Vibration signal analysis of a hydropower unit based on adaptive local iterative filtering

2016 ◽  
Vol 231 (7) ◽  
pp. 1339-1353 ◽  
Author(s):  
Xueli An ◽  
Weiwei Yang ◽  
Xuemin An
Keyword(s):  
Vibration Signal ◽  
Hydraulic Turbine ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Nonstationary Vibration ◽  
Empirical Mode Decomposition Method ◽  
Vibration Signal Analysis ◽  
Iterative Filtering ◽  
Strong Vortex

The vibration signals coming from a hydropower unit have strong nonstationary characteristics when strong vortex develops in the hydraulic turbine draft tube. Related to this problem, a new vibration analysis method for a hydropower unit based on adaptive local iterative filtering is proposed. Firstly, adaptive local iterative filtering was used to decompose the complex vibration signal into several intrinsic mode functions. Then, frequency spectrum analysis of these components was performed to obtain the vortex characteristic frequency from the vibration signal. Simulated and real-world signals were used to verify the proposed method. The obtained results show that this method can overcome the problem of mode mixing in the existing empirical mode decomposition method, since it improves the efficiency and accuracy of feature extraction for nonstationary vibration signals from a hydropower unit.

Analysis of hydropower unit vibration signals based on variational mode decomposition

2015 ◽  
Vol 23 (12) ◽  
pp. 1938-1953 ◽  
Author(s):  
Xueli An ◽  
Luoping Pan ◽  
Fei Zhang
Keyword(s):  
Hydraulic Turbine ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Accurate Analysis ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
The Face ◽  
Band Limited ◽  
Nonlinear Signal

The vibration signals of hydropower units are nonstationary when serious vortex occurs in the draft tube of the hydraulic turbine. The traditional signal analysis method based on Fourier transform is not suitable for the nonstationary signals. In the face of the nonstationarity of such signals and the limitation of the empirical mode decomposition method, a new nonstationary and nonlinear signal analyzing method based on variational mode decomposition (VMD) is introduced into hydropower unit vibration signals analysis. Firstly, VMD is used to decompose the signal into an ensemble of band-limited intrinsic mode functions components. Then, frequency spectrum analysis of these components is conducted to obtain the characteristic frequencies of the signal caused by the serious vortex of hydraulic turbine. Analysis of real test data shows that this proposed method can effectively suppress mode mixing. It can realize accurate analysis of nonstationary vibration signals. This provides a new way for analyzing hydropower unit vibration signals.

Improving empirical mode decomposition for vibration signal analysis

2016 ◽  
Vol 231 (12) ◽  
pp. 2223-2234 ◽  
Author(s):  
Mousa Rezaee ◽  
Amin Taraghi Osguei
Keyword(s):  
Empirical Mode Decomposition ◽  
Decomposition Method ◽  
Vibration Signal ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Local Mean ◽  
The Common ◽  
Vibration Signal Analysis ◽  
Overshoot And Undershoot ◽  
Better Than

In this paper, the empirical mode decomposition as a signal processing method has been studied to overcome one of its shortcomings. In the previous studies, some improvements have been made on the empirical mode decomposition and it has been applied for condition monitoring of mechanical systems. These improvements include elimination of mode mixing and restraining of end effect in empirical mode decomposition method. In this research, to increase the accuracy of empirical mode decomposition, a new local mean has been proposed in the sifting process. Through the proposed local mean, the overshoot and undershoot problems in defining the local mean of common algorithm are alleviated. Meanwhile, it is capable to separate the components with close frequencies. Through the analysis of simulated signals via the new algorithm, it is shown that the accuracy is improved. Finally, empirical mode decomposition-based fault diagnosis approach has been applied to a vibration signal obtained from a faulty gearbox. The results show that the proposed method can resolve the effects of damage in vibration signals better than the common empirical mode decomposition method and helps for the isolation and localization of the fault.

scholarly journals Application of EMD-Based SVD and SVM to Coal-Gangue Interface Detection

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Wei Liu ◽  
Kai He ◽  
Qun Gao ◽  
Cheng-yin Liu
Keyword(s):  
Longwall Mining ◽  
Feature Vector ◽  
Vibration Signal ◽  
Singular Values ◽  
Coal Gangue ◽  
Mining Machine ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Vibration Signal Analysis

Coal-gangue interface detection during top-coal caving mining is a challenging problem. This paper proposes a new vibration signal analysis approach to detecting the coal-gangue interface based on singular value decomposition (SVD) techniques and support vector machines (SVMs). Due to the nonstationary characteristics in vibration signals of the tail boom support of the longwall mining machine in this complicated environment, the empirical mode decomposition (EMD) is used to decompose the raw vibration signals into a number of intrinsic mode functions (IMFs) by which the initial feature vector matrices can be formed automatically. By applying the SVD algorithm to the initial feature vector matrices, the singular values of matrices can be obtained and used as the input feature vectors of SVMs classifier. The analysis results of vibration signals from the tail boom support of a longwall mining machine show that the method based on EMD, SVD, and SVM is effective for coal-gangue interface detection even when the number of samples is small.

