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An improved similarity-based prognostic algorithm for RUL estimation using an RNN autoencoder scheme

机译：一种利用RNN AutoEncoder方案的RUL估计的基于改进的基于相似性的预测算法

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摘要

Remaining useful life (RUL) estimation of a degrading system is the major prognostic activity in many industry applications. This paper presents an improved version of the similarity-based curve matching method for the remaining useful life estimation of a mechanical system, which is a companion paper of our previous work on RUL estimations using a bidirectional recurrent neural network (RNN) based autoencoder scheme. We propose a zero-centering rule to tackle the varying initial health across instances (systems) when using the similarity-based health index curve matching technique to identify the training instances that share a similar degradation pattern with the WA instance whose RUL needs to be determined. However, this rule will also induce a significant prediction error, especially when the off-line training instances are abundant, or the true RULs of the on-line test instances are large. Thus, an ensemble approach that integrates the RUL estimations obtained from the similarity-based curve matching techniques, with and without the zero-centering rules, is introduced to increase the robustness and accuracy of proposed method for RUL estimations. We evaluate the prognostic performance of the ensemble algorithm and standalone algorithms on four publicly available turbofan engine degradation datasets. The results demonstrate that the proposed ensemble approach gives more robust and reliable RUL estimations compared to any independent algorithm used on all the studied datasets.
机译：剩余的使用寿命（RUL）降解系统的估计是许多行业应用中的主要预后活动。本文提出了一种改进的基于曲线匹配方法，用于机械系统的剩余使用寿命估计，这是我们使用基于双向复发神经网络（RNN）的AutoEncoder方案的RUL估计工作的伴随文件。我们提出了一个零居中规则，以在使用基于相似性的健康索引曲线匹配技术的情况下识别与vel需要确定的wa实例相似的劣化模式的培训实例时解决跨实例（系统）的零初始健康。 。但是，该规则也将引起显着的预测误差，尤其是当离线训练实例丰富时，或者在线测试实例的真实RUL很大。因此，引入了集成从基于相似性的曲线匹配技术，具有和不具有零中心规则的RUL估计的集合方法，以提高所提出的RUL估计方法的鲁棒性和准确性。我们评估了集合算法和独立算法的预后性能，在四个公共涡轮机发动机劣化数据集中。结果表明，与所有学习数据集上使用的任何独立算法相比，所提出的集合方法提供了更强大和可靠的RUL估算。

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