Research on Wavelet Compression and Reconstruction of Wind Turbine Vibration Signal

2013 ◽  
Vol 300-301 ◽  
pp. 753-757
Author(s):  
Xiao Ye Gan ◽  
Zhi Dong Huang
Keyword(s):  
Wind Turbine ◽  
Compression Ratio ◽  
Wavelet Transformation ◽  
Vibration Signal ◽  
Wavelet Base ◽  
Reconstruction Accuracy ◽  
Wavelet Compression

The method of wavelet compression and reconstruction is studied. The wind turbine vibration signal is compressed and reconstructed by wavelet transformation. The compression and reconstruction of wind turbine vibration signal are achieved with different wavelet base. The compression ratio and the compression reconstruction accuracy are analyzed. The method and the result facilitate the choice of wavelet base for transmission of wind turbine vibration signal.

Research on Compression and Reconstruction of Wind Turbine Vibration Signal Based on Discrete Cosine Transform

2013 ◽  
Vol 712-715 ◽  
pp. 1668-1671
Author(s):  
Zhi Dong Huang ◽  
Xiao Ye Gan
Keyword(s):  
Wind Turbine ◽  
Discrete Cosine Transform ◽  
Compression Ratio ◽  
Vibration Signal ◽  
Reconstruction Accuracy ◽  
Vibration Signals ◽  
Cosine Transform ◽  
The Relationship

The method of compression and reconstruction for wind turbine vibration signal based on Discrete Cosine Transform is presented. Two group wind turbine vibration signals are compressed and reconstructed by DCT. The compression ratio and the compression reconstruction accuracy are investigated. The relationship between compression ratio, NSRMSE and the number of the reserved data is clarified. The result facilitates the application of compression and reconstruction for wind turbine vibration signal based on DCT.

Research on Online Monitoring of Gear Fault of Wind Power Gearbox Based on Undecimated Wavelet Transformation

2011 ◽  
Vol 383-390 ◽  
pp. 4928-4931 ◽  
Author(s):  
Fu Cheng Zhou
Keyword(s):  
Wind Turbine ◽  
Wavelet Transformation ◽  
Online Monitoring ◽  
Vibration Signal ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transformation ◽  
Wind Turbine Gearbox ◽  
Gear Fault ◽  
On Line ◽  
Undecimated Wavelet

According to fault characteristics of wind turbine gearbox, an On-line Monitoring System of wind turbine gearbox based on the undecimated discrete wavelet transformation was developed. Measured the vibration signal of gear fault in the laboratory, The signal after undecimated wavelet transformation can be more accurate and clear display characteristics, which was conducive to diagnose gear failure quickly and effectively.

Parameters Identification for Nonlinear Duffing System Based on Wavelet Transformation

2013 ◽  
Vol 357-360 ◽  
pp. 1524-1530
Author(s):  
Shi Zhou ◽  
Dong Mei Huang ◽  
Wei Xin Ren ◽  
Qiong Li Wang
Keyword(s):  
Forced Vibration ◽  
Wavelet Transformation ◽  
Recognition Accuracy ◽  
Vibration Signal ◽  
Least Square Method ◽  
Least Square ◽  
Instantaneous Amplitude ◽  
Approximate Analytical Expression ◽  
Duffing System ◽  
The Relationship

Continuous wavelet transformation is made to identify the parameters of damped harmonic forced vibration Duffing system. With the aid of conversion relationship between the scale and frequency, the solution of nonlinear Duffing equation is adopted by average method, which gained approximate analytical expression for instantaneous amplitude and instantaneous frequency of the system. The nonlinear stiffness coefficient and natural frequency can be gained by least square method and the relationship between recognition accuracy and parameter selection are summarized in the article. Parameter identification method of harmonic forced vibration system is proposed in this paper. Studying the wavelet ridge and corresponding scale by segments to filter out the affects of the simple harmonic motion, to extract systems free vibration signal and to achieve the goal of identifying system parameters.

Adaptive Multiple Second-order Synchrosqueezing Wavelet Transform and Its Application in Wind Turbine Gearbox Fault Diagnosis

2021 ◽  
Author(s):  
Zhaohong Yu ◽  
Cancan Yi ◽  
Xiangjun Chen ◽  
Tao Huang
Keyword(s):  
Wavelet Transform ◽  
Wind Turbine ◽  
Time Window ◽  
Vibration Signal ◽  
Second Order ◽  
Center Frequency ◽  
Wind Turbine Gearbox ◽  
Adaptive Wavelet ◽  
Time Frequency ◽  
Window Width

