A New Method in Gears Fault Diagnosis

2013 ◽  
Vol 333-335 ◽  
pp. 1621-1624
Author(s):  
Feng Lei Ma ◽  
Xiao Long Zhou ◽  
Yong Tao Zheng
Keyword(s):  
Fault Diagnosis ◽  
Signal Analysis ◽  
Intrinsic Mode Function ◽  
New Method ◽  
Signal Frequency ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Mode Function ◽  
Meshing Gears

In this paper, the gears fault signal in the engine was studied. Hilbert-Huang transform was applied for the gears fault signal analysis. From the experiment, the normal engine frequency of 240 Hz was got and the gears fault signal frequency concentrated in 2800 Hz. Through the study of intrinsic mode function and the Hilbert spectrum, improper meshing gears were the cause of this problem. The results showed that this method can effectively extract the fault feature and found out the cause of the problem. A new effective method is provided for the gears fault diagnosis.

Application of Hilbert-Huang Transform to Vibration Signal Analysis of Coal and Gangue

2010 ◽  
Vol 40-41 ◽  
pp. 995-999 ◽  
Author(s):  
Wei Liu
Keyword(s):  
Signal Analysis ◽  
Vibration Signal ◽  
Intrinsic Mode Function ◽  
Frequency Interval ◽  
Hilbert Huang Transform ◽  
Mode Function ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
The Difference ◽  
Vibration Signal Analysis

In this paper, a new method of vibration signal analysis of coal and gangue based on Hilbert-Huang transform is presented. Empirical mode decomposition algorithm was used to decompose the original vibration signal of coal and gangue into the intrinsic modes for further extract useful information contained in response signals under complicated environment. By analyzing local Hilbert marginal spectrum and local energy spectrum of the first four intrinsic mode function components, we found the difference of coal and rock in specific frequency interval that the amplitude and energy mainly distributed at frequency interval between 100Hz and 600Hz when coal was drawn, while the amplitude and energy were more concentrated at 1000Hz or so when gangue was drawn. Furthermore, the further analysis result from marginal spectrum of each intrinsic mode function component agreed well with the conclusion above. So the extracted features with the propose approach can be served as coal and gangue interface recognition.

Gearbox Fault Diagnosis Based on Empirical Mode Decomposition and Hilbert Transform

2012 ◽  
Vol 542-543 ◽  
pp. 238-241
Author(s):  
Yan Li Liu ◽  
De Xiang Zhang ◽  
Ming Wei Ji
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Hilbert Transform ◽  
Intrinsic Mode Function ◽  
Hilbert Spectrum ◽  
Time Frequency ◽  
Mode Function ◽  
Mode Decomposition ◽  
Wide Range ◽  
Vibration Signal Analysis

Gearbox is vital components in a wide range of industrial and transport applications. It is very important how to monitor operating state of automobile gearbox and detect incipient faults. This paper applies the empirical mode decomposition (EMD) and Hilbert spectrum methods to gearbox vibration signal analysis capture from vibrating acceleration sensor for gearbox fault diagnosis. The original modulation fault vibration signals are firstly decomposed into a number of intrinsic mode function (IMF) by the EMD method. Then Hilbert spectrum of intrinsic mode function at different fault characteristic frequencies is obtained by Hilbert transform. Finally, the time-frequency fault characteristics of gearbox are analyzed by the Hilbert spectrum value of intrinsic mode function. Experiment result has shown the feasibility and efficiency of the EMD algorithms and Hilbert spectrum characteristic method in fault diagnosis and fault message abstraction.

scholarly journals Fault Diagnosis of Rolling Bearing Based on Multiscale Intrinsic Mode Function Permutation Entropy and a Stacked Sparse Denoising Autoencoder

2019 ◽  
Vol 9 (13) ◽  
pp. 2743 ◽  
Author(s):  
Dai ◽  
Tang ◽  
Shao ◽  
Huang ◽  
Wang
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Health Condition ◽  
Intrinsic Mode Function ◽  
Permutation Entropy ◽  
Diagnostic Model ◽  
Denoising Autoencoder ◽  
Essential Information ◽  
Mode Function

Effective intelligent fault diagnosis of bearings is important for improving safety and reliability of machine. Benefiting from the training advantages, deep learning method can automatically and adaptively learn more abstract and high-level features without much priori knowledge. To realize representative features mining and automatic recognition of bearing health condition, a diagnostic model of stacked sparse denoising autoencoder (SSDAE) which combines sparse autoencoder (SAE) and denoising autoencoder (DAE) is proposed in this paper. The sparse criterion in SAE, corrupting operation in DAE and reasonable designing of the stack order of autoencoders help to mine essential information of the input and improve fault pattern classification robustness. In order to provide better input features for the constructed network, the raw non-stationary and nonlinear vibration signals are processed with ensemble empirical mode decomposition (EEMD) and multiscale permutation entropy (MPE). MPE features which are extracted based on both the selected characteristic frequency-related intrinsic mode function components (IMFs) and the raw signal, are used as low-level feature for the input of the proposed diagnostic model for health condition recognition and classification. Two experiments based on the Case Western Reserve University (CWRU) dataset and the measurement dataset from laboratory were conducted, and results demonstrate the effectiveness of the proposed method and highlight its excellent performance relative to existing methods.

