Application of Short-time Energy Method in the Analysis of Mechanical Vibration Signal of Circuit Breaker

2019 ◽  
Author(s):  
Jingjun Wang ◽  
Ke Zhao ◽  
Fei Wang ◽  
Yuanwei Yang ◽  
Yonggang Guan
Keyword(s):  
Energy Method ◽  
Mechanical Vibration ◽  
Circuit Breaker ◽  
Vibration Signal ◽  
Short Time ◽  
Short Time Energy

Characteristic parameter change of circuit breaker under closing spring fatigue

2019 ◽  
Vol 43 (2) ◽  
pp. 189-198
Author(s):  
Longjiang Dou ◽  
Shuting Wan
Keyword(s):  
Circuit Breaker ◽  
Vibration Signal ◽  
Characteristic Parameter ◽  
New Method ◽  
Threshold Method ◽  
Fault Features ◽  
Key Points ◽  
Adams Simulation ◽  
Working Principle ◽  
Short Time

Closing spring fatigue faults of high voltage circuit breakers affect the timing parameters in closing operations and reduce the closing performance of the circuit breaker. Traditional tests of timing parameter based on travel curve cannot be applied online, and sensor installation is complicated. In this paper, a new method to extract key circuit breaker timing parameters from the vibration signal under closing spring fatigue fault is proposed. First, the travel curve of the circuit breaker under closing spring fatigue is simulated in Automatic Dynamic Analysis of Mechanical Systems (ADAMS). Results indicate that the time intervals between key points of the travel curve can be used as fault features. Then, according to the working principle of the circuit breaker’s spring operating mechanism, the vibration event caused by component impact in the closing operation is analyzed. The corresponding timing parameters are extracted from the vibration signal using the double threshold method based on the short-time energy to entropy ratio. Finally, comparison of experimental measurements with ADAMS simulation results and vibration extraction provides the change law of the fault feature. The correctness of the proposed method is verified. This paper presents a new method for online monitoring of circuit breaker closing spring fatigue.

scholarly journals Mechanical Vibration Signal Denoising Using Quantum-Inspired Standard Deviation Based on Subband Based Gaussian Mixture Model

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Aidong Xu ◽  
Wenqi Huang ◽  
Peng Li ◽  
Huajun Chen ◽  
Jiaxiao Meng ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Mechanical Vibration ◽  
Vibration Signal ◽  
Gaussian Mixture ◽  
Wavelet Coefficients ◽  
Denoising Method ◽  
Reduction Effect ◽  
Thresholding Function

Aiming at improving noise reduction effect for mechanical vibration signal, a Gaussian mixture model (SGMM) and a quantum-inspired standard deviation (QSD) are proposed and applied to the denoising method using the thresholding function in wavelet domain. Firstly, the SGMM is presented and utilized as a local distribution to approximate the wavelet coefficients distribution in each subband. Then, within Bayesian framework, the maximum a posteriori (MAP) estimator is employed to derive a thresholding function with conventional standard deviation (CSD) which is calculated by the expectation-maximization (EM) algorithm. However, the CSD has a disadvantage of ignoring the interscale dependency between wavelet coefficients. Considering this limit for the CSD, the quantum theory is adopted to analyze the interscale dependency between coefficients in adjacent subbands, and the QSD for noise-free wavelet coefficients is presented based on quantum mechanics. Next, the QSD is constituted for the CSD in the thresholding function to shrink noisy coefficients. Finally, an application in the mechanical vibration signal processing is used to illustrate the denoising technique. The experimental study shows the SGMM can model the distribution of wavelet coefficients accurately and QSD can depict interscale dependency of wavelet coefficients of true signal quite successfully. Therefore, the denoising method utilizing the SGMM and QSD performs better than others.

scholarly journals A combined generalized Warblet transform and second order synchroextracting transform for analyzing nonstationary signals of rotating machinery

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kai Wei ◽  
Xuwen Jing ◽  
Bingqiang Li ◽  
Chao Kang ◽  
Zhenhuan Dou ◽  
...  
Keyword(s):  
Vibration Signal ◽  
Rotating Machinery ◽  
Second Order ◽  
Variable Speed ◽  
Short Time Fourier Transform ◽  
Stationary Characteristic ◽  
Nonstationary Signals ◽  
Time Frequency ◽  
Effective Technology ◽  
Short Time

AbstractIn recent years, considerable attention has been paid in time–frequency analysis (TFA) methods, which is an effective technology in processing the vibration signal of rotating machinery. However, TFA techniques are not sufficient to handle signals having a strong non-stationary characteristic. To overcome this drawback, taking short-time Fourier transform as a link, a TFA methods that using the generalized Warblet transform (GWT) in combination with the second order synchroextracting transform (SSET) is proposed in this study. Firstly, based on the GWT and SSET theories, this paper proposes a method combining the two TFA methods to improve the TFA concentration, named GWT–SSET. Secondly, the method is verified numerically with single-component and multi-component signals, respectively. Quantized indicators, Rényi entropy and mean relative error (MRE) are used to analyze the concentration of TFA and accuracy of instantly frequency (IF) estimation, respectively. Finally, the proposed method is applied to analyze nonstationary signals in variable speed. The numerical and experimental results illustrate the effectiveness of the GWT–SSET method.

Misfire Diagnosis of Diesel Engine Based on Short-Time Vibration Characters

2010 ◽  
Vol 34-35 ◽  
pp. 301-305
Author(s):  
Zhao Qian Zhu ◽  
Jue Yang ◽  
Xiao Ming Zhang ◽  
Xiao Lei Li
Keyword(s):  
Neural Network ◽  
Diesel Engine ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Vibration Signal ◽  
Vibration Monitoring ◽  
Monitoring Test ◽  
Bp Neural Network Model ◽  
Short Time

This paper studied misfire diagnosis of diesel engine based on short-time vibration characters. Misfire of diesel engine was simulated by the vibration monitoring test. Cylinder vibration signal and top center signal were collected under different states. The short-time vibration signal of each cylinder was intercepted according to the diesel combustion sequence, effective value was calculated, and BP Neural Network model built with this character was used to diagnose diesel misfire. The result shows that this method can locate the misfire cylinder effectively, and it is meaningful for guiding the detection and repair of vehicles.

scholarly journals Recognition of combustion process irregularities in small volume displacement diesel engines with the use of non-dimensional characteristics of the vibration signal

2017 ◽  
Vol 169 (2) ◽  
pp. 18-23
Author(s):  
Jerzy MERKISZ ◽  
Marek WALIGÓRSKI
Keyword(s):  
Urban Areas ◽  
Small Volume ◽  
Fuel Injection ◽  
Combustion Process ◽  
Diagnostic Algorithm ◽  
Vibration Signal ◽  
Technical Object ◽  
Operation Process ◽  
Short Time ◽  
Direct Fuel Injection

The subject of the considerations described in the paper is the problem of early detection of abnormalities and damages during operation process of the turbo diesel engine with small volume displacement and direct fuel injection, which is used in modern LDV vehicles dedicated especially for urban areas, in the context of present and future requirements for a technical object diagnostics, taking into account the criteria of optimizing overall efficiency, toxic compound emission and safety of the object in real conditions of its operation. The paper presents the results of empirical research of vibroacoustic signal application to the diagnostic evaluation of correctness of short-time engine main processes. The evaluation of the combustion process variability from structural and operational abnormalities by using dimensionless estimates of a vibration process was conducted, and functional characteristics necessary to built the diagnostic algorithm in accordance with the requirements of on-board diagnostics were obtained.

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