scholarly journals Spur Gear Fault Diagnosis Using a Multilayer Gated Recurrent Unit Approach With Vibration Signal

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 56880-56889 ◽  
Author(s):  
Ying Tao ◽  
Xiaodan Wang ◽  
Rene-Vinicio Sanchez ◽  
Shuai Yang ◽  
Yun Bai
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Spur Gear ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
Gated Recurrent Unit

Gear Fault Diagnosis Methods Based on EMD and HMM

2013 ◽  
Vol 333-335 ◽  
pp. 1684-1687
Author(s):  
Bin Wu ◽  
Song He Zhang ◽  
Yue Gang Luo ◽  
Shan Ping Yu
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Modulation Amplitude ◽  
Double Frequency ◽  
Gear Mesh ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Modulation Signal ◽  
Gear Fault Diagnosis ◽  
Modulation Sideband

Due to the feature and the forms of motion of the gears, the vibration signal of the gear is mainly the frequency modulation, amplitude modulation, or hybrid modulation signal corresponding to the gear-mesh frequency and its double frequency signal. When faults arise on the gears, the number and shape of the modulation sideband will be changed. The structures and forms of the FM composition differ according to the type of faults. According to the above mentioned characteristic, this essay raises a method to disassemble the gear vibrate signal, points out the formulas to build up characteristic vector, on that basis, the essay raised a gear fault diagnosis method based on EMD and Hidden Markov Model (HMM), this method can identify the working condition of the normal gears, snaggletooth gears, and pitting gears.

Gear fault diagnosis based on time–frequency domain de-noising using the generalized S transform

2017 ◽  
Vol 24 (15) ◽  
pp. 3338-3347 ◽  
Author(s):  
Jianhua Cai ◽  
Xiaoqin Li
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Vibration Signal ◽  
Time Frequency ◽  
Transmission Modes ◽  
S Transform ◽  
Mining Machinery ◽  
Gear Fault ◽  
Generalized S Transform ◽  
Gear Fault Diagnosis

Gears are the most important transmission modes used in mining machinery, and gear faults can cause serious damage and even accidents. In the work process, vibration signals are influenced not only by friction, nonlinear stiffness, and nonstationary loads, but also by strong noise. It is difficult to separate the useful information from the noise, which brings some trouble to the fault diagnosis of mining machinery gears. The generalized S transform has the advantages of the short time Fourier transform and wavelet transform and is reversible. The time–frequency energy distribution of the gear vibration signal can be accurately presented by the generalized S transform, and a time–frequency filter factor can be constructed to filter the vibration signal in the time–frequency domain. These characteristics play an important role when the generalized S transform is used to remove the noise in the time–frequency domain. In this paper, a new gear fault diagnosis based on the time–frequency domain de-noising is proposed that uses the generalized S transform. The application principle, method steps, and evaluation index of the method are presented, and a wavelet soft-threshold filtering method is implemented for comparison with the proposed approach. The effectiveness of the proposed method is demonstrated by numerical simulation and experimental investigation of a gear with a tooth crack. Our analyses also indicate that the proposed method can be used for fault diagnosis of mining machinery gears.

Wind Turbine Gear Fault Diagnosis Experiment Research Based on Sensorless Detection

2013 ◽  
Vol 427-429 ◽  
pp. 1191-1195
Author(s):  
Jun Shan Si ◽  
Hui Zhu ◽  
Jian Yu ◽  
Qing Chun Meng ◽  
Xian Jiang Shi
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Fault Simulation ◽  
Vibration Signal ◽  
Vibration Monitoring ◽  
Test Bed ◽  
Experiment Research ◽  
Detection Techniques ◽  
Gear Fault ◽  
Gear Fault Diagnosis

In the wind turbine gear fault detection, using conventional vibration monitoring exists installation and maintenance inconvenience and numerous other shortcomings, this often leads to the wind turbine vibration detection techniques are not widely promoted. Based on sensorless wind turbine gear fault diagnosis experiment research in this paper, the use of general-purpose inverter and induction motor, built a double-fed asynchronous induction generator simulation test bed, and had a broken tooth gear fault simulation experiments. Through the generator stator current signal and the gear vibration signal contrast and analysis, preliminary validation of the proposed method is effective and feasible.

scholarly journals Spur gear fault diagnosis using FIR and IIR Filter

IARJSET ◽  
2017 ◽  
Vol 4 (3) ◽  
pp. 68-71
Author(s):  
Sanjeev Kumar ◽  
Ghanshyam Das
Keyword(s):  
Fault Diagnosis ◽  
Spur Gear ◽  
Iir Filter ◽  
Gear Fault ◽  
Gear Fault Diagnosis

scholarly journals Gear Fault Diagnosis Based on Kurtosis Criterion VMD and SOM Neural Network

2019 ◽  
Vol 9 (24) ◽  
pp. 5424 ◽  
Author(s):  
Dongming Xiao ◽  
Jiakai Ding ◽  
Xuejun Li ◽  
Liangpei Huang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Characteristic Curve ◽  
Vibration Signal ◽  
Good Effect ◽  
Feature Vectors ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Som Neural Network ◽  
Gear Fault Diagnosis

A gear fault diagnosis method based on kurtosis criterion variational mode decomposition (VMD) and self-organizing map (SOM) neural network is proposed. Firstly, the VMD algorithm is used to decompose the gear vibration signal, and the instantaneous frequency mean is calculated as the evaluation index, and the characteristic curve is drawn to screen out the most relevant intrinsic mode functions (IMFs) of the original vibration signal. Then, the number of VMD decompositions is determined, and the kurtosis value of IMFs are extracted to form the feature vectors. Then, the kurtosis value feature vectors of IMFs are normalized to form the kurtosis value normalized vectors. Finally, the normalized vectors of kurtosis value are input into SOM neural network to realize gear fault diagnosis. When the number of training times of SOM neural network is 100, the gear fault category is accurately classified by SOM neural network. The results show that when the training times of SOM neural network is 100 times, the gear fault diagnosis method, based on the kurtosis criterion VMD and SOM neural network is 100%, which indicates that the new method has a good effect on gear fault diagnosis.

Research of Gear Fault Comprehensive Diagnosis Method Based on Analysis of Vibration Signal

2014 ◽  
Vol 898 ◽  
pp. 892-895
Author(s):  
Zhan Jie Lv ◽  
Wen Xu ◽  
Gui Ji Tang ◽  
Guo Dong Han ◽  
Shu Ting Wan
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
The Time Domain ◽  
Envelope Spectrum ◽  
Signal Processing Methods

For gearbox common type of fault, leads to common methods gear fault diagnosis, according to the various parameters of the gearbox, to give a gearbox fault frequencies. Using mat lab signal analysis, by the time domain analysis, frequency domain analysis, cestrum analysis, signal processing methods envelope spectrum consolidated results there is a fault in the gearbox countershaft. This papers they have certain significance to gear fault diagnosis.

Sign in / Sign up

Export Citation Format

Share Document