scholarly journals Rolling Bearing Fault Diagnosis Based on ELCD Permutation Entropy and RVM

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Jiang Xingmeng ◽  
Wu Li ◽  
Pan Liwu ◽  
Ge Mingtao ◽  
Hu Daidi
Keyword(s):  
Vibration Signal ◽  
Rolling Bearing ◽  
Relevance Vector Machine ◽  
Permutation Entropy ◽  
Local Characteristic ◽  
Characteristic Scale ◽  
Bearing Fault Diagnosis ◽  
Gear Fault ◽  
Intrinsic Scale ◽  
Better Than

Aiming at the nonstationary characteristic of a gear fault vibration signal, a recognition method based on permutation entropy of ensemble local characteristic-scale decomposition (ELCD) and relevance vector machine (RVM) is proposed. First, the vibration signal was decomposed by ELCD; then a series of intrinsic scale components (ISCs) were obtained. Second, according to the kurtosis of ISCs, principal ISCs were selected and then the permutation entropy of principal ISCs was calculated and they were combined into a feature vector. Finally, the feature vectors were input in RVM classifier to train and test and identify the type of rolling bearing faults. Experimental results show that this method can effectively diagnose four kinds of working condition, and the effect is better than local characteristic-scale decomposition (LCD) method.

scholarly journals A Novel Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Mingliang Liang ◽  
Dongmin Su ◽  
Daidi Hu ◽  
Mingtao Ge
Keyword(s):  
Extreme Learning Machine ◽  
Rolling Bearing ◽  
Local Characteristic ◽  
Support Vector ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Evaluation Approach ◽  
Diagnosis Method ◽  
Learning Machine ◽  
Intrinsic Scale

A rolling bearing fault diagnosis method based on ensemble local characteristic-scale decomposition (ELCD) and extreme learning machine (ELM) is proposed. Vibration signals were decomposed using ELCD, and numerous intrinsic scale components (ISCs) were obtained. Next, time-domain index, energy, and relative entropy of intrinsic scale components were calculated. According to the distance-based evaluation approach, sensitivity features can be extracted. Finally, sensitivity features were input to extreme learning machine to identify rolling bearing fault types. Experimental results show that the proposed method achieved better performance than support vector machine (SVM) and backpropagation (BP) neural network methods.

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 Balanced Multi-Wavelet in the Fault Diagnosis of Rolling Bearing

2012 ◽  
Vol 226-228 ◽  
pp. 210-215
Author(s):  
Sui Zheng Zhang ◽  
Jian Yu Zhang ◽  
Yang Yang
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Vanishing Moments ◽  
One Dimensional ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Band Index ◽  
Different Characteristics ◽  
Better Than

Multi-wavelet has many excellent properties that single wavelet cannot satisfy simultaneously, such as symmetry, orthogonality, compact support and high vanishing moments etc. It contains several scaling functions and wavelet functions, which can make it match different characteristics of analyzed signal. Therefore, it is always used in bearing fault diagnosis. However, multi-wavelet is multi-dimensional and vibration signal is one-dimensional, so the 1-D vibration signal should be preprocessed before being decomposed with multi-wavelet. It means that the initial data need to be converted to r-dimensional data, and then is input to a tower algorithm. If preprocessing is done, multi-wavelet properties will be destroyed. Due to balanced multi-wavelet has unique properties, the preprocessing can be omitted. In this paper, a balanced multi-wavelet called CL4BAL is designed through balancing original CL4 multi-wavelet and is applied in the vibration signal processing. Comparing the frequency band index after decomposition and reconstruction of CL4BAL and CL4 multi-wavelet, it can be proved that CL4BAL is much better than that of CL4 multi-wavelet in bearing fault diagnosis.

Rolling bearing fault diagnosis of PSO–LSSVM based on CEEMD entropy fusion

2020 ◽  
Vol 44 (3) ◽  
pp. 405-418
Author(s):  
Shuzhi Gao ◽  
Tianchi Li ◽  
Yimin Zhang
Keyword(s):  
Fault Diagnosis ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Fault Classification ◽  
Kernel Principal Component Analysis ◽  
Permutation Entropy ◽  
Support Vector ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Taking aim at the nonstationary nonlinearity of the rolling bearing vibration signal, a rolling bearing fault diagnosis method based on the entropy fusion feature of complementary ensemble empirical mode decomposition (CEEMD) is proposed in combination with information fusion theory. First, CEEMD of the vibration signal of the rolling bearing is performed. Then the signal is decomposed into the sum of several intrinsic mode functions (IMFs), and the singular entropy, energy entropy, and permutation entropy are obtained for the IMFs with fault features. Second, the feature extraction method of entropy fusion is proposed, and the three entropy data obtained are input into kernel principal component analysis (KPCA) for feature fusion and dimensionality reduction to obtain complementary features. Finally, the extracted features are imported into the particle swarm optimization (PSO) algorithm to optimize the least-squares support vector machine (LSSVM) for fault classification. Through experimental verification, the proposed method can be used for roller bearing fault diagnosis.

