scholarly journals Gear Fault Diagnosis using Bispectrum Analysis of ANC based Adaptive Filtered Sound and Vibration Signal

2013 ◽  
Vol 18 (2) ◽  
Author(s):  
Dibya Prakash Jena ◽  
S. N. Panigrahi
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Gear Fault ◽  
Gear Fault Diagnosis

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 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.

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.

Envelope demodulation based on variational mode decomposition for gear fault diagnosis

2016 ◽  
Vol 231 (4) ◽  
pp. 864-870 ◽  
Author(s):  
Xueli An ◽  
Hongtao Zeng ◽  
Chaoshun Li
Keyword(s):  
Fault Diagnosis ◽  
Amplitude Modulation ◽  
Decomposition Method ◽  
Modulation Frequency ◽  
Vibration Signal ◽  
Variational Mode Decomposition ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
Component Amplitude

A new time–frequency analysis method, based on variational mode decomposition, was investigated. When a gear fault occurs, its vibration signal is nonstationary, nonlinear, and exhibits complex modulation performance. According to the modulation characteristics of the gear vibration signal arising from faults therein, a gear fault diagnosis method based on variational mode decomposition and envelope analysis was proposed. The variational mode decomposition method can decompose a complex signal into several stable components. The obtained components were analyzed by envelope demodulation. According to the envelope spectrum, gear faults can be diagnosed. In essence, the variational mode decomposition method can decompose a multi-component signal into a number of single component amplitude modulation–frequency modulation signals. The method is suited to the handling of multi-component amplitude modulation–frequency modulation signals. The simulated signal and the actual gear fault vibration signals were analyzed. The results showed that the method can be effectively applied to gear fault diagnosis.

scholarly journals Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning

2020 ◽  
Vol 33 (2) ◽  
pp. 418-426 ◽  
Author(s):  
Xueyi LI ◽  
Jialin LI ◽  
Yongzhi QU ◽  
David HE
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Gear Fault ◽  
Gear Fault Diagnosis

scholarly journals Learning Attention Representation with a Multi-Scale CNN for Gear Fault Diagnosis under Different Working Conditions

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1233 ◽  
Author(s):  
Yong Yao ◽  
Sen Zhang ◽  
Suixian Yang ◽  
Gui Gui
Keyword(s):  
Fault Diagnosis ◽  
Working Conditions ◽  
Vibration Signal ◽  
Multiple Scale ◽  
Attention Mechanism ◽  
Signal Acquisition ◽  
Multi Scale ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
Vibration Signal Analysis

The gear fault signal under different working conditions is non-linear and non-stationary, which makes it difficult to distinguish faulty signals from normal signals. Currently, gear fault diagnosis under different working conditions is mainly based on vibration signals. However, vibration signal acquisition is limited by its requirement for contact measurement, while vibration signal analysis methods relies heavily on diagnostic expertise and prior knowledge of signal processing technology. To solve this problem, a novel acoustic-based diagnosis (ABD) method for gear fault diagnosis under different working conditions based on a multi-scale convolutional learning structure and attention mechanism is proposed in this paper. The multi-scale convolutional learning structure was designed to automatically mine multiple scale features using different filter banks from raw acoustic signals. Subsequently, the novel attention mechanism, which was based on a multi-scale convolutional learning structure, was established to adaptively allow the multi-scale network to focus on relevant fault pattern information under different working conditions. Finally, a stacked convolutional neural network (CNN) model was proposed to detect the fault mode of gears. The experimental results show that our method achieved much better performance in acoustic based gear fault diagnosis under different working conditions compared with a standard CNN model (without an attention mechanism), an end-to-end CNN model based on time and frequency domain signals, and other traditional fault diagnosis methods involving feature engineering.

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