基于多尺度多样性熵的早期故障点检测

S. Ekwaro-Osire, Nazir Laureano Gandur, Camilo Alberto Lopez-Salazar
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引用次数: 0

摘要

如果初始故障(If)点没有得到纠正,则可能意味着不良的预测框架、错误的剩余使用寿命(RUL)值以及意外的灾难性故障。多尺度多样性熵(MDE)的概念在中频预测中的应用是一个尚未充分探索的新领域。由于MDE通常用于从信号中测量系统的动态复杂性,因此值得探索预测中频。MDE可以用来开发一个框架来预测系统的中频吗?本研究开发了一个新的框架来确定IF。在轴承数据和电池数据上验证了该框架的性能。此外,本研究的结果与另一种广泛用于预测IF的方法进行了比较。总之,由于与MDE相关的物理原理,这种新方法可以更准确地预测IF。
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Incipient Fault Point Detection Based on Multiscale Diversity Entropy
If the incipient fault (IF) point is not corrected, it may imply a poor prognostic framework, a false value of remaining useful life (RUL), and unexpected catastrophic failure. The use of the concept of multiscale diversity entropy (MDE) in the context of predicting IF is a novel area that has yet to be fully explored. Since MDE is commonly used for measuring a system's dynamic complexity from a signal, it is worth exploring for predicting IF. Can MDE be used to develop a framework to predict the IF of a system? This study developed a new framework to determine the IF. The performance of the framework was demonstrated on bearing data and battery data. Additionally, the results of this study were compared with another methodology, widely used for predicting IF. In conclusion, this new methodology produces a more accurate prediction of IF because of the physical principles associated with MDE.
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CiteScore
3.80
自引率
9.10%
发文量
25
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