Abstract Wind turbines usually operate in harsh environments and in working conditions of variable speed, which easily causes their key components such as gearboxes to fail. The gearbox vibration signal of a wind turbine has nonstationary characteristics, and the existing Time-Frequency (TF) Analysis (TFA) methods have some problems such as insufficient concentration of TF energy. In order to obtain a more apparent and more congregated Time-Frequency Representation (TFR), this paper proposes a new TFA method, namely Adaptive Multiple Second-order Synchrosqueezing Wavelet Transform (AMWSST2). Firstly, a short-time window is innovatively introduced on the foundation of classical Continuous Wavelet Transform (CWT), and the window width is adaptively optimized by using the center frequency and scale factor. After that, a smoothing process is carried out between different segments to eliminate the discontinuity and thus Adaptive Wavelet Transform (AWT) is generated. Then, on the basis of the theoretical framework of Synchrosqueezing Transform (SST) and accurate Instantaneous Frequency (IF) estimation by the utilization of second-order local demodulation operator, Adaptive Second-order Synchrosqueezing Wavelet Transform (AWSST2) is formed. Considering that the quality of actual time-frequency analysis is greatly disturbed by noise components, through performing multiple Synchrosqueezing operations, the congregation of TFR energy is further improved, and finally, the AMWSST2 algorithm studied in this paper is proposed. Since Synchrosqueezing operations are performed only in the frequency direction, this method AMWSST2 allows the signal to be perfectly reconstructed. For the verification of its effectiveness, this paper applies it to the processing of the vibration signal of the gearbox of a 750 kW wind turbine.

Wind Turbine Generator Fault Detection by Wavelet-Based Multifractal Analysis

2013 ◽  
Vol 644 ◽  
pp. 346-349
Author(s):  
Chang Zheng Chen ◽  
Yu Zhang ◽  
Quan Gu ◽  
Yan Ling Gu
Keyword(s):  
Wind Turbine ◽  
Multifractal Analysis ◽  
Vibration Signal ◽  
Rotational Frequency ◽  
Turbine Generator ◽  
Vibration Signals ◽  
Wind Turbine Generator ◽  
Fault Features ◽  
Multifractal Theory ◽  
Incipient Fault

It is difficult to obtain the obvious fault features of wind turbine, because the vibration signal of them are non-linear and non-stationary. To solve the problem, a multifractal analysis based on wavelet is presented in this research. The real signals of 1.5 MW wind turbine are studied by multifractal theory. The incipient fault features are extracted from the original signal. Using the Wavelet Transform Modulo Maxima Method, the multifractal was obtained. The results show that fault features of high rotational frequency of wind turbine are different from low rotational frequency, and the complexity of the vibration signals increases with the rotational frequency. These demonstrate the multifractal analysis is effective to extract the fault features of wind turbine generator.

Fault Diagnosis for Wind Turbine Gearboxes Based on EMD and the Energy Operator

2013 ◽  
Vol 281 ◽  
pp. 10-13 ◽  
Author(s):  
Xian You Zhong ◽  
Liang Cai Zeng ◽  
Chun Hua Zhao ◽  
Xian Ming Liu ◽  
Shi Jun Chen
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Empirical Mode Decomposition ◽  
Energy Operator ◽  
Vibration Signal ◽  
Intrinsic Mode Functions ◽  
Wind Turbine Gearbox ◽  
Mode Decomposition ◽  
The Cost ◽  
Fault Information

Wind turbine gearbox is subjected to different sorts of failures, which lead to the increasement of the cost. A approach to fault diagnosis of wind turbine gearbox based on empirical mode decomposition (EMD) and teager kaiser energy operator (TKEO) is presented. Firstly, the original vibration signal is decomposed into a number of intrinsic mode functions (IMFs) using EMD. Then the IMF containing fault information is analyzed with TKEO, The experimental results show that EMD and TKEO can be used to effectively diagnose faults of wind turbine gearbox.

scholarly journals The Exploitation of Wavelet De-Noising To Detect Bearing Faults

2007 ◽  
Vol 3 (1) ◽  
pp. 7-16
Author(s):  
Khalid Al-Raheem ◽  
Asok Roy ◽  
K. Ramachandran ◽  
David Harrison ◽  
Steven Grainger
Keyword(s):  
Fault Detection ◽  
Vibration Signal ◽  
Damping Factor ◽  
Wavelet Base ◽  
Base Function ◽  
Bearing Faults ◽  
Time Frequency ◽  
Bearing Fault ◽  
Detection Process ◽  
Bearing Vibration

The Exploitation of Wavelet De-Noising To Detect Bearing Faults Failure diagnosis is an important component of the Condition Based Maintenance (CBM) activities for most engineering systems. Rolling element bearings are the most common cause of rotating machinery failure. The existence of the amplitude modulation and noises in the faulty bearing vibration signal present challenges to effective fault detection method. The wavelet transform has been widely used in signal de-noising due to its extraordinary time-frequency representation capability. In this paper, we proposed new approach for bearing fault detection based on the autocorrelation of wavelet de-noised vibration signal through a wavelet base function derived from the bearing impulse response. To improve the fault detection process the wavelet parameters (damping factor and center frequency) are optimized using maximization kurtosis criteria to produce wavelet base function with high similarity with the impulses generated by bearing defects, that leads to increase the magnitude of the wavelet coefficients related to the fault impulses and enhance the fault detection process. The results show the effectiveness of the proposed technique to reveal the bearing fault impulses and its periodicity for both simulated and real rolling bearing vibration signals.

Sign in / Sign up

Export Citation Format

Share Document