scholarly journals Application of Adaptive Local Iterative Filtering and Permutation Entropy in Gear Fault Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wenbin Zhang ◽  
Yun Wang ◽  
Yushuo Tan ◽  
Dewei Guo ◽  
Yasong Pu
Keyword(s):  
Vibration Signal ◽  
Intrinsic Mode Function ◽  
Gear System ◽  
Permutation Entropy ◽  
Intrinsic Mode Functions ◽  
Mode Function ◽  
Gray Correlation Degree ◽  
Correlation Degree ◽  
Gear Fault ◽  
Iterative Filtering

In this paper, a fault identification method combining adaptive local iterative filtering and permutation entropy is proposed. The adaptive local iterative filtering can decompose the nonstationary signal into a finite number of stationary intrinsic mode functions. And the experiment gear fault data are decomposed into several intrinsic mode functions by this method. Then, using the permutation entropy to calculate each intrinsic mode function, it is found that the permutation entropy of the first several intrinsic mode functions can represent the characteristics of different fault types, and the permutation entropy of the intrinsic mode function corresponding to the rotating frequency signal of the gear system could be the boundary. Finally, the fault type of gear is identified by calculating the gray correlation degree of permutation entropy of essential mode function of vibration signal decomposition under different working conditions. The example analysis results show that the proposed method can be effectively applied to the fault diagnosis of the gear system.

scholarly journals Feature Extraction of Ship Radiated Noise Based on Permutation Entropy of the Intrinsic Mode Function with the Highest Energy

2016 ◽  
Author(s):  
Yu-Xing Li ◽  
Ya-An Li ◽  
Zhe Chen ◽  
Xiao Chen
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Characteristic Parameter ◽  
Intrinsic Mode Function ◽  
Permutation Entropy ◽  
Intrinsic Mode Functions ◽  
Characteristic Parameters ◽  
Radiated Noise ◽  
Mode Function ◽  
Mode Decomposition

In order to solve the problem of feature extraction of underwater acoustic signals in complex ocean environment, a new method for feature extraction from ship radiated noise is presented based on empirical mode decomposition theory and permutation entropy. It analyzes the separability for permutation entropies of the intrinsic mode functions of three types of ship radiated noise signals, and discusses the permutation entropy of the intrinsic mode function with the highest energy. In this study, ship radiated noise signals measured from three types of ships are decomposed into a set of intrinsic mode functions with empirical mode decomposition method. Then, the permutation entropies of all intrinsic mode functions are calculated with appropriate parameters. The permutation entropies are obviously different in the intrinsic mode functions with the highest energy, thus, the permutation entropy of the intrinsic mode function with the highest energy is regarded as a new characteristic parameter to extract the feature of ship radiated noise. After that, the characteristic parameters, namely, the energy difference between high and low frequency, permutation entropy, and multi-scale permutation entropy, are compared with the permutation entropy of the intrinsic mode function with the highest energy. It is discovered that the four characteristic parameters are at the same level for similar ships, however, there are differences in the parameters for different types of ships. The results demonstrate that the permutation entropy of the intrinsic mode function with the highest energy is better in separability as the characteristic parameter than the other three parameters by comparing their fluctuation ranges and the average values of the four characteristic parameters. Hence, the feature of ship radiated noise can be extracted efficiently with the method.

scholarly journals Gear Fault Diagnosis Based on Empirical Mode Decomposition and 1.5 Dimension Spectrum

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Jianhua Cai ◽  
Xiaoqin Li
Keyword(s):  
Fault Diagnosis ◽  
Power Spectrum ◽  
Correlation Coefficient ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Intrinsic Mode Function ◽  
Dimension Spectrum ◽  
Mode Function ◽  
Mode Decomposition ◽  
Gear Fault

