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An Equipment Failure Prediction Accuracy Improvement Method Based on the Gray GM(1,1) Model

机译:基于灰色GM(1,1)模型的设备故障预测精度的提高方法

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摘要

Failure prediction is an important and difficult research aspect. Firstly we introduce the concepts of Gray GM(1,1) model. In this paper, addressed to the existing problems of GM(1,1) in the prediction accuracy aspect, that is often affected by the smoothness of the sequence of collected failure datum. In order to improve the prediction accuracy, we introduce the concept of transforming trying to improve the smoothness of the original failure datum sequence. That is using GM(1,1) model to implement the transforming. And apply it to equipment prediction. Based on many collected failure datum, we use the proposed method. Then MATLAB simulation is applied to implement the residual test and posterior difference test. The results show that the above methods are valid and accuracy test meets the requirements. And the program proposed in this paper is shown to improve accuracy on the failure prediction.
机译:失效预测是重要而困难的研究方面。首先,我们介绍了灰色GM(1,1)模型的概念。在本文中,针对预测精度方面的GM(1,1)存在的问题,该问题通常受收集的失效基准序列的平滑性影响。为了提高预测精度,我们引入了变换的概念,试图提高原始失效基准序列的平滑度。那就是使用GM(1,1)模型来实现转换。并将其应用于设备预测。基于许多收集的失效数据,我们使用所提出的方法。然后应用MATLAB仿真来实现残差检验和后验检验。结果表明,以上方法是有效的,准确度测试符合要求。并提出了本文提出的程序,以提高故障预测的准确性。

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