scholarly journals Gear Crack Level Classification Based on EMD and EDT

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Haiping Li ◽  
Jianmin Zhao ◽  
Xinghui Zhang ◽  
Hongzhi Teng
Keyword(s):  
Vibration Signal ◽  
Test Sample ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Euclidean Distances ◽  
Accuracy Rates ◽  
Gear Crack ◽  
Fault Information

Gears are the most essential parts in rotating machinery. Crack fault is one of damage modes most frequently occurring in gears. So, this paper deals with the problem of different crack levels classification. The proposed method is mainly based on empirical mode decomposition (EMD) and Euclidean distance technique (EDT). First, vibration signal acquired by accelerometer is processed by EMD and intrinsic mode functions (IMFs) are obtained. Then, a correlation coefficient based method is proposed to select the sensitive IMFs which contain main gear fault information. And energy of these IMFs is chosen as the fault feature by comparing with kurtosis and skewness. Finally, Euclidean distances between test sample and four classes trained samples are calculated, and on this basis, fault level classification of the test sample can be made. The proposed approach is tested and validated through a gearbox experiment, in which four crack levels and three kinds of loads are utilized. The results show that the proposed method has high accuracy rates in classifying different crack levels and may be adaptive to different conditions.

scholarly journals Fault Identification of Gearbox Degradation with Optimized Wavelet Neural Network

2013 ◽  
Vol 20 (2) ◽  
pp. 247-262 ◽  
Author(s):  
Hanxin Chen ◽  
Yanjun Lu ◽  
Ling Tu
Keyword(s):  
Neural Network ◽  
Wavelet Packet ◽  
Back Propagation ◽  
Experimental System ◽  
Vibration Signal ◽  
Wavelet Neural Network ◽  
Acceleration Sensors ◽  
Gear Fault ◽  
Gear Crack

A novel intelligent method based on wavelet neural network (WNN) was proposed to identify the gear crack degradation in gearbox in this paper. The wavelet packet analysis (WPA) is applied to extract the fault feature of the vibration signal, which is collected by two acceleration sensors mounted on the gearbox along the vertical and horizontal direction. The back-propagation (BP) algorithm is studied and applied to optimize the scale and translation parameters of the Morlet wavelet function, the weight coefficients, threshold values in WNN structure. Four different gear crack damage levels under three different loads and three various motor speeds are presented to obtain the different gear fault modes and gear crack degradation in the experimental system. The results show the feasibility and effectiveness of the proposed method by the identification and classification of the four gear modes and degradation.

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 Gear Fault Detection Based on Teager-Huang Transform

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Hui Li ◽  
Haiqi Zheng ◽  
Liwei Tang
Keyword(s):  
Fault Detection ◽  
Energy Operator ◽  
Vibration Signal ◽  
Intrinsic Mode Functions ◽  
Instantaneous Amplitude ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Novel Method ◽  
Detection And Diagnosis

Gear fault detection based on Empirical Mode Decomposition (EMD) and Teager Kaiser Energy Operator (TKEO) technique is presented. This novel method is named as Teager-Huang transform (THT). EMD can adaptively decompose the vibration signal into a series of zero mean Intrinsic Mode Functions (IMFs). TKEO can track the instantaneous amplitude and instantaneous frequency of the Intrinsic Mode Functions at any instant. The experimental results provide effective evidence that Teager-Huang transform has better resolution than that of Hilbert-Huang transform. The Teager-Huang transform can effectively diagnose the fault of the gear, thus providing a viable processing tool for gearbox defect detection and diagnosis.

Gear Fault Diagnosis Method Based on the Integration of Empirical Mode Decomposition and Cepstrum Analysis

2013 ◽  
Vol 310 ◽  
pp. 328-333 ◽  
Author(s):  
Bing Luo ◽  
Wen Tong Yang ◽  
Zhi Feng Liu ◽  
Yong Sheng Zhao ◽  
Li Gang Cai
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Mechanical Transmission ◽  
Intrinsic Mode Functions ◽  
Cepstrum Analysis ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Gear Fault Diagnosis

Gear is the most common mechanical transmission equipment. Therefore, gear fault diagnosis is of much significance. In this article, a gear fault diagnosis method based on the integration of empirical mode decomposition and cepstrum is proposed by introducing empirical mode decomposition and cepstrum into gear fault analysis. Firstly EMD is used to decompose the gear vibration signal finite number of intrinsic mode functions and a residual error item. To do gear fault diagnosis, cepstrum analysis is carried upon those intrinsic mode functions to extract feature information from the vibration signal. The results of the study on simulated and experimental signals show that this method is better than the cepstrum method and it can precisely locate the site of gear failure.