scholarly journals Intelligent fault diagnosis of rotating machinery based on deep learning with feature selection

2019 ◽  
Vol 39 (4) ◽  
pp. 939-953 ◽  
Author(s):  
Dongying Han ◽  
Kai Liang ◽  
Peiming Shi
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Time Domain ◽  
Deep Neural Network ◽  
Intrinsic Mode Function ◽  
Mode Function ◽  
Mode Decomposition ◽  
Fault Features

In the absence of a priori knowledge, manual feature selection is too blind to find the sensitive features which can effectively classify the different fault features. And it is difficult to obtain a large number of typical fault samples in practice to train the intelligent classifier. A novel intelligent fault diagnosis method based on feature selection and deep learning is proposed for rotating machine mechanical in the paper. In this method, the deep neural network is not only used for feature extraction but also for fault diagnosis. First, the deep neural network 1 is used to extract feature from the spectral signal of the original signal. In addition, the original vibration signal is decomposed to a series of intrinsic mode function components by empirical mode decomposition, and the statistical features of each intrinsic mode function component are extracted by the deep neural network 2 in time domain and frequency domain. Second, the extraction features of the original signal spectrum and the extraction features of each intrinsic mode function component are evaluated, respectively. After features evaluation, the selected sensitive features are combined together to construct a joint feature. Finally, the joint feature is put into the deep neural network 3 to realize the automatic recognition of different fault states of rotating machinery. The experimental results show that the method proposed in this paper which integrated time-domain, frequency-domain statistical characteristics, empirical mode decomposition, feature selection, and deep learning methods can obtain the fault information in detail and can select sensitive features from a large number of fault features. The method can reduce the network size, improve the mechanical fault diagnosis classification accuracy, and has strong robustness.

Vibration Signal Analysis of Bearing Based on EMD and Resonance Demodulation

2014 ◽  
Vol 556-562 ◽  
pp. 1286-1289 ◽  
Author(s):  
Jie Shi ◽  
Xing Wu ◽  
Nan Pan ◽  
Sen Wang ◽  
Jun Zhou
Keyword(s):  
Fault Diagnosis ◽  
Correlation Coefficient ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Experimental Station ◽  
Fault Type ◽  
Mode Function ◽  
Vibration Signal Analysis

In order to monitor the operation state and implement fault diagnosis of rolling bearing in rotating machinery, this paper presents a method of fault diagnosis of rolling bearing, which is based on EMD and resonance demodulation. Using EMD to decompose the signal, which comes from QPZZ-II experimental station, the components of intrinsic mode function (IMF) will be obtained. Then, calculating the correlation coefficient of each IMF component, the highest correlation coefficient of IMF component will be analyzed by resonance demodulation. Finally, the experimental results show that the method can accurately identify and diagnose the running state and bearing fault type.

Gearbox Fault Diagnosis Based on EMD and Coefficient-Energy Value

2012 ◽  
Vol 542-543 ◽  
pp. 234-237
Author(s):  
Ping Wang ◽  
De Xiang Zhang ◽  
Yan Li Liu
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Intrinsic Mode Function ◽  
Vibration Signals ◽  
Energy Value ◽  
Mode Function ◽  
Mode Decomposition ◽  
Gearbox Vibration ◽  
Vibration Signal Analysis ◽  
Fault Information

This paper applies the empirical mode decomposition (EMD) methods to gearbox vibration signal analysis capture from vibrating acceleration sensor for gearbox fault diagnosis. The original modulation fault vibration signals are firstly decomposed into a number of intrinsic mode function (IMF) by the EMD method. Then the fault information diagnosis of the gearbox vibration signals can be extracted from the coefficient-energy value of intrinsic mode function. Experiment result has shown the feasibility and efficiency of the EMD algorithms and energy characteristic method in fault diagnosis and fault message abstraction. It is significant for the monitor operating state of gearbox and detects incipient faults as soon as possible.

BUILT-IN TEST SIGNAL FEATURE EXTRACTION METHOD BASED ON HILBERT–HUANG TRANSFORM

2012 ◽  
Vol 04 (01n02) ◽  
pp. 1250005
Author(s):  
MIAO ZHANG ◽  
YI SHEN ◽  
XIAO-LEI ZHANG ◽  
ZHI-BO WANG ◽  
YE ZHANG
Keyword(s):  
Feature Extraction ◽  
Data Analysis ◽  
Fault Diagnosis ◽  
Extraction Method ◽  
Feature Extraction Method ◽  
Hilbert Huang Transform ◽  
Mode Function ◽  
Reference Signals ◽  
Voltage Signal ◽  
Self Adaptation

This work proposes a feature extraction method from Hilbert–Huang-transform- or HHT-based data analysis of built-in test (BIT) signals, which are sampled on-site and without reference signals for fault diagnosis. The proposed method fully utilizes self-adaptation of the HHT method in characterizing the envelope amplitude and instantaneous frequency for the intrinsic mode function (IMF), so as to single out the features with most irregular characteristics. Simulations are carried out on steering gear feedback voltage signal of target drone aircraft, and the extracted features show great potential for the improvement in built-in fault diagnosis.

Sign in / Sign up

Export Citation Format

Share Document