scholarly journals Signal Denoising Method Based on Adaptive Redundant Second-Generation Wavelet for Rotating Machinery Fault Diagnosis

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Na Lu ◽  
Guangtao Zhang ◽  
Yuanchu Cheng ◽  
Diyi Chen
Keyword(s):  
Fault Diagnosis ◽  
Second Generation ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Support Vector ◽  
Signal Denoising ◽  
Denoising Method ◽  
Machine Method ◽  
Second Generation Wavelet ◽  
Machinery Fault Diagnosis

Vibration signal of rotating machinery is often submerged in a large amount of noise, leading to the decrease of fault diagnosis accuracy. In order to improve the denoising effect of the vibration signal, an adaptive redundant second-generation wavelet (ARSGW) denoising method is proposed. In this method, a new index for denoising result evaluation (IDRE) is constructed first. Then, the maximum value of IDRE and the genetic algorithm are taken as the optimization objective and the optimization algorithm, respectively, to search for the optimal parameters of the ARSGW. The obtained optimal redundant second-generation wavelet (RSGW) is used for vibration signal denoising. After that, features are extracted from the denoised signal and then input into the support vector machine method for fault recognition. The application result indicates that the proposed ARSGW denoising method can effectively improve the accuracy of rotating machinery fault diagnosis.

Mechanical faulty signal denoising using a redundant non-linear second-generation wavelet transform

2011 ◽  
Vol 225 (4) ◽  
pp. 799-808 ◽  
Author(s):  
N Li ◽  
R Zhou ◽  
X Z Zhao
Keyword(s):  
Wavelet Transform ◽  
Second Generation ◽  
Selection Criterion ◽  
Signal Denoising ◽  
Mechanical Equipment ◽  
Denoising Method ◽  
Second Generation Wavelet Transform ◽  
Non Linear ◽  
Early Fault Detection ◽  
Second Generation Wavelet

Denoising and extraction of the weak signals are crucial to mechanical equipment fault diagnostics, especially for early fault detection, in which cases fault features are very weak and masked by the noise. The wavelet transform has been widely used in mechanical faulty signal denoising due to its extraordinary timefrequency representation capability. However, the mechanical faulty signals are often non-stationary, with the structure varying significantly within each scale. Because a single wavelet filter cannot mimic the signal structure of an entire scale, the traditional wavelet-based signal denoising method cannot achieve an ideal effect, and even worse some faulty information of the raw signal may be lost in the denoising process. To overcome this deficiency, a novel mechanical faulty signal denoising method using a redundant non-linear second generation wavelet transform is proposed. In this method, an optimal prediction operator is selected for each transforming sample according to the selection criterion of minimizing each individual prediction error. Consequently, the selected predictor can always fit the local characteristics of the signals. The signal denoising results from both simulated signals and experimental data are presented and both support the proposed method.

Rolling Bearing Fault Diagnosis Based on Second Generation Wavelet Denoising and Improved EEMD

2014 ◽  
Vol 556-562 ◽  
pp. 2677-2680 ◽  
Author(s):  
Ling Jie Meng ◽  
Jia Wei Xiang
Keyword(s):  
Fault Diagnosis ◽  
Second Generation ◽  
Selection Criterion ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Wavelet Denoising ◽  
Intrinsic Mode Functions ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Second Generation Wavelet

A new rolling bearing fault diagnosis approach is proposed. The original vibration signal is purified using the second generation wavelet denoising. The purified signal is further decomposed by an improved ensemble empirical mode decomposition (EEMD) method. A new selection criterion, including correlation analysis and the first two intrinsic mode functions (IMFs) with the maximum energy, is put forward to eliminate the pseudo low-frequency components. Experimental investigation show that the rolling bearing fault features can be effectively extracted.

Shaft Vibration Signal Denoising Based on Wavelet Analysis

2013 ◽  
Vol 300-301 ◽  
pp. 1110-1113
Author(s):  
Tie Qiang Sun ◽  
Rong Liu ◽  
Zhi Qi Qiu
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Wavelet Packet ◽  
Mechanical Vibration ◽  
Vibration Signal ◽  
Signal Denoising ◽  
Axial Vibration ◽  
Denoising Method ◽  
Original Source ◽  
Separation Result

In actual , there exist inevitably a lot of interference from neighbor machine and noise from surrondings in mechanical vibration signal measured by sensor ,which is disadvantageous for condition monitoring and fault diagnosis. In order to eliminate the axial vibration signal in the noise, using Wavelet packet denoising method in this article, Emulating experiment s were carried out under the MATLAB software ,original signals adopted vibration impulsion signal produced by vice position of faulty bear. Separation result s confirm this method successfully ext ract original source ,efficiently removes noise.

scholarly journals A New Intelligent Fault Diagnosis Method of Rotating Machinery under Varying-Speed Conditions Using Infrared Thermography

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yongbo Li ◽  
Xianzhi Wang ◽  
Shubin Si ◽  
Xiaoqiang Du
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Infrared Thermography ◽  
Rotating Machinery ◽  
Support Vector ◽  
Intelligent Fault Diagnosis ◽  
Fault Features ◽  
Diagnosis Method ◽  
Systematic Framework ◽  
Machinery Fault Diagnosis

