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Remaining useful life estimation in aeronautics: Combining data-driven and Kalman filtering

机译：航空中的剩余使用寿命估算：结合数据驱动和卡尔曼滤波

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摘要

Data-driven prognostics can be described in two sequential steps: a training stage, in which, the data-driven model is constructed based on observations; and a prediction stage, in which, the model is used to compute the end of life and remaining useful life of systems. Often, these predictions are noisy and difficult to integrate. A technique well known for its integrative and robustness abilities is the Kalman filter. In this paper we study the applicability of the Kalman filter to filter the estimates of remaining useful life. Using field data from an aircraft bleed valve we conduct a number of real case experiments investigating the performance of the Kalman filter on five data-driven prognostics approaches: generalized linear models, neural networks, k-nearest neighbors, random forests and support vector machines. The results suggest that Kalman-based models are better in precision and convergence. It was also found that the Kalman filtering technique can improve the accuracy and the bias of the original regression models near the equipment end of life. Here, the approach with the best overall improvement was the nearest neighbors, which suggests that Kalman filters may work the best for instance-based methods. (C) 2018 Elsevier Ltd. All rights reserved.
机译：数据驱动的预测可以分为两个顺序的步骤：训练阶段，其中，基于观察结果构建数据驱动模型；在预测阶段，使用该模型来计算系统的使用寿命和剩余使用寿命。通常，这些预测是嘈杂的，难以整合。卡尔曼滤波器是一种以其综合性和鲁棒性而著称的技术。在本文中，我们研究了卡尔曼滤波器对剩余使用寿命的估计进行滤波的适用性。利用飞机放气阀的现场数据，我们进行了许多实际案例实验，研究了卡尔曼滤波器在五种数据驱动的预测方法上的性能：广义线性模型，神经网络，k近邻，随机森林和支持向量机。结果表明，基于卡尔曼模型的精度和收敛性更好。还发现，卡尔曼滤波技术可以提高接近设备使用寿命的原始回归模型的准确性和偏差。在这里，整体改进效果最好的方法是最近的邻居，这表明卡尔曼滤波器对于基于实例的方法可能效果最好。 （C）2018 Elsevier Ltd.保留所有权利。

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