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Research on combined diagnosis of mechanical fault vibration-sound signal of high voltage circuit breaker based on EEMD-Energy Entropy feature

机译：基于EEMD-Energy熵特征的高压断路器机械故障振动信号组合诊断研究

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Current research reveal many problems in the process of mechanical fault diagnosis of high-voltage circuit breakers (HVCBs). One of the problems concerns the vibration signals of HVCBs involving a wide range and large amplitude. This makes it difficult to monitor. A single vibration signal is seriously affected by the position. And it is easy to saturate and top off, which can"t fully display the mechanical fault information of HVCBs.Therefore, this paper collects vibration and sound signals for the typical five working conditions of the HVCBs, and proposes an EEMD-energy entropy feature extraction method, then uses the KNN algorithm to diagnose the five working conditions. Compared with the diagnosis results under a single vibration signal and a single sound signal, the superiority of the vibration-sound signal combined analysis and the effectiveness of the proposed feature extraction method areverified, which provides a new idea for the study of mechanical fault diagnosis of HVCBs.

机译：目前的研究揭示了高压断路器（HVCB）机械故障诊断过程中的许多问题。其中一个问题涉及涉及宽范围和大幅度的HVCB的振动信号。这使得难以监测。单个振动信号受到位置的严重影响。它易于饱和和顶部，可以完全显示HVCBS的机械故障信息。因此，本文收集了HVCBS的典型五个工作条件的振动和声音信号，并提出了EEMD-Energy熵特征提取方法，然后使用KNN算法诊断五个工作条件。与单个振动信号下的诊断结果和单一声音信号相比，振动声信号组合分析的优势和所提出的特征提取方法的有效性厌恶，为HVCBS的机械故障诊断提供了新的思路。

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《International Conference on Sensors and Instruments》|2021年|118870Z.1-118870Z.7|共7页
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Zhang Yanfei; Shao Yang; Gong Weiwei; Zhang Zhaowei; Wu Jianwen;
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HVCBs; Vibration signal; Sound signal; EEMD; Energy entropy;

机译：HVCBS;振动信号;声音信号;EEMD;能量熵;

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专利

1. Mechanical Fault Diagnosis of High Voltage Circuit Breakers with Unknown Fault Type Using Hybrid Classifier Based on LMD and Time Segmentation Energy Entropy [J] . Nantian Huang, Lihua Fang, Guowei Cai, Entropy . 2016,第9期

机译：基于LMD和时间分段能量熵的混合分类器的未知故障高压断路器机械故障诊断。
2. A Novel Mechanical Fault Feature Selection and Diagnosis Approach for High-Voltage Circuit Breakers Using Features Extracted without Signal Processing [J] . Lin Lin, Wang Bin, Qi Jiajin, Nature reviews Cancer . 2019,第2期

机译：一种新的机械故障特征选择和诊断使用功能提取的高压断路器没有信号处理
3. Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Wavelet Time-Frequency Entropy and One-Class Support Vector Machine [J] . Nantian Huang, Huaijin Chen, Shuxin Zhang, Entropy . 2016,第1期

机译：基于小波时频熵和一类支持向量机的高压断路器机械故障诊断
4. CNN based Mechanical Fault Diagnosis of High Voltage Circuit Breaker using Sound and Current Signal [C] . Xiaoming Wang, Xiangyu Lin, Ke Zhou, IEEE International Conference on High Voltage Engineering and Application . 2020

机译：基于CNN的高压断路器使用声音信号的机械故障诊断
5. Short-Circuit Protection for Low-Voltage DC Distribution Systems Based on Solid-State Circuit Breakers. [D] . Munasib, Sharthak. 2017

机译：基于固态断路器的低压直流配电系统的短路保护。
6. Mechanical Fault Diagnosis of a High Voltage Circuit Breaker Based on High-Efficiency Time-Domain Feature Extraction with Entropy Features [O] . Jiajin Qi, Xu Gao, Nantian Huang 2020

机译：基于高效率时域特征提取的高压断路器机械故障诊断熵特征
7. A Novel Mechanical Fault Feature Selection and Diagnosis Approach for High-Voltage Circuit Breakers Using Features Extracted without Signal Processing [O] . Lin Lin, Bin Wang, Jiajin Qi, 2019

机译：一种新的机械故障特征选择和诊断使用功能提取的高压断路器没有信号处理
8. Combining Model-Based and Feature-Driven Diagnosis Approaches - A Case Study on Electromechanical Actuators [R] . Narasimhan, S., Roychoudhury, I., Balaban, E., 2010

机译：结合基于模型和特征驱动的诊断方法 - 机电执行器的案例研究

Research on combined diagnosis of mechanical fault vibration-sound signal of high voltage circuit breaker based on EEMD-Energy Entropy feature

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