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

机译:基于灰克(1,1)模型的设备故障预测精度改进方法

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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:失败预测是一个重要且困难的研究方面。首先,我们介绍灰色GM(1,1)模型的概念。在本文中,在预测精度方面发布于GM(1,1)的现有问题,这通常受收集的失效数据序列的平滑性的影响。为了提高预测准确性,我们介绍了改变试图改善原始失败基准序列的平滑度的概念。这是使用GM(1,1)模型来实现变换。并将其应用于设备预测。基于许多收集的失败数据,我们使用该方法。然后应用MATLAB仿真来实现残余测试和后差检验。结果表明,上述方法有效,准确测试满足要求。并显示本文提出的程序,以提高对故障预测的准确性。

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