Aiming at the nonlinear and nonstationary feature of mechanical fault vibration signal, a new fault diagnosis method, which is based on a combination of empirical mode decomposition (EMD) and 1.5 dimension spectrum, is proposed. Firstly, the vibration signal is decomposed by EMD and the correlation coefficient between each intrinsic mode function and original signal is calculated. Then these intrinsic mode function components, which have a big correlation coefficient, are selected to estimate its 1.5 dimension spectrum. And this method uses 1.5 dimension spectrum of each intrinsic mode function to reconstruct its power spectrum. And these power spectrums are summed to obtain the primary power spectrum of gear fault signal. Finally, the information feature of fault is extracted from the reconstructed 1.5 dimension spectrum. A model to reconstruct 1.5 dimension spectrum is established, and the principle and steps of the method are presented. Some simulated and measured gear fault signals have been processed to demonstrate the effectiveness of new method. The result shows that this method can greatly inhibit the interference of Gauss noise to raise the SNR and recognize the secondary phase coupling feature of the signal. The proposed method has a good real-time performance and provides an effective method to determine the early crack fault of gear root.

Approach to extracting gear fault feature based on dominant intrinsic mode function

2014 ◽  
Vol 228 (15) ◽  
pp. 2810-2819 ◽  
Author(s):  
Zhifeng Liu ◽  
Bing Luo ◽  
Wentong Yang ◽  
Ligang Cai ◽  
Jingying Zhang
Keyword(s):  
Empirical Mode Decomposition ◽  
Intrinsic Mode Function ◽  
Good Time ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Mode Function ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Rate Evaluation ◽  
Instantaneous Energy

Complex nonlinear and nonstationary signals can be adaptively analyzed by the Hilbert–Huang transform through empirical mode decomposition and the Hilbert transform to generate the instantaneous energy. The instantaneous energy was able to display the local characteristics of the signals and had good time–frequency analysis capability, it is therefore widely applied to the analysis of vibration signals in the field of gear fault diagnosis. However, only a few extracted intrinsic mode functions through empirical mode decomposition can reflect fault feature or closely related to the faults but others are irrelevant. Therefore, the fault feature of the instantaneous energy for all intrinsic mode functions was not obvious and the accuracy of diagnosis was low. Aimed at solving this problem, a fault leading rate evaluation algorithm was proposed that can select those intrinsic mode functions, which reflect fault features (it was called the dominant intrinsic mode function) from all intrinsic mode functions. In the paper, this algorithm was applied to gear fault feature extraction. By calculating the instantaneous energy of the dominant intrinsic mode function the method could accurately extract gear fault feature and improve the accuracy of diagnosis. Both simulated signals and experimental signals of a Klingelnberg bevel gear were analyzed to verify the effectiveness and correctness of the algorithm.

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 Fault Diagnosis for Rolling Element Bearings Based on Feature Space Reconstruction and Multiscale Permutation Entropy

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 519 ◽  
Author(s):  
Weibo Zhang ◽  
Jianzhong Zhou
Keyword(s):  
Diagnostic Algorithm ◽  
Feature Space ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Classification Performance ◽  
Fault Classification ◽  
Permutation Entropy ◽  
Support Vector ◽  
Intrinsic Mode Functions

Aimed at distinguishing different fault categories of severity of rolling bearings, a novel method based on feature space reconstruction and multiscale permutation entropy is proposed in the study. Firstly, the ensemble empirical mode decomposition algorithm (EEMD) was employed to adaptively decompose the vibration signal into multiple intrinsic mode functions (IMFs), and the representative IMFs which contained rich fault information were selected to reconstruct a feature vector space. Secondly, the multiscale permutation entropy (MPE) was used to calculate the complexity of reconstructed feature space. Finally, the value of multiscale permutation entropy was presented to a support vector machine for fault classification. The proposed diagnostic algorithm was applied to three groups of rolling bearing experiments. The experimental results indicate that the proposed method has better classification performance and robustness than other traditional methods.