A novel systematic framework, infrared thermography- (IRT-) based method, for rotating machinery fault diagnosis under nonstationary running conditions is presented in this paper. In this framework, IRT technique is first applied to obtain the thermograph. Then, the fault features are extracted using bag-of-visual-word (BoVW) from the IRT images. In the end, support vector machine (SVM) is utilized to automatically identify the fault patterns of rotating machinery. The effectiveness of proposed method is evaluated using lab experimental signal of rotating machinery. The diagnosis results show that the IRT-based method has certain advantages in classification rotating machinery faults under nonstationary running conditions compared with the traditional vibration-based method.

scholarly journals Effects of Phase Shift Errors in Recurrence Plot for Rotating Machinery Fault Diagnosis

2021 ◽  
Vol 11 (2) ◽  
pp. 873
Author(s):  
Ignacio Torres-Contreras ◽  
Juan Carlos Jáuregui-Correa ◽  
Carlos Santiago López-Cajún ◽  
Salvador Echeverría-Villagómez
Keyword(s):  
Fault Diagnosis ◽  
Phase Shift ◽  
Phase Plane ◽  
Vibration Signal ◽  
Recurrence Plot ◽  
Rotating Machinery ◽  
Periodic Functions ◽  
Gaussian Peak ◽  
Phase Errors ◽  
Machinery Fault Diagnosis

For fault diagnosis and predictive maintenance of rotating machinery, the phase errors generated by the integration processing of a vibration signal are an essential investigation subject. Phase errors affect the solution of mechanical systems with multiple vibration sources and also the information transmitted through the vibration that is used for fault diagnosis. This work proposes the use of phase plane, recurrence plot (RP), and cross recurrence plot (CRP) to evaluate phase shift error effects on the solution of multiple asynchronous and simple periodic functions, and on the smoothing of a Gaussian peak with white noise. Noisy peaks were smoothed twice with the triangular method and with a different number of points. The analysis of the asynchronous periodic functions and the smoothing indicated that a small phase shift changes the phase plane and the RP pattern. These changes can affect not only the accuracy of machinery fault diagnosis but also prediction for the application of timely maintenance actions.

scholarly journals A Refined Composite Multivariate Multiscale Fluctuation Dispersion Entropy and Its Application to Multivariate Signal of Rotating Machinery

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 128
Author(s):  
Chenbo Xi ◽  
Guangyou Yang ◽  
Lang Liu ◽  
Hongyuan Jiang ◽  
Xuehai Chen
Keyword(s):  
Fault Diagnosis ◽  
Single Channel ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Support Vector ◽  
Data Set ◽  
Rotary Machine ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Low Sensitivity

In the fault monitoring of rotating machinery, the vibration signal of the bearing and gear in a complex operating environment has poor stationarity and high noise. How to accurately and efficiently identify various fault categories is a major challenge in rotary fault diagnosis. Most of the existing methods only analyze the single channel vibration signal and do not comprehensively consider the multi-channel vibration signal. Therefore, this paper presents Refined Composite Multivariate Multiscale Fluctuation Dispersion Entropy (RCMMFDE), a method which extracts the recognition information of multi-channel signals with different scale factors, and the refined composite analysis ensures the recognition stability. The simulation results show that this method has the characteristics of low sensitivity to signal length and strong anti-noise ability. At the same time, combined with Joint Mutual Information Maximisation (JMIM) and support vector machine (SVM), RCMMFDE-JMIM-SVM fault diagnosis method has been proposed. This method uses RCMMFDE to extract the state characteristics of the multiple vibration signals of the rotary machine, and then uses the JMIM method to extract the sensitive characteristics. Finally, different states of the rotary machine are classified by SVM. The validity of the method is verified by the composite gear fault data set and bearing fault data set. The diagnostic accuracy of the method is 99.25% and 100.00%. The experimental results show that RCMMFDE-JMIM-SVM can effectively recognize multiple signals.

Gearbox Fault Diagnosis Using Adaptive Redundant Second Generation Wavelet

2005 ◽  
Vol 293-294 ◽  
pp. 95-102 ◽  
Author(s):  
Hongkai Jiang ◽  
Zheng Jia He ◽  
Chendong Duan ◽  
Xue Feng Chen
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Optimization Algorithm ◽  
Second Generation ◽  
Vibration Signal ◽  
Vibration Signals ◽  
Air Compressor ◽  
Dominant Structure ◽  
Second Generation Wavelet ◽  
Initial Prediction

Vibration signals acquired from a gearbox usually are complex, and it is difficult to detect the symptoms of an inherent fault in a gearbox. In this paper, an adaptive redundant second generation wavelet (ARSGW) based on second generation wavelet (SGW) is developed. It adopts data-based optimization algorithm to design the initial prediction operator and update operator at each scale. The initial operators are interpolated with zero, and then the redundant prediction operator and update operator are obtained. The splitting step in ARSGW is removed, the approximation signal at each scale is predicted and updated with redundant prediction operator and update operator directly, and the length of approximation signal and detail signal at every scale remains the same, ARSGW eliminates translation variance of SGW. Since the redundant prediction operator and update operator lock on to the dominant structure of the signal, ARSGW can well reveal the characteristics of the signal in time domain. ARSGW is found to be very effective in detection of symptoms from the vibration signal of a large air compressor gearbox with impact rub fault. SGW is also used to analyze the same signal for comparison, no modulation signals and periodic impulses appear at any scale.

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