scholarly journals Identification of Cutting Chatter through Deep Learning and Classification

2020 ◽  
Vol 19 (4) ◽  
pp. 667-677
Author(s):  
H. N. Gao ◽  
D. H. Shen ◽  
L. Yu ◽  
W. C. Zhang
Keyword(s):  
Vibration Signal ◽  
Identification Accuracy ◽  
Support Vector ◽  
Identification Algorithm ◽  
Variational Mode Decomposition ◽  
Time Frequency ◽  
Chatter Identification ◽  
Mode Decomposition ◽  
Cutting Chatter ◽  
Cutting Vibration

The traditional analytical method has difficulty in accurately modelling cutting chatter. This paper constructs the vibration datasets of different chatter states and establishes a machine learning (ML) model for chatter identification, treating physical vibration signal as the input. Specifically, the cutting vibration signal was converted into the time-frequency spectrum, which was then classified by a self-designed deep residual convolutional neural network (DR-CNN). After that, the cutting vibration signal was broken down into chatter bands through variational mode decomposition (VMD). The information entropies of the chatter bands were calculated as cutting chatter features. Next, support vector machine (SVM) was introduced to classify the extracted features and used to create an online cutting chatter identification algorithm. The proposed method achieved a much higher mean identification accuracy (92.57 %) than the traditional identification method.

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.

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

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.

scholarly journals A Novel Method for Mechanical Fault Diagnosis Based on Variational Mode Decomposition and Multikernel Support Vector Machine

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Zhongliang Lv ◽  
Baoping Tang ◽  
Yi Zhou ◽  
Chuande Zhou
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Optimal Parameter ◽  
Locally Linear Embedding ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Time Frequency ◽  
Feature Sets ◽  
Mode Decomposition ◽  
Mechanical Faults

A novel fault diagnosis method based on variational mode decomposition (VMD) and multikernel support vector machine (MKSVM) optimized by Immune Genetic Algorithm (IGA) is proposed to accurately and adaptively diagnose mechanical faults. First, mechanical fault vibration signals are decomposed into multiple Intrinsic Mode Functions (IMFs) by VMD. Then the features in time-frequency domain are extracted from IMFs to construct the feature sets of mixed domain. Next, Semisupervised Locally Linear Embedding (SS-LLE) is adopted for fusion and dimension reduction. The feature sets with reduced dimension are inputted to the IGA optimized MKSVM for failure mode identification. Theoretical analysis demonstrates that MKSVM can approximate any multivariable function. The global optimal parameter vector of MKSVM can be rapidly identified by IGA parameter optimization. The experiments of mechanical faults show that, compared to traditional fault diagnosis models, the proposed method significantly increases the diagnosis accuracy of mechanical faults and enhances the generalization of its application.

scholarly journals Arc Fault Detection Algorithm Based on Variational Mode Decomposition and Improved Multi-Scale Fuzzy Entropy

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4137
Author(s):  
Lina Wang ◽  
Hongcheng Qiu ◽  
Pu Yang ◽  
Longhua Mu
Keyword(s):  
Fault Diagnosis ◽  
Detection Algorithm ◽  
Fuzzy Entropy ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Experiment Platform ◽  
Multi Scale ◽  
Mode Decomposition

Arc fault diagnosis is necessary for the safety and efficiency of PV stations. This study proposed an arc fault diagnosis algorithm formed by combining variational mode decomposition (VMD), improved multi-scale fuzzy entropy (IMFE), and support vector machine (SVM).. This method first uses VMD to decompose the current into intrinsic mode functions (IMFs) in the time-frequency domain, then calculates the IMFE according to the IMFs associated with the arc fault. Finally, it uses SVM to detect arc faults according to IMFEs. Arc fault data gathered from a PV arc generation experiment platform are used to validate the proposed method. The results indicated the proposed method can classify arc fault data and normal data effectively.

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

Entropy ◽  
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)).

scholarly journals Casing Vibration Fault Diagnosis Based on Variational Mode Decomposition, Local Linear Embedding, and Support Vector Machine

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Yizhou Yang ◽  
Dongxiang Jiang
Keyword(s):  
Support Vector Machine ◽  
Vibration Signal ◽  
Support Vector ◽  
Training Procedure ◽  
Variational Mode Decomposition ◽  
Local Linear Embedding ◽  
Local Linear ◽  
Mode Decomposition ◽  
Linear Embedding ◽  
Low Dimensional

To diagnose mechanical faults of rotor-bearing-casing system by analyzing its casing vibration signal, this paper proposes a training procedure of a fault classifier based on variational mode decomposition (VMD), local linear embedding (LLE), and support vector machine (SVM). VMD is used first to decompose the casing signal into several modes, which are subsignals usually modulated by fault frequencies. Vibrational features are extracted from both VMD subsignals and the original one. LLE is employed here to reduce the dimensionality of these extracted features and make the samples more separable. Then low-dimensional data sets are used to train the multiclass SVM whose accuracy is tested by classifying the test samples. When the parameters of LLE and SVM are well optimized, this proposed method performs well on experimental data, showing its capacity of diagnosing casing vibration faults.

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

Electronics ◽  
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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