Mechanical Fault Diagnosis of High Voltage Circuit Breakers with Unknown Fault Type Using Hybrid Classifier Based on LMD and Time Segmentation Energy Entropy

Abstract

Abstract:In order to improve the identification accuracy of the high voltage circuit breakers' (HVCBs) mechanical fault types without training samples, a novel mechanical fault diagnosis method of HVCBs using a hybrid classifier constructed with Support Vector Data Description (SVDD) and fuzzy c-means (FCM) clustering method based on Local Mean Decomposition (LMD) and time segmentation energy entropy (TSEE) is proposed. Firstly, LMD is used to decompose nonlinear and non-stationary vibration signals of HVCBs into a series of product functions (PFs). Secondly, TSEE is chosen as feature vectors with the superiority of energy entropy and characteristics of time-delay faults of HVCBs. Then, SVDD trained with normal samples is applied to judge mechanical faults of HVCBs. If the mechanical fault is confirmed, the new fault sample and all known fault samples are clustered by FCM with the cluster number of known fault types. Finally, another SVDD trained by the specific fault samples is used to judge whether the fault sample belongs to an unknown type or not. The results of experiments carried on a real SF 6 HVCB validate that the proposed fault-detection method is effective for the known faults with training samples and unknown faults without training samples.

Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Variational Mode Decomposition and Multi-Layer Classifier

“…Runde et al [2] demonstrated through an extensive HVCB diagnostic test that vibration analysis is a suitable and reliable noninvasive diagnostic method for HVCBs. Analysis of HVCB vibration signals collected by acceleration sensor has been widely used in the state detection and fault diagnosis of HVCBs [3,4,5,6,7,8,9]. …”

“…The signal acquisition equipment must have a high sampling rate. An acceleration sensor has high accuracy, wide frequency and amplitude responses, small size, and is easy to install; thus, it is widely used in vibration data acquisition [3,4,5,6,7,8,9,10,11]. …”

“…Traditional signal-processing methods, such as Fourier transform (FT) [12], are unsuitable for vibration signal analysis and processing. Conversely, time-frequency analysis methods, including wavelet packet decomposition (WPD) [3], empirical mode decomposition (EMD) [6,7], and local mean decomposition (LMD) [9], can analyze HVCB vibration signals well. Wavelet analysis can represent the local characteristics of signals both in time and frequency domains; thus, it is widely used in mechanical fault diagnosis [13].…”

Fault diagnosis for high voltage circuit breaker based on timing parameters and FCM

“…Therefore, vibration diagnosis is suitable for the mechanical condition monitoring of high voltage CB [4,5]. In some recent years, researchers have done a lot of work in these fields.…”

Failure Prognosis of High Voltage Circuit Breakers with Temporal Latent Dirichlet Allocation

“…Degradation of the HVCB is caused by several types of stress and aging, such as mechanical maladjustment, switching arcs erosion, and insulation level decline. The existing literature covers a wide range of specific countermeasures, including mechanism dynamic features [8][9][10], dynamic contact resistance [11], partial discharge signal [12,13], decomposition gas [14], vibration [15], and spectroscopic monitoring [16]. Furthermore, numerous studies applied neural networks [8], support vector machine (SVM) [17], fuzzy logic [18], and other methods [19], to introduce more automation and intelligence into the signal analysis.…”