A New Vibration Analysis Approach for Detecting Mechanical Anomalies on Power Circuit Breakers

Abstract

On-line monitoring and the diagnosis of the high-voltage circuit breaker (HVCB) have been discussed and investigated significantly in the past few decades. The vibration analysis is a noninvasive and advanced diagnostic technique suitable for the detection of mechanical conditions during the HVCB operation, which plays an important role in improving the operating reliability of HVCB and reduces maintenance costs. However, due to the very complicated mechanical system and extremely short operation time of HVCB, the vibration signal has the characteristics of highly nonlinear, non-stationary, and corrupted by heavy garbage noise, which makes it very difficult to precisely extract effective features for machinery fault diagnosis. To address this issue, an energy entropy of Hilbert marginal spectrum (HMS) based on variational mode decomposition (VMD) is presented to analyze the vibration signals of HVCB in this paper. The VMD is used to decompose the vibration signal into a set of intrinsic mode functions (IMFs) reflecting its local characteristics, and then, the energy entropy of IMF's HMS, which varies from different failure modes of HVCB, is obtained by Hilbert transform and entropy-information theory. The characteristics of IMF's HMS, which reveal the variation of vibration signals, under different failure modes of HVCB, are practically analyzed and examined to illustrate the advantage of the proposed method in feature extraction. The IMF that best reflects the mechanical anomaly information of HVCB is ascertained from IMF's HMS, and its Hilbert marginal spectrum energy entropy (HMSEE), namely, IMF-HMSEE, which synthetically reflects the variations of vibration signal's amplitude, phase, and frequency, is turned out to have excellent classification performance for some mechanical anomalies of HVCB. The effectiveness of the proposed approaches is substantiated by experiments carried out in a 12-kV vacuum HVCB.INDEX TERMS Online high voltage circuit breaker (HVCB) assessment, vibration analysis, machinery fault diagnosis, variational mode decomposition (VMD), Hilbert marginal spectrum energy entropy (HMSEE.

Condition Monitoring of High Voltage Circuit Breakers: Past to Future

“…To give an illustration, deformation of shaft, close coil, insulation pull rod, and damper of CB have been investigated based on the vibration signals in [51]. Similarly, in [53], the malfunction in damper and insulation push/pull rod have been identified using vibration. While the earlier is dealt with absorbing the residual energy and reducing the impact of mechanical collision during HVCB operating, the latter transmits the required energy from operating mechanism to the dynamic contact to perform closing and opening operation.…”

“…The main disadvantage of this method is the dependency of its accuracy on the number of sensors and the post processing techniques as discussed in IV-A. The reason lies in the fact that this signal is quite short in the time domain whereas the inherently highly nonlinear and non-stationary vibration signals are extremely wide in the frequency domain [53].…”

“…Energy entropy of Hilbert marginal spectrum (HMS), variational mode decomposition (VMD), [53] (2019) Wavelet, stacked auto encoder neural network [46] (2018) Current vibration signal entropy weight characteristic, grey wolf optimization-support vector machine (GWO-SVM), [49] (2019) Phase space reconstruction, Lyapunov exponent (LLE), correlation dimension (CD),Kolmogorov entropy (KE) [51] (2019) Empirical mode decomposition (EMD) energy entropy, multi-class support vector machine (MSVM) [ Neural network and support vector machine (SVM) [18] (2019) empirical mode decomposition (EMD), empirical wavelet transform (EWT), wavelet packet transform (WPT), variational mode decomposition (VMD) for extraction of features. Subsequently, this signal can be transformed into several intrinsic mode functions.…”

Condition Monitoring of Marine Fuel Oil Separator System

“…This paves the requirement for customized condition monitoring approaches for sea. One of the most powerful tools for predictive or condition-based maintenance is 'Vibration Analysis' [7]. Vibration analysis is a reliable approach for identifying faults at an early stage and preventing unexpected Machine Downtime.…”

Mechanical Condition Identification and Prediction of Spring Operating Mechanism of High Voltage Circuit Breaker

“…To address this issue, many methods have been proposed. For example, an energy entropy of Hilbert marginal spectrum based on vibrational mode decomposition is presented to analyze it [11]; another research introduced a non-linear feature mapping in the wavelet package time-frequency energy rate feature space based on random forest binary coding to extend the feature width [12]. In addition, two layers of independent one-class support vector machines are adopted to distinguish normal or fault conditions with known or unknown fault types respectively.…”