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 Application of Variational Mode Decomposition and Permutation Entropy for Rolling Bearing Fault Diagnosis

2019 ◽  
Vol 24 (2) ◽  
pp. 303-311 ◽  
Author(s):  
Xiaoxia Zheng ◽  
Guowang Zhou ◽  
Dongdong Li ◽  
Haohan Ren
Keyword(s):  
Vibration Signal ◽  
Rolling Bearing ◽  
Permutation Entropy ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Bearing Vibration

Rolling bearings are the key components of rotating machinery. However, the incipient fault characteristics of a rolling bearing vibration signal are weak and difficult to extract. To solve this problem, this paper presents a novel rolling bearing vibration signal fault feature extraction and fault pattern recognition method based on variational mode decomposition (VMD), permutation entropy (PE) and support vector machines (SVM). In the proposed method, the bearing vibration signal is decomposed by VMD, and the intrinsic mode functions (IMFs) are obtained in different scales. Then, the PE values of each IMF are calculated to uncover the multi-scale intrinsic characteristics of the vibration signal. Finally, PE values of IMFs are fed into SVM to automatically accomplish the bearing condition identifications. The proposed method is evaluated by rolling bearing vibration signals. The results indicate that the proposed method is superior and can diagnose rolling bearing faults accurately.

Application of an improved dynamic time synchronous averaging method for fault diagnosis in conditions of speed fluctuation and no tachometer

2015 ◽  
Vol 230 (14) ◽  
pp. 2517-2531 ◽  
Author(s):  
Huifang Xiao ◽  
Xiaojun Zhou ◽  
Yimin Shao
Keyword(s):  
Dynamic Time Warping ◽  
Averaging Method ◽  
Vibration Signal ◽  
Intrinsic Mode Functions ◽  
Time Warping ◽  
Mode Function ◽  
Mode Decomposition ◽  
Speed Fluctuation ◽  
Dynamic Time ◽  
Phase Accumulation

Time synchronous averaging has been widely used for machinery fault diagnosis. However, it cannot reveal signal characteristics accurately in conditions of speed fluctuation and no tachometer due to the phase accumulation error. In this paper, an improved dynamic-time synchronous averaging method is proposed to extract the periodic feature signal from the fluctuated vibration signal for fault detection when no tachometer signal is available. In this method, empirical mode decomposition, dynamic time warping, and time synchronous averaging are performed on gear vibration signals to detect fault characteristic information. First, empirical mode decomposition is performed on the vibration signal and a series of intrinsic mode functions are produced. The sensitive intrinsic mode functions providing fault-related information are selected and reconstructed and the corresponding envelop signals are equal-space intercepted. Then, the phase accumulation error among the envelop signal segments is estimated by the dynamic time warping, which is further used to compensate the phase accumulation error between the intrinsic mode function segments of the reconstructed signal. Finally, the compensated intrinsic mode function segments are averaged to obtain the feature signal. Simulation analysis shows the advantages of the proposed method in extracting faulty feature signal from speed fluctuation signal without tachometer and identifying gear fault. Experiments with both normal and faulty gear were conducted and the vibration signals were captured. The proposed method is applied to identify the gear damage and the diagnosis results demonstrate its superiority than other methods.

Chatter Detection in Milling Process Based on Time-Frequency Analysis

2017 ◽  
Author(s):  
Meng-Kun Liu ◽  
Quang M. Tran ◽  
Yi-Wen Qui ◽  
Chun-Hui Chung
Keyword(s):  
Ensemble Empirical Mode Decomposition ◽  
Milling Process ◽  
Intrinsic Mode Function ◽  
Intrinsic Mode Functions ◽  
Machined Surface ◽  
Time Frequency ◽  
Mode Function ◽  
Mode Decomposition ◽  
The Rich ◽  
The Stability

Chatter identification is necessary in order to achieve stable machining conditions. However, the linear approximation in regenerative chatter vibration is problematic because of the rich nonlinear characteristics in machining. In this study, a novel method to detect chatter is proposed. Firstly, measured cutting force signals are decomposed into a set of intrinsic mode functions by using ensemble empirical mode decomposition. Hilbert transform is following to extract the instantaneous frequency. Fast Fourier transform is also utilized for each intrinsic mode function to determine the intrinsic mode function that contains rich chatter. Finally, the standard deviation and energy ratio in frequency domain of intrinsic mode functions are found as simply dimensionless chatter indicators. The effectively proposed approach is validated by analyzing the machined surface topography and also compared to the stability lobe diagram.

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