Mathematical Problems in Engineering

Volume 2015 (2015), Article ID 101757, 6 pages

http://dx.doi.org/10.1155/2015/101757

## Mechanical Fault Diagnosis for HV Circuit Breakers Based on Ensemble Empirical Mode Decomposition Energy Entropy and Support Vector Machine

^{1}HLJ Province Key Lab of Senior-Education for Electronic Engineering, Heilongjiang University, Harbin 150080, China^{2}College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China

Received 8 April 2015; Revised 19 June 2015; Accepted 28 June 2015

Academic Editor: Jean-Charles Beugnot

Copyright © 2015 Jianfeng Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

During the operation process of the high voltage circuit breaker, the changes of vibration signals can reflect the machinery states of the circuit breaker. The extraction of the vibration signal feature will directly influence the accuracy and practicability of fault diagnosis. This paper presents an extraction method based on ensemble empirical mode decomposition (EEMD). Firstly, the original vibration signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs). Secondly, calculating the envelope of each IMF and separating the envelope by equal-time segment and then forming equal-time segment energy entropy to reflect the change of vibration signal are performed. At last, the energy entropies could serve as input vectors of support vector machine (SVM) to identify the working state and fault pattern of the circuit breaker. Practical examples show that this diagnosis approach can identify effectively fault patterns of HV circuit breaker.

#### 1. Introduction

As an import part of the electric system, a HV circuit breaker is a key device to control and protect the power network. Therefore, the action reliability of HV circuit breaker is extremely important in the electric system. In recent years, research on diagnosis method of circuit breaker is growing fast, and many new techniques have been used in practice, in which the technique based on the analysis of the vibration signal has gradually become hot [1–3].

Many vibration signals produced by circuit breaker contain a number of pieces of important information, which can be used to evaluate the machinery state of circuit breaker. Through the analysis of vibration signals acquired by the piezoelectric sensor, the running states of circuit breaker are convenient and accurate to diagnose. To analyze the vibration signal, some signal processing methods, such as wavelet [4, 5] and EMD [6, 7], have been used in practice. Wavelet analysis has become popular in the past decade as a method for time-frequency representation. In principle, wavelet transform (WT) uses short windows at high frequencies and long windows at low frequencies, which renders WT more suitable for dealing with nonstationary time series. Nonetheless, wavelet analysis is also limited by the fundamental uncertainty principle, in which both time and frequency cannot simultaneously be resolved with the same precision. Moreover, the results of WT analysis depend on the choice of the mother wavelet, which is arbitrary and may not be optimal for the time series under scrutiny. In contrast to the WT approach, the empirical mode decomposition (EMD) [8] method adaptively decomposes nonstationary time series into narrow-band components, namely, intrinsic mode functions (IMFs), by empirically identifying the physical time scales intrinsic to the data without assuming any basis functions. Thus, the EMD can potentially localize events in both time and frequency, even in nonstationary time series [9–12]. So, the EMD is a suitable method to process nonlinear and nonstationary signals. However, mode mixing problems brought by EMD greatly restrict its application in practice. EEMD is the repeated EMD by adding Gauss white noise in each of the decompositions. It takes advantage of the uniform distribution statistical characteristics of Gauss white noise in frequency domain [13]. Through this method, EEMD could decompose signal continuously in different scales. Therefore, the problem of mode mixing will be eliminated effectively. A nonstationary vibration signal is decomposed into a series of intrinsic mode functions (IMFs) by EEMD. The envelope of IMF can be obtained through Hilbert transform and separated by equal-time segment. Then, we can get the energy entropy of each envelope of IMF with the energy entropy theory. Those IMF energy entropies can form the entropy vector, and this could serve as the input vector of SVM for judging circuit breaker working states and fault types. The experiment result indicates the method that applied the EEMD-energy entropy and support vector machine is effective and has many potential applications in practice.

#### 2. EEMD Method

EEMD is a new method of signal process; the specific decomposition steps and principles are as follows [14].

*Step 1. *Adding the random Gauss white noise with the mean zero of amplitude and the constant of standard deviation to the original signal (the standard deviation of white noise is 0.1–0.4 times the size of the original signal.), the function is as follows:Signal is the signal that added the th Gauss white noise. The Gauss white noise will directly affect the decomposition of signal by EEMD.

*Step 2. *Signal is decomposed into several IMFs and the margin . The with the th Gauss white noise is the th IMF decomposition.

*Step 3. *Repeat Steps 1 and 2* N* times. Next, with the principle that the statistical mean of random and independent sequence is zero, the overall average operation for the corresponding IMF could eliminate the effects of multiple Gauss white noise on the real IMF. The final IMF is written asin which the is the th IMF component of original signal by EEMD. When is larger, the sum of the white noise of IMFS will tend to zero. At this time, the results for EEMD are written asin which is the final residual component, representing the average trend of signal. Through EEMD method, we can put any signal into several of IMFs and a residual component. The intrinsic mode components () represent the elements of signal from high to low frequency band; in each band the frequency components are not the same and will change following the change of vibration signal .

Figure 1 shows the normal state of vibration signal. The signal can get eight major components and a residual component by EEMD, as shown in Figure 2. From the diagram, the normal state of nonstationary vibration signal is decomposed into a number of stationary IMF components by EEMD, and different IMF component contains a variety of time scales.