系数θ = 0.8546 。
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表 2 不同算法得到的退化量估计值与实际值的均方误差
Table 2. The MSEs between the estimated degradation path and true degradation path based on different algorithms
算法 均方误差 牵引电机 IGBT 贝叶斯-EM 0.0247 0.0166 EM 0.04682 0.03411
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表 3 不同预测模型得到的剩余使用寿命估计值与实际剩余使用寿命的均方误差
Table 3. The MSEs between the estimated RUL and true RUL based on different prediction models
模型 均方误差 模型1 0.08974 模型2 0.07612 模型3 0.03672
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