Fault Identification of Rolling Bearing Using Variational Mode Decomposition Multiscale Permutation Entropy and Adaptive GG Clustering

The nonlinear and nonstationary characteristics of vibration signal in mechanical equipment make fault identification difficult. To tackle this problem, this paper proposes a novel fault identification method based on improved variational mode decomposition (IVMD), multiscale permutation entropy (MPE), and adaptive GG clustering. Firstly, the original vibration signal is decomposed into a set of mode components adaptively by IVMD, and the mode components that are highly correlated with the original signal are selected to reconstruct the original signal. After that, the MPE values of the reconstructed signal are calculated as feature vectors which can differentiate machinery conditions. Finally, low-dimensional sensitive features obtained by principal component analysis (PCA) are fed into the adaptive GG clustering algorithm to perform fault identification. In this method, the residual energy ratio is used to find the optimal parameter K of the VMD and the PBMF function is incorporated into the GG to determine the number of clusters adaptively. Two bearing datasets are used to validate the performance of the proposed method. The results show that the proposed method can effectively identify different fault types.

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Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM

Entropy ◽

10.3390/e23060762 ◽

2021 ◽

Vol 23 (6) ◽

pp. 762

Author(s):

Maoyou Ye ◽

Xiaoan Yan ◽

Minping Jia

Keyword(s):

Vibration Signal ◽

Rolling Bearing ◽

Classification Model ◽

Permutation Entropy ◽

Support Vector ◽

Detection Accuracy ◽

Variational Mode Decomposition ◽

Mode Decomposition ◽

Reconstructed Signal ◽

Bearing Vibration

The goal of the paper is to present a solution to improve the fault detection accuracy of rolling bearings. The method is based on variational mode decomposition (VMD), multiscale permutation entropy (MPE) and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, the original bearing vibration signal is decomposed into several intrinsic mode functions (IMF) by using the VMD method, and the feature energy ratio (FER) criterion is introduced to reconstruct the bearing vibration signal. Secondly, the multiscale permutation entropy of the reconstructed signal is calculated to construct multidimensional feature vectors. Finally, the constructed multidimensional feature vector is fed into the PSO-SVM classification model for automatic identification of different fault patterns of the rolling bearing. Two experimental cases are adopted to validate the effectiveness of the proposed method. Experimental results show that the proposed method can achieve a higher identification accuracy compared with some similar available methods (e.g., variational mode decomposition-based multiscale sample entropy (VMD-MSE), variational mode decomposition-based multiscale fuzzy entropy (VMD-MFE), empirical mode decomposition-based multiscale permutation entropy (EMD-MPE) and wavelet transform-based multiscale permutation entropy (WT-MPE)).

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Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network

Electronics ◽

10.3390/electronics10111248 ◽

2021 ◽

Vol 10 (11) ◽

pp. 1248

Author(s):

Rafia Nishat Toma ◽

Cheol-Hong Kim ◽

Jong-Myon Kim

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Empirical Mode Decomposition ◽

Vibration Signal ◽

Ensemble Empirical Mode Decomposition ◽

Fault Classification ◽

Original Signal ◽

Time Frequency ◽

Mode Decomposition ◽

Reconstructed Signal

Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings.

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Research on Fault Diagnosis of Gearbox with Improved Variational Mode Decomposition

Sensors ◽

10.3390/s18103510 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3510 ◽

Cited By ~ 24

Author(s):

Zhijian Wang ◽

Junyuan Wang ◽

Wenhua Du

Keyword(s):

Fault Diagnosis ◽

Signal To Noise Ratio ◽

Optimal Number ◽

Original Signal ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Multiple Fault ◽

Mode Decomposition ◽

Fault Features ◽

Reconstructed Signal

Variational Mode Decomposition (VMD) can decompose signals into multiple intrinsic mode functions (IMFs). In recent years, VMD has been widely used in fault diagnosis. However, it requires a preset number of decomposition layers K and is sensitive to background noise. Therefore, in order to determine K adaptively, Permutation Entroy Optimization (PEO) is proposed in this paper. This algorithm can adaptively determine the optimal number of decomposition layers K according to the characteristics of the signal to be decomposed. At the same time, in order to solve the sensitivity of VMD to noise, this paper proposes a Modified VMD (MVMD) based on the idea of Noise Aided Data Analysis (NADA). The algorithm first adds the positive and negative white noise to the original signal, and then uses the VMD to decompose it. After repeated cycles, the noise in the original signal will be offset to each other. Then each layer of IMF is integrated with each layer, and the signal is reconstructed according to the results of the integrated mean. MVMD is used for the final decomposition of the reconstructed signal. The algorithm is used to deal with the simulation signals and measured signals of gearbox with multiple fault characteristics. Compared with the decomposition results of EEMD and VMD, it shows that the algorithm can not only improve the signal to noise ratio (SNR) of the signal effectively, but can also extract the multiple fault features of the gear box in the strong noise environment. The effectiveness of this method is verified.