scholarly journals Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Jinde Zheng ◽  
Junsheng Cheng ◽  
Yu Yang
Keyword(s):  
Experimental Data ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Permutation Entropy ◽  
Support Vector ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Laplacian Score ◽  
Definition Of

A new rolling bearing fault diagnosis approach based on multiscale permutation entropy (MPE), Laplacian score (LS), and support vector machines (SVMs) is proposed in this paper. Permutation entropy (PE) was recently proposed and defined to measure the randomicity and detect dynamical changes of time series. However, for the complexity of mechanical systems, the randomicity and dynamic changes of the vibration signal will exist in different scales. Thus, the definition of MPE is introduced and employed to extract the nonlinear fault characteristics from the bearing vibration signal in different scales. Besides, the SVM is utilized to accomplish the fault feature classification to fulfill diagnostic procedure automatically. Meanwhile, in order to avoid a high dimension of features, the Laplacian score (LS) is used to refine the feature vector by ranking the features according to their importance and correlations with the main fault information. Finally, the rolling bearing fault diagnosis method based on MPE, LS, and SVM is proposed and applied to the experimental data. The experimental data analysis results indicate that the proposed method could identify the fault categories effectively.

scholarly journals A Rolling Bearing Fault Diagnosis Method Based on EMD and Quantile Permutation Entropy

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Qiang-qiang Chen ◽  
Shao-wu Dai ◽  
Hong-de Dai
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Permutation Entropy ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Rolling Bearings ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Main Fault

The vibration signals resulting from rolling bearings are nonlinear and nonstationary, and an approach for the fault diagnosis of rolling bearings using the quantile permutation entropy and EMD (empirical mode decomposition) is proposed. Firstly, the EMD is used to decompose the rolling bearings vibration signal, and several IMFs (intrinsic mode functions) spanning different scales are obtained. Secondly, aiming at the shortcomings of the permutation entropy algorithm, a new permutation entropy algorithm based on sample quantile is proposed, and the quantile permutation entropy of the first few IMFs, which contain the main fault information, is calculated. The quantile permutation entropies are accordingly seen as the characteristic vector and then input to the particle swarm optimization and support vector machine. Finally, the proposed method is applied to the experimental data. The analysis results show that the proposed approach can effectively achieve fault diagnosis of rolling bearings.

scholarly journals Rolling Bearing Fault Diagnosis Based on Component Screening Vector Local Characteristic-Scale Decomposition

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Tengfei Guan ◽  
Shijun Liu ◽  
Wenbo Xu ◽  
Zhisheng Li ◽  
Hongtao Huang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Single Channel ◽  
Simulation Analysis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Local Characteristic ◽  
Characteristic Scale ◽  
Dual Channel ◽  
Component Selection ◽  
Envelope Spectrum

The fault vibration signal of a bearing has nonstationary and nonlinear characteristics and can be regarded as the combination of multiple amplitude- and frequency-modulation components. The envelope of a single component contains the fault characteristics of a bearing. Local characteristic-scale decomposition (LCD) can decompose the vibration signal into a series of multiple intrinsic scale components. Some components can clearly reflect the running state of a bearing, and fault diagnosis is conducted according to the envelope spectrum. However, the conventional LCD takes a single-channel signal as the research object, which cannot fully reflect the characteristic information of the rotor, and the analysis results based on different channel signals of the same section will be inconsistent. To solve this problem, based on full vector spectrum technology, the homologous dual-channel information is fused. A vector LCD method based on cross-correlation coefficient component selection is given, and a simulation analysis is completed. The effectiveness of the proposed method is verified by simulated signals and experimental signals of a bearing, which provides a method for bearing feature extraction and fault diagnosis.

Feature extraction for rolling bearing fault diagnosis by electrostatic monitoring sensors

2014 ◽  
Vol 229 (10) ◽  
pp. 1887-1903 ◽  
Author(s):  
Ying Zhang ◽  
Hongfu Zuo ◽  
Fang Bai
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Hankel Matrix ◽  
Rolling Bearing ◽  
Difference Spectrum ◽  
Permutation Entropy ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Quantitative Indicator ◽  
Bearing Fault Diagnosis

There are mainly two problems with the current feature extraction methods used in the electrostatic monitoring of rolling bearings, which affect their abilities to identify early faults: (1) since noises are mixed in the electrostatic signals, it is difficult to extract weak early fault features; (2) traditional time and frequency domain features have limited ability to provide a quantitative indicator of degradation state. With regard to these two problems, a new feature extraction method for rolling bearing fault diagnosis by electrostatic monitoring sensors is proposed in this paper. First, the spectrum interpolation is adopted to suppress the power-frequency interference in the electrostatic signal. Then the resultant signal is used to construct Hankel matrix, the number of useful components is automatically selected based on the difference spectrum of singular values, after that the signal is reconstructed to remove background noises and random pulses. Finally, the permutation entropy of the denoised signal is calculated and smoothed using the exponential weighted moving average method, which is used to be a quantitative indicator of bearing performance state. The simulation and experimental results show that the proposed method can effectively remove noises and significantly bring forward the time when early faults are detected.

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