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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >A similarity-based method for remaining useful life prediction based on operational reliability
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A similarity-based method for remaining useful life prediction based on operational reliability

机译：一种基于相似性的方法，其基于操作可靠性剩余使用寿命预测

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摘要

Remaining useful life (RUL) prediction is an effective method to ensure system safety and avoid catastrophe in applied intelligent perspective. A similarity-based method for RUL prediction based on operational reliability has been proposed in this paper to overcome the shortcomings existing in the conventional similarity-based RUL prediction method of devices. The first shortcoming is the construction difficulty of degradation index (DI) and the second shortcoming is the low accuracy of similarity measurement. First, a novel operational reliability fused index (ORI) was proposed by calculating offset distance and offset angle between current state and normal state of devices, and the index was used as the DI for life prediction. Then, a multi-factor similarity measurement method was proposed to select the most similarity samples and a novel similarity measurement and weight assigned method considering degradation degree was proposed based on the previous similarity measurement. Finally, the RUL was weighted predicted. The effectiveness of the proposed method was validated by implementing two cases.
机译：剩余的使用寿命（RUL）预测是一种有效的方法，可以确保系统安全，避免应用智能视角下的灾难。本文提出了一种基于RUL预测的基于RUL预测方法，以克服设备的传统相似性的RUL预测方法存在的缺点。第一个缺点是降解指数（DI）的施工难度，第二缺点是相似性测量的低精度。首先，通过计算电流状态和常规状态之间的偏移距离和偏移角来提出新的操作可靠性融合指数（ORI），并且将索引用作寿命预测的DI。然后，提出了一种多因素相似性测量方法来选择最相似性样本，并且基于先前的相似性测量提出了考虑劣化程度的新型相似性测量和重量分配的方法。最后，RUL预测了加权。通过实施两种情况验证了所提出的方法的有效性。

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