A gearbox fault diagnosis method based on frequency-modulated empirical mode decomposition and support vector machine

2016 ◽  
Vol 232 (2) ◽  
pp. 369-380 ◽  
Author(s):  
Chao Zhang ◽  
Zhongxiao Peng ◽  
Shuai Chen ◽  
Zhixiong Li ◽  
Jianguo Wang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Dynamic State ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Diagnosis Method

During the operation process of a gearbox, the vibration signals can reflect the dynamic states of the gearbox. The feature extraction of the vibration signal will directly influence the accuracy and effectiveness of fault diagnosis. One major challenge associated with the extraction process is the mode mixing, especially under such circumstance of intensive frequency. A novel fault diagnosis method based on frequency-modulated empirical mode decomposition is proposed in this paper. Firstly, several stationary intrinsic mode functions can be obtained after the initial vibration signal is processed using frequency-modulated empirical mode decomposition method. Using the method, the vibration signal feature can be extracted in unworkable region of the empirical mode decomposition. The method has the ability to separate such close frequency components, which overcomes the major drawback of the conventional methods. Numerical simulation results showed the validity of the developed signal processing method. Secondly, energy entropy was calculated to reflect the changes in vibration signals in relation to faults. At last, the energy distribution could serve as eigenvector of support vector machine to recognize the dynamic state and fault type of the gearbox. The analysis results from the gearbox signals demonstrate the effectiveness and veracity of the diagnosis approach.

WTG’s Gearbox Condition Assessment by Matching the Hilbert-Huang Spectrum of Vibration Signal

2013 ◽  
Vol 291-294 ◽  
pp. 522-526
Author(s):  
Hai Ning Pan ◽  
Ming Qin ◽  
Lei Pan
Keyword(s):  
Vibration Signal ◽  
Condition Assessment ◽  
Assessment Method ◽  
Iterative Closest Point ◽  
Intrinsic Mode Functions ◽  
Turbine Generator ◽  
Vibration Signals ◽  
Icp Algorithm ◽  
Wind Turbine Generator ◽  
Mode Decomposition

A gearbox condition assessment method for the Wind Turbine Generator (WTG) is proposed. Vibration signal’s Intrinsic Mode Functions (IMF) are decomposed by Empirical Mode Decomposition (EMD). Normalized Hilbert-Huang and Direct Quadrature (DQ) method are used to determine the instantaneous frequency. The HHS of vibration signals is plotted and then is shifted to match the pre-defined faulty gear condition by the Iterative Closest Point (ICP) algorithm to diagnose their similarities. The principle and effectiveness of the proposed method are illustrated by simulation, the fault types of gearbox can be identified by ICP algorithm effectively.

Study on Feature Extraction Method for Diesel Engine Valve Faults

2016 ◽  
Author(s):  
Hongzi Fei ◽  
Long Liu ◽  
Xuemin Li ◽  
Xiuzhen Ma
Keyword(s):  
Feature Extraction ◽  
Diesel Engine ◽  
Vibration Signal ◽  
Intrinsic Mode Functions ◽  
Stationary Characteristic ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Fault Features ◽  
Empirical Mode Decomposition Method ◽  
Faults Diagnosis

Valve faults diagnosis technique of a diesel engine is studied deeply in this paper. The experiment of valve clearance and air leakage faults are done in a diesel engine, and cylinder head vibration and transient speed signals are measured synchronously on normal and fault conditions respectively. These signals are used to feature extraction. In order to avoid the leakage and aliasing of vibration signal’s frequent spectrum, resample method based on order tracking is proposed, and vibration signal was transformed from time domain to crank angle domain accurately. Considering the non-stationary characteristic of vibration signal, a series of intrinsic mode functions with different scales were obtained using the empirical mode decomposition method, and fault features parameters were extracted through 3D Hilbert spectrums of the intrinsic mode functions. Experimental results show that the method can effectively extract fault features of diesel engine and use them to realize the valve system faults diagnosis further.

scholarly journals An Improved Empirical Mode Decomposition Method for Vibration Signal

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiaohan Liu ◽  
Guangfeng Shi ◽  
Weina Liu
Keyword(s):  
Signal Processing ◽  
Power Spectrum ◽  
Empirical Mode Decomposition ◽  
Decomposition Method ◽  
Vibration Signal ◽  
Vibration Monitoring ◽  
Blasting Vibration ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method

