剩余寿命预测对保证复杂结构运行安全具有重大意义,为了提高水工闸门剩余寿命预测精度,提出了一种多特征信息融合的闸门剩余寿命预测方法。首先,采用Gamma过程模拟闸门锈蚀演化过程,通过数值仿真获得闸门因锈蚀引起的应力、自振频率、干湿模态振型等特征参数的退化过程。其次,综合考虑单调性和离散性对特征参数进行筛选,并基于主成分分析法进行特征融合构建健康因子;进一步采用非线性维纳过程对闸门退化过程进行建模,利用粒子滤波方法预测其剩余寿命。最后,结合工程实例及有限元仿真验证了所提方法的可靠性和有效性。结果表明,融合多信息的方法能更加充分地反映闸门的退化状态,预测精度评价指标均方根RMSE为1.395 5,平均绝对误差MAE为1.262 8,方差绝对误差VAE为0.352 8,说明预测达到了较高精度,可为闸门的健康管理和安全评估提供依据。
Abstract
Predicting the remaining useful life (RUL) holds great significance in ensuring the operational safety of complex structures. To enhance the accuracy of RUL prediction for hydraulic gates, we propose a multi-feature information fusion-based approach. Initially, we employ the gamma process to simulate the corrosion evolution of gates and analyze the corrosion-caused degradation of characteristic parameters, including stress, natural vibration frequency, and dry/wet modal shapes through numerical simulations. Subsequently, we screen the feature parameters considering monotonicity and discreteness. We construct a health index by fusing these features based on principal component analysis. To model the gate degradation process, we employ a non-linear Wiener process and utilize the particle filtering method to obtain RUL prediction results for the gate at different operating times. Finally, we validate the reliability and effectiveness of our proposed method through engineering examples and finite element simulations. Our results demonstrate that the fusion of multiple information sources enables a more comprehensive reflection of the gate’s degradation state. The root mean square error (RMSE) of the prediction accuracy evaluation index is 1.395 5, the mean absolute error (MAE) is 1.262 8, and the absolute error of variance (VAE) is 0.352 8, showcasing high accuracy. This method can serve as a basis for gate health management and safety assessment.
关键词
水工钢闸门 /
信息融合 /
剩余寿命预测 /
粒子滤波
Key words
hydraulic steel gate /
information fusion /
remaining useful life prediction /
particle filter
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基金
工业和信息化部智能制造综合标准化与新模式应用项目(2018037);河南省水利厅水利科技攻关项目(GG202068)
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