Analysis of statistical coefficients and autoregressive parameters over intrinsic mode functions (IMFs) for epileptic seizure detection

2020 ◽  
Vol 65 (6) ◽  
pp. 693-704
Author(s):  
Rafik Djemili
Keyword(s):  
Involuntary Movement ◽  
Epileptic Seizures ◽  
Intrinsic Mode Functions ◽  
Eeg Signals ◽  
Time Segment ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Finite Set ◽  
Student’S T

AbstractEpilepsy is a persistent neurological disorder impacting over 50 million people around the world. It is characterized by repeated seizures defined as brief episodes of involuntary movement that might entail the human body. Electroencephalography (EEG) signals are usually used for the detection of epileptic seizures. This paper introduces a new feature extraction method for the classification of seizure and seizure-free EEG time segments. The proposed method relies on the empirical mode decomposition (EMD), statistics and autoregressive (AR) parameters. The EMD method decomposes an EEG time segment into a finite set of intrinsic mode functions (IMFs) from which statistical coefficients and autoregressive parameters are computed. Nevertheless, the calculated features could be of high dimension as the number of IMFs increases, the Student’s t-test and the Mann–Whitney U test were thus employed for features ranking in order to withdraw lower significant features. The obtained features have been used for the classification of seizure and seizure-free EEG signals by the application of a feed-forward multilayer perceptron neural network (MLPNN) classifier. Experimental results carried out on the EEG database provided by the University of Bonn, Germany, demonstrated the effectiveness of the proposed method which performance assessed by the classification accuracy (CA) is compared to other existing performances reported in the literature.

A method of eliminating the vibration signal noise of hydropower unit based on NA-MEMD and approximate entropy

2016 ◽  
Vol 231 (2) ◽  
pp. 317-328 ◽  
Author(s):  
Xueli An ◽  
Junjie Yang
Keyword(s):  
Empirical Mode Decomposition ◽  
Real World ◽  
Approximate Entropy ◽  
Vibration Signal ◽  
Signal Denoising ◽  
Intrinsic Mode Functions ◽  
Denoising Method ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Simulation Signal

A new vibration signal denoising method of hydropower unit based on noise-assisted multivariate empirical mode decomposition (NA-MEMD) and approximate entropy is proposed. Firstly, the NA-MEMD is used to decompose the signal into a number of intrinsic mode functions. Then, the approximate entropy of each component is computed. According to a preset threshold of approximate entropy, these components are reconstructed to denoise vibration signal of hydropower unit. The analysis results of simulation signal and real-world signal show that the proposed method is adaptive and has a good denoising performance. It is very suitable for online denoising of hydropower unit's vibration signal.

Research on an Algorithm and System for Estimation of Instantaneous Frequency of Rotating Machinery

2012 ◽  
Vol 452-453 ◽  
pp. 153-159
Author(s):  
Rong Qing Yao
Keyword(s):  
Embedded System ◽  
Instantaneous Frequency ◽  
Vibration Signal ◽  
Average Frequency ◽  
Rotating Machinery ◽  
Intrinsic Mode Functions ◽  
System Technology ◽  
Mode Decomposition ◽  
Phase Signal ◽  
The Embedded System

Instantaneous frequency is an import parameter to diagnose faults of rotating machinery. This paper puts forward an algorithm based Hilbert-Huang Transformation (HHT) to estimate the instantaneous frequency of rotating machinery and develops an instantaneous cymometer based embedded system technology. In order to estimate instantaneous frequency of rotating machinery, the vibration signal is decomposed into a series of intrinsic mode functions (IMF) first by the method of empirical mode decomposition (EMD), then one of the intrinsic mode functions is analyzed with the Hilbert transformation to acquire an estimate value of instantaneous frequency. An instantaneous cymometer is also described in this paper, which is designed to measure the average frequency and instantaneous frequency of rotating machinery in real time. The average frequency is acquired from measuring the cycle of key-phase signal, and the instantaneous frequency is from the above-mentioned method based HHT. The instantaneous cymometer is consisted of an embedded system, which is connected to a PC with an Ethernet. The embedded system is based on an ARM chip (Samsung S3C4510) A/D conversion, EMD and Hilbert transform are completed on the embedded system, and then the results are compressed and sent to the PC by TCP/IP.

