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Fault diagnosis for circuit-breakers using adaptive chirp mode decomposition and attractor's morphological characteristics

机译：断路器故障诊断使用自适应啁啾模式分解和吸引子的形态特征

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摘要

In order to minimize operation and maintenance costs and enhance the reliability of circuit-breaker (CB), faults should be detected simply and diagnosed effectively. To enable this, monitoring or diagnostic-test techniques should provide critical information of the CB and make the decision-making process simple. In recent years, vibration-based analysis has gained attention for the said purpose. However, the diagnostic performance is limited by the highly time-varying and non-stationary vibration signals. Hence, within this paper a new diagnostic framework is proposed for CB fault diagnosis. The recently proposed adaptive chirp mode decomposition (ACMD) is introduced for extracting the fast fluctuating instantaneous frequency in the CB's vibration signal. A high-resolution adaptive time-frequency spectrum which can clearly represent the time-frequency (TF) characteristics of the vibration signal is obtained by combining the wavelet transform and ACMD. The component with the most significant TF fluctuation is reconstructed into phase space to study the dynamical characteristics of CB. Based on the reconstructed phase space, a new set of dynamical features, namely RST (i.e., ratio of major to minor axis, shape complexity and trajectory compactness), is proposed for achieving a stable and accurate diagnosis of CB faults. The proposed diagnostic framework is evaluated on two experimental scenarios with different types of CB. The reasonable diagnostic performances confirm the ability of the proposed technique in diagnosing CB faults.
机译：为了最大限度地减少操作和维护成本并增强断路器（CB）的可靠性，应简单地检测故障并有效地诊断。为了实现此目的，监视或诊断测试技术应提供CB的关键信息，并使决策过程简单。近年来，基于振动的分析已经引起了上述目的。然而，诊断性能受到高度时变和非静止振动信号的限制。因此，在本文中，提出了一种新的诊断框架，用于CB故障诊断。介绍了最近提出的自适应啁啾模式分解（ACMD），用于提取CB振动信号中的快速波动瞬时频率。通过组合小波变换和ACMD来获得可以清楚地代表振动信号的时频（TF）特性的高分辨率自适应时频谱。具有最显着的TF波动的组件重建为相空间以研究CB的动态特性。基于重建的相空间，提出了一种新的动态特征，即RST（即，主要到短轴的比例，形状复杂度和轨迹紧凑率的比例），以实现对CB故障的稳定和准确的诊断。所提出的诊断框架是在具有不同类型CB的两种实验场景上进行评估。合理的诊断表演证实了所提出的技术在诊断CB故障方面的能力。

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