A Principal Components Rearrangement Method for Feature Representation and Its Application to the Fault Diagnosis of CHMI
A Principal Components Rearrangement Method for Feature Representation and Its Application to the Fault Diagnosis of CHMI
复制标题
一种主成分重排特征表示方法及其在CHMI故障诊断中的应用
DOI：
10.3390/en10091273
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发表时间：
2017-09-01
期刊：
影响因子：
3.2
通讯作者：
Wang, Yide
中科院分区：
文献类型：
--
作者：
Liu, Zhuo;Wang, Tianzhen;Wang, Yide
级联H桥多电平逆变器（CHMI）由于其诸多优点而在工业应用中得到广泛应用。然而，随着其水平的增加，CHMI的可靠性下降。故障诊断技术是保证CHMI可靠性的关键。故障诊断方法的性能取决于所提取的特征的特性。在实践中，一些提取的特征可能非常相似，以确保良好的诊断性能在一些H桥的CHMI。在噪音存在的情况下，情况变得更糟。针对这些问题，本文首先研究了信号去噪和数据预处理技术。然后，提出了一种主成分重排方法（PCR）来表示彼此足够不同的不同特征。最后，设计了一种基于PCR的故障诊断策略。在一个7级CHMI硬件平台上，将该方法与其它故障诊断方法进行了性能比较。
Cascaded H-bridge Multilevel Inverter (CHMI) is widely used in industrial applications thanks to its many advantages. However, the reliability of a CHMI is decreased with the increase of its levels. Fault diagnosis techniques play a key role in ensuring the reliability of a CHMI. The performance of a fault diagnosis method depends on the characteristics of the extracted features. In practice, some extracted features may be very similar to ensure a good diagnosis performance at some H-bridges of CHMI. The situation becomes even worse in the presence of noise. To fix these problems, in this paper, signal denoising and data preprocessing techniques are firstly developed. Then, a Principal Components Rearrangement method (PCR) is proposed to represent the different features sufficiently distinct from each other. Finally, a PCR-based fault diagnosis strategy is designed. The performance of the proposed strategy is compared with other fault diagnosis strategies, based on a 7-level CHMI hardware platform.