scholarly journals Study on Frequency Characteristics of Rotor Systems for Fault Detection Using Variational Mode Decomposition

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Kai Chen ◽  
Xin-Cong Zhou ◽  
Jun-Qiang Fang ◽  
Li Qin
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Intrinsic Mode Functions ◽  
Rotor Systems ◽  
Multicomponent Signal ◽  
Mode Decomposition ◽  
Nonlinear Features ◽  
Adaptive Signal Decomposition ◽  
Adaptive Signal

Due to the complicated structure, vibration signal of rotating machinery is multicomponent with nonstationary and nonlinear features, so it is difficult to diagnose faults effectively. Therefore, effective extraction of vibration signal characteristics is the key to diagnose the faults of rotating machinery. Mode mixing and illusive components existed in some conventional methods, such as EMD and EEMD, which leads to misdiagnosis in extracting signals. Given these reasons, a new fault diagnosis method, namely, variation mode decomposition (VMD), was proposed in this paper. VMD is a newly developed technique for adaptive signal decomposition, which can decompose a multicomponent signal into a series of quasi-orthogonal intrinsic mode functions (IMFs) simultaneously, corresponding to the components of signal clearly. To further research on VMD method, the advantages and characteristics of VMD are investigated via numerical simulations. VMD is then applied to detect oil whirl and oil whip for rotor systems fault diagnosis via practical vibration signal. The experimental results demonstrate the effectiveness of VMD method.

A NEW APPLICATION OF ENSEMBLE EMD AMELIORATING THE ERROR FROM INSUFFICIENT SAMPLING RATE

2011 ◽  
Vol 03 (04) ◽  
pp. 493-508 ◽  
Author(s):  
DISHAN HUANG ◽  
YULIN XU
Keyword(s):  
Ball Bearing ◽  
Broad Band ◽  
Sampling Rate ◽  
Vibration Signal ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Ensemble Mean ◽  
Mixed Noise ◽  
Bearing Vibration ◽  
The Mean

The objective of this paper is to apply an assisted noise method for ameliorating the empirical mode decomposition (EMD) error from insufficient sampling rate for a vibration signal. When the intrinsic mode functions (IMFs) are extracted from a signal mixed noise at a certain level on the sifting algorithm, an extraordinary phenomenon, where noise submerges the EMD error, is discovered. Thus, noise-assisted data is proposed to disturb the EMD error in the sifting process. In order to cancel out noise after serving its purpose, the IMFs are processed with an ensemble mean. As a result, the noise-assisted data ameliorates the EMD error from insufficient sampling rate, and the method treats the mean as the final true result. An EMD example of ball bearing vibration is presented to illustrate the validity of the approach. This paper recommends implementing the noise-assisted method in the EMD on vibration and acoustic signals with broad band.

scholarly journals Time-Frequency Characteristics Analysis on Vibration Signals of a Three-Supported Rotor System with Misalignment

2011 ◽  
Vol 2-3 ◽  
pp. 717-721 ◽  
Author(s):  
Xiao Xuan Qi ◽  
Mei Ling Wang ◽  
Li Jing Lin ◽  
Jian Wei Ji ◽  
Qing Kai Han
Keyword(s):  
Wavelet Packet ◽  
Vibration Signal ◽  
Dominant Frequency ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal ◽  
Characteristics Analysis ◽  
Novel Method ◽  
Misalignment Fault

In light of the complex and non-stationary characteristics of misalignment vibration signal, this paper proposed a novel method to analyze in time-frequency domain under different working conditions. Firstly, decompose raw misalignment signal into different frequency bands by wavelet packet (WP) and reconstruct it in accordance with the band energy to remove noises. Secondly, employ empirical mode decomposition (EMD) to the reconstructed signal to obtain a certain number of stationary intrinsic mode functions (IMF). Finally, apply further spectrum analysis on the interested IMFs. In this way, weak signal is caught and dominant frequency is picked up for the diagnosis of misalignment fault. Experimental results show that the proposed method is able to detect misalignment fault characteristic frequency effectively.

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