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

10.20855/ijav.2019.24.21325 ◽

2019 ◽

Vol 24 (2) ◽

pp. 303-311 ◽

Cited By ~ 5

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.

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An optimized variational mode decomposition method and its application in vibration signal analysis of bearings

Structural Health Monitoring ◽

10.1177/14759217211057444 ◽

2021 ◽

pp. 147592172110574

Author(s):

Jun Gu ◽

Yuxing Peng ◽

Hao Lu ◽

Xiangdong Chang ◽

Shuang Cao ◽

...

Keyword(s):

Mean Squared Error ◽

Input Parameter ◽

Vibration Signal ◽

Rolling Bearing ◽

Permutation Entropy ◽

Support Vector ◽

Svm Classifier ◽

Variational Mode Decomposition ◽

Mode Decomposition ◽

Penalty Factor

The performance of the rolling bearing of a spindle device is directly related to the safety and reliability of the operation of a mine hoist. To extract bearing vibration signal features effectively for fault diagnosis, a feature extraction method based on the parameter optimization of a variational mode decomposition (VMD) method and permutation entropy (PE) is proposed. In addition, a support vector machine (SVM) classifier is used to identify bearing fault types. An analogue signal is used to test the effect of noise and sampling frequency on VMD performance. Focused on the problem of the VMD method needing to determine the number of mode components K and a penalty factor α during the signal decomposition process, a genetic algorithm is used to optimize the parameter combination [K,α] with the minimum sample entropy as the indicator. By using mean squared error (MSE) and correlation coefficient, an evaluation indicator is constructed to determine the decomposition effects of the optimized VMD, centre frequency, empirical mode decomposition (EMD) and ensemble EMD (EEMD) methods. The normalized PE of the five mode components is used as an eigenvalue, which is used as the input parameter of the SVM. Two different experimental datasets are used to verify the effectiveness of the proposed method. The results show that the proposed method has better diagnostic accuracy than EMD, EEMD and a BP neural network in the case of limited samples and unknown sample inputs. It can provide a good reference for the diagnosis of a rolling bearing and has practical application value.

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The Application of EEMD to Fault Diagnosis of Rolling Bearing

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.765-767.2065 ◽

2013 ◽

Vol 765-767 ◽

pp. 2065-2069 ◽

Cited By ~ 2

Author(s):

Zhong Liang Lv ◽

Pei Wen An ◽

Bao Ping Tang ◽

Li Hui Zhang

Keyword(s):

Data Analysis ◽

Fault Diagnosis ◽

Vibration Signal ◽

Rolling Bearing ◽

Fault Identification ◽

Original Signal ◽

Failure Data ◽

Mode Decomposition ◽

Bearing Vibration ◽

Eemd Method

The EEDM(Ensemle Empirical Mode Decomposition) combined with correlation coefficient was proposed for identify the fault of rolling bearing. First, the fault of rolling bearing vibration signal will be decomposed into several IMF components, in view of the illusive component may appear in EEDM components, respectively calculate each IMF components and the correlation between the original signal, then carry out spectrum analysis to each IMF components and pick up fault feature. Through the experimental failure data analysis of rolling bearing inner ring, the EEMD method is good for fault identification of rolling bearing.

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Fault Diagnosis of Spindle Device in Hoist Using Variational Mode Decomposition and Statistical Features

Shock and Vibration ◽

10.1155/2020/8849513 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Jun Gu ◽

Yuxing Peng ◽

Hao Lu ◽

Shuang Cao ◽

Bobo Cao

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Back Propagation ◽

Rolling Bearing ◽

Statistical Characteristics ◽

Permutation Entropy ◽

Mechanical Equipment ◽

Variational Mode Decomposition ◽

Mode Decomposition ◽

Propagation Network

By analyzing nonlinear and nonstationary vibration signals from the spindle device of the mine hoist, it is a challenge to overcome the difficulty of fault feature extraction and accurately identify the fault of rotor-bearing system. In response to this problem, this paper proposes a new approach based on variational mode decomposition (VMD), SVM, and statistical characteristics such as variance contribution rate (VCR), energy entropy (EE), and permutation entropy (PE). Comparisons have gone to evaluate the performance of rolling bearing defect by using EMD (Empirical Mode Decomposition), MEEMD (Modified Ensemble EMD), BP (Back Propagation) network, single or multiple statistical characteristics, and different motor loads. The experiment was carried out on the mechanical failure simulator of the main shaft device of the hoist, which verified the reliability and effectiveness of the method. The results show that the diagnosis method is suitable for feature extraction of bearing fault signals, with the highest diagnosis accuracy. It can provide a good practical reference for the fault diagnosis of mechanical equipment of the hoist spindle device and has certain practical value.

