Gear Local Fault Diagnosis with Empirical Mode Decomposition and Hilbert Huang Transformation

2011 ◽  
Vol 199-200 ◽  
pp. 899-904 ◽  
Author(s):  
Zhen Nan Han ◽  
Jian Xin Gao
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Instantaneous Frequency ◽  
Signal Analysis ◽  
Physical Simulation ◽  
Gear Tooth ◽  
Vibration Signal ◽  
Vibration Energy ◽  
Mode Decomposition ◽  
Vibration Signal Analysis

A new method for gear local fault diagnosis based on vibration signal analysis is presented in this paper by using the concept of instantaneous frequency. The data from the physical simulation are used to detect the change in the instantaneous frequency and meshing vibration energy of the gear tooth fault by Empirical Mode Decomposition and Hilbert Huang Transformation (EMD-HHT). It is verified that method is effective by rig testing of geared system.

Diagnosis of Low-Speed Helical Gears Based on Hilbert Demodulation and Empirical Mode Decomposition

2013 ◽  
Vol 572 ◽  
pp. 443-446
Author(s):  
Bao Yu Song ◽  
Zhi Jie Xie ◽  
Feng Zhang ◽  
Jin Ping Yang
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Intrinsic Mode Functions ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Helical Gears ◽  
Low Speed ◽  
Mode Decomposition ◽  
Vibration Signal Analysis

As the vibration signal of helical gears is nonlinear and nonstationary, it is very difficult to diagnose their faults based on the vibration signal analysis and processing, particularly when the gears rotate at low speed. In this paper, an applicable fault diagnosis approach is proposed based on Hilbert demodulation and EMD (empirical mode decomposition). Firstly, the modulated signals are extracted through Hilbert envelope demodulationand low pass filter. Furthermore, EMD is used to decompose the multi-component demodulated signal into a series of intrinsic mode functions (IMFs) whose instantaneous frequencies have a physically meaningful characterization of the original signal. Finally, the fault features of low-speed helical gear are obtained by the spectrum analysis to each IMF. The experiments of tooth broken fault diagnosis show that this method is more effective than traditional Hilbert demodulation analysis.

A fault diagnosis method for rolling element bearing (REB) based on reducing REB foundation vibration and noise-assisted vibration signal analysis

2018 ◽  
Vol 233 (7) ◽  
pp. 2574-2587 ◽  
Author(s):  
Heng-di Wang ◽  
Si-er Deng ◽  
Jian-xi Yang ◽  
Hui Liao
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Foundation Vibration ◽  
Diagnosis Method ◽  
Element Bearing ◽  
Vibration Signal Analysis

Owing to the problem of the incipient fault characteristics being difficult to be extracted from the raw vibration signal of rolling element bearing, based on the empirical mode decomposition and kurtosis criteria, a fault diagnosis method for rolling element bearing is proposed by reducing rolling element bearing foundation vibration and noise-assisted vibration signal analysis. Firstly, rolling element bearing vibration signal is decomposed into a set of intrinsic mode functions using empirical mode decomposition and the intrinsic mode function component with the maximal kurtosis value is selected. Afterwards, zero mean normalization is applied to the selected intrinsic mode function component, and then the intrinsic mode function’s foundation vibration components within [Formula: see text] are removed to minimize the interference. In order to eliminate interruption and intermittency after removal of the foundation vibration components, white noise is added to the newly generated signal. The noise-added signal is decomposed via empirical mode decomposition, and later on, IMF1 with the highest frequency band is selected and demodulated using envelope analysis. The resulting envelope spectrum can show more significant fault pulse characteristics, which are highly helpful to diagnose the rolling element bearing incipient faults. The proposed method in this paper was applied to the fault diagnosis for low noise REB 6203 and the testing results showed that the method could identify the rolling element bearing incipient faults accurately and quickly.

scholarly journals Research on Multidomain Fault Diagnosis of Large Wind Turbines under Complex Environment

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Rong Jia ◽  
Fuqi Ma ◽  
Jian Dang ◽  
Guangyi Liu ◽  
Huizhi Zhang
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Empirical Mode Decomposition ◽  
Wind Turbines ◽  
Vibration Signal ◽  
Characteristic Parameters ◽  
Feature Extraction Method ◽  
Feature Vectors ◽  
Mode Decomposition ◽  
Large Wind

Under the complicated environment of large wind turbines, the vibration signal of a wind turbine has the characteristics of coupling and nonlinearity. The traditional feature extraction method for the signal is hard to accurately extract fault information, and there is a serious problem of information redundancy in fault diagnosis. Therefore, this paper proposed a multidomain feature fault diagnosis method based on complex empirical mode decomposition (CEMD) and random forest theory (RF). Firstly, this paper proposes a novel method of complex empirical mode decomposition by using the correlation information between two-dimensional signals and utilizing the idea of ensemble empirical mode decomposition (EEMD) by adding white noise to suppress the problem mode mixing in empirical mode decomposition (EMD). Secondly, the collected vibration signals are decomposed into IMFs by CEMD. Then, calculate 11 time domain characteristic parameters and 13 frequency domain characteristic parameters of the vibration signal, and calculate the energy and energy entropy of each IMF components. Make all the characteristic parameters as the multidomain feature vectors of wind turbines. Finally, the redundant feature vectors are eliminated by the importance of each feature vector which has been calculated, and the feature vectors selected are input to the random forest classifier to achieve the fault diagnosis of large wind turbines. Simulation and experimental results show that this method can effectively extract the fault feature of the signal and achieve the fault diagnosis of wind turbines, which has a higher accuracy of fault diagnosis than the traditional classification methods.

Research on the Gear Box Fault Diagnosis of Self-Propelled Artillery Based on Vibration Signal Analysis

2014 ◽  
Vol 940 ◽  
pp. 136-139
Author(s):  
Ren Bin Zhou ◽  
Yong Feng Zhang ◽  
Jie Min Yang ◽  
Feng Ling
Keyword(s):  
Fault Diagnosis ◽  
Data Acquisition ◽  
Real Time ◽  
Signal Analysis ◽  
Great Influence ◽  
Vibration Signal ◽  
Vibration Data ◽  
Gearbox Vibration ◽  
Gear Box ◽  
Vibration Signal Analysis

As a universal component connection and power transmission gear box, is widely used in the modern industrial equipment, but also an easy failure parts, has a great influence on the running state of the working performance of the whole machine. This paper first analyzes the gear box fault form and characteristics, the gear box fault diagnosis method based on vibration signal analysis, and analysis of the vibration signal processing method for gear vibration signal analysis in time domain, including parameters, resonance demodulation method and cepstrum analysis method. Then using Visual C + + language and data acquisition card for real-time acquisition of gearbox vibration data software, including parameter setting, data acquisition module, signal real-time display module and data storage module. The data acquisition program is developed, the actual acquisition of gearbox vibration data of gear fault and bearing fault, and analyzed.

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.

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