With the development of electronic measurement and signal processing technology, nonstationary and nonlinear signal characteristics are widely used in the fields of error diagnosis, system recognition, and biomedical instruments. Whether these features can be extracted effectively usually affects the performance of the entire system. Based on the above background, the research purpose of this paper is an improved vibration empirical mode decomposition method. This article introduces a method of blasting vibration signal processing—Differential Empirical Mode Decomposition (DEMD), combined with phosphate rock engineering blasting vibration monitoring test, and Empirical Mode Decomposition (EMD) to compare and analyze the frequency screening of blasting vibration signals, the aliasing distortion, and the power spectrum characteristics of the decomposed signal. The results show that compared with EMD, DEMD effectively suppresses signal aliasing and distortion, and from the characteristics of signal power spectrum changes, DEMD extracts different dominant frequency components, and the frequency screening effect of blasting vibration signals is superior to EMD. It can bring about an obvious improvement in accuracy, and the calculation time is about 4 times that of the EMD method. Based on the ground analysis of ground motion signals, this paper uses the EMD algorithm to analyze measured ground blast motion signals and study its velocity characteristics and differential time, which provides a new way of studying motion signals.

Gearbox Fault Diagnosis Based on EMD and Coefficient-Energy Value

2012 ◽  
Vol 542-543 ◽  
pp. 234-237
Author(s):  
Ping Wang ◽  
De Xiang Zhang ◽  
Yan Li Liu
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Intrinsic Mode Function ◽  
Vibration Signals ◽  
Energy Value ◽  
Mode Function ◽  
Mode Decomposition ◽  
Gearbox Vibration ◽  
Vibration Signal Analysis ◽  
Fault Information

This paper applies the empirical mode decomposition (EMD) methods to gearbox vibration signal analysis capture from vibrating acceleration sensor for gearbox fault diagnosis. The original modulation fault vibration signals are firstly decomposed into a number of intrinsic mode function (IMF) by the EMD method. Then the fault information diagnosis of the gearbox vibration signals can be extracted from the coefficient-energy value of intrinsic mode function. Experiment result has shown the feasibility and efficiency of the EMD algorithms and energy characteristic method in fault diagnosis and fault message abstraction. It is significant for the monitor operating state of gearbox and detects incipient faults as soon as possible.

Vibration Signal Analysis and Fault Diagnosis of Gears Based on Hilbert Marginal Spectrum

2014 ◽  
Vol 909 ◽  
pp. 121-126 ◽  
Author(s):  
Jiang Ping Wang ◽  
Jin Cui
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Operating Conditions ◽  
Characteristic Time Scale ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Vibration Signal Analysis ◽  
Non Stationary Signal

Hilbert-Huang transform is a new method of signal processing, which is very suitable for dealing with nonlinear and non-stationary signal. In this article, a gear fault diagnosis method based on Hilbert marginal spectrum is proposed in view of the non-stationary characteristics of gear vibration signal. First the original vibration signal is decomposed into several intrinsic mode functions (IMF) of different characteristic time scale smoothly by means of empirical mode decomposition (EMD) method. Then the Hilbert-Huang transform is carried out for IMF and the Hilbert marginal spectrum under different operating conditions are obtained. Gear faults can be judged through the analysis of the marginal spectrum. The experimental results show that this method can effectively diagnose the gear faults.

scholarly journals Order Tracking Using Variational Mode Decomposition to Detect Gear Faults under Speed Fluctuations

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Madhurjya Dev Choudhury ◽  
Liu Hong ◽  
Jaspreet Singh Dhupia
Keyword(s):  
Fault Detection ◽  
Reference Signal ◽  
Vibration Signal ◽  
Operating Speed ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Order Tracking ◽  
Mode Decomposition ◽  
Gearbox Vibration

Fault detection in gearboxes plays a significant role in ensuring their reliability. Vibration signals collected during gearbox operation contain a wealth of valuable condition information that can be exploited for fault detection. However, in an industrial environment machine operating speed always fluctuates around its nominal value, which causes smearing of the gearbox vibration spectrum. Considering operating speed fluctuation and multi-component nature of measured gearbox vibration signals, an order-tracking method combining the variational mode decomposition (VMD) and the fast dynamic time warping (FDTW) is proposed in this paper. Firstly, the multi-component vibration signal is decomposed into several intrinsic mode functions (IMFs) using VMD in order to extract a signal component with higher signal-to-noise ratio (SNR). Then, the sensitive fault information carrying IMF is exploited to estimate the instantaneous speed profile in order to construct the shaft rotational vibration signal. The measured vibration signal is then resampled based on the optimal warping path obtained by FDTW, which performs an “elastic” stretching and compression along the time axis of the extracted shaft vibration signal with respect to a sinusoidal reference signal of constant shaft rotational frequency. Finally, the gear fault is detected by constructing the order spectrum of the resampled vibration signal. The effectiveness of the proposed algorithm is demonstrated using simulation results.

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