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Rolling bearing degradation condition clustering using multidimensional degradation feature and Gath–Geva fuzzy clustering algorithm

Journal of Vibration and Control ◽

10.1177/1077546320954958 ◽

2020 ◽

pp. 107754632095495

Author(s):

Bing Wang ◽

Xiong Hu ◽

Tao X Mei ◽

Sun D Jian ◽

Wang Wei

Keyword(s):

Fuzzy Clustering ◽

Clustering Algorithm ◽

Chaotic Map ◽

Vibration Signal ◽

Degradation Process ◽

Rolling Bearing ◽

Performance Degradation ◽

Time Parameter ◽

Mechanical Equipment ◽

Time Dimension

In allusion to the issue of rolling bearing degradation feature extraction and degradation condition clustering, a logistic chaotic map is introduced to analyze the advantages of C0 complexity and a technique based on a multidimensional degradation feature and Gath–Geva fuzzy clustering algorithmic is proposed. The multidimensional degradation feature includes C0 complexity, root mean square, and curved time parameter which is more in line with the performance degradation process. Gath–Geva fuzzy clustering is introduced to divide different conditions during the degradation process. A rolling bearing lifetime vibration signal from intelligent maintenance system bearing test center was introduced for instance analysis. The results show that C0 complexity is able to describe the degradation process and has advantages in sensitivity and calculation speed. The introduced degradation indicator curved time parameter can reflect the agglomeration character of the degradation condition at time dimension, which is more in line with the performance degradation pattern of mechanical equipment. The Gath–Geva fuzzy clustering algorithmic is able to cluster degradation condition of mechanical equipment such as bearings accurately.

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Research on gearbox impact feature extraction method based on improved ESMD

Insight - Non-Destructive Testing and Condition Monitoring ◽

10.1784/insi.2022.64.1.20 ◽

2022 ◽

Vol 64 (1) ◽

pp. 20-27

Author(s):

Fengfeng Bie ◽

Sheng Gu ◽

Yue Guo ◽

Gang Yang ◽

Jian Peng

Keyword(s):

Feature Vector ◽

Vibration Signal ◽

Permutation Entropy ◽

Significant Feature ◽

Support Vector ◽

Feature Extraction Method ◽

Mode Decomposition ◽

Envelope Spectrum ◽

Impact Characteristics ◽

The Impact

A gearbox vibration signal contains non-linear impact characteristics and the significant feature information tends to be overwhelmed by other interference components, which make it difficult to extract the typical fault features fully and effectively. Aiming at the key issue of how to effectively extract the impact characteristics, a fault diagnosis method based on improved extreme symmetric mode decomposition (ESMD) and a support vector machine (SVM) is proposed in this paper. The vibration signal is adaptively decomposed into multiple intrinsic mode function (IMF) components by the improved ESMD and then a certain number of components are selected with the maximum kurtosis-envelope spectrum index. The singular spectral entropy, energy entropy and permutation entropy of each component are applied to construct the feature vector set, in which the dimensionality of the set is reduced with the distance separability criterion. Finally, the dimension-reduced feature vector set is input into the SVM for pattern recognition. Dynamic simulation and experimental gearbox research show that the improved ESMD method can extract and identify gearbox fault information effectively.

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A Novel Approach for Acoustic Signal Processing of a Drum Shearer Based on Improved Variational Mode Decomposition and Cluster Analysis

Sensors ◽

10.3390/s20102949 ◽

2020 ◽

Vol 20 (10) ◽

pp. 2949

Author(s):

Changpeng Li ◽

Tianhao Peng ◽

Yanmin Zhu

Keyword(s):

Signal Processing ◽

Acoustic Signal ◽

Clustering Algorithm ◽

Pso Algorithm ◽

Variational Mode Decomposition ◽

Novel Approach ◽

Mode Decomposition ◽

Drum Shearer ◽

Best Parameter ◽

And Cluster Analysis

During operation, the acoustic signal of the drum shearer contains a wealth of information. The monitoring or diagnosis system based on acoustic signal has obvious advantages. However, the signal is challenging to extract and recognize. Therefore, this paper proposes an approach for acoustic signal processing of a shearer based on the parameter optimized variational mode decomposition (VMD) method and a clustering algorithm. First, the particle swarm optimization (PSO) algorithm searched for the best parameter combination of the VMD. According to the results, the approach determined the number of modes and penalty parameters for VMD. Then the improved VMD algorithm decomposed the acoustic signal. It selected the ideal component through the minimum envelope entropy. The PSO was designed to optimize the clustering analysis, and the minimum envelope entropy of the acoustic signal was regarded as the feature for classification. We then use a shearer simulation platform to collect the acoustic signal and use the approach proposed in this paper to process and classify the signal. The experimental results show that the approach proposed can effectively extract the features of the acoustic signal of the shearer. The recognition accuracy of the acoustic signal was high, which has practical application value.

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