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

Abstract

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.