scholarly journals Fault Classification of Rolling Bearing Based on Time-Frequency Generalized Dimension of Vibration Signal and ANFIS

2014 ◽  
Vol 8 (1) ◽  
pp. 861-864 ◽  
Author(s):  
Fang Li
Keyword(s):  
Vibration Signal ◽  
Rolling Bearing ◽  
Fault Classification ◽  
Time Frequency ◽  
Generalized Dimension

scholarly journals Fine-Grained Fault Diagnosis Method of Rolling Bearing Combining Multisynchrosqueezing Transform and Sparse Feature Coding Based on Dictionary Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Guodong Sun ◽  
Yuan Gao ◽  
Kai Lin ◽  
Ye Hu
Keyword(s):  
Fault Diagnosis ◽  
Dictionary Learning ◽  
Nonnegative Matrix ◽  
Rolling Bearing ◽  
Fault Classification ◽  
Support Vector ◽  
Time Frequency ◽  
Fine Grained ◽  
Diagnosis Method

To accurately diagnose fine-grained fault of rolling bearing, this paper proposed a new fault diagnosis method combining multisynchrosqueezing transform (MSST) and sparse feature coding based on dictionary learning (SFC-DL). Firstly, the high-resolution time-frequency images of raw vibration signals, including different kinds of fine-grained faults of rolling bearing, were constructed by MSST. Then, the basis dictionary was trained through nonnegative matrix factorization with sparseness constraints (NMFSC), and the trained basis dictionary was employed to extract features from time-frequency matrixes by using nonnegative linear equations. Finally, a linear support vector machine (LSVM) was trained with features of training samples, and the trained LSVM was employed to diagnosis the fault classification of test samples. Compared with state-of-the-art fault diagnosis methods, the proposed method, which was tested on the bearing dataset from Case Western Reserve University (CWRU), achieved the fine-grained classification of 10 mixed fault states. Meanwhile, the proposed method was applied on the dataset from the Machinery Failure Prevention Technology (MFPT) Society and realized the classification of 3 fault states under different working conditions. These results indicate that the proposed method has great robustness and could better meet the needs of practical engineering.

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.

Enhanced deep residual network with multilevel correlation information for fault diagnosis of rotating machinery

2020 ◽  
pp. 107754632094971 ◽  
Author(s):  
Shoucong Xiong ◽  
Shuai He ◽  
Jianping Xuan ◽  
Qi Xia ◽  
Tielin Shi
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Learning Algorithms ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Classification Performance ◽  
Residual Network ◽  
Time Frequency ◽  
Correlation Information

Modern machinery becomes more precious with the advance of science, and fault diagnosis is vital for avoiding economical losses or casualties. Among massive diagnosis methods, deep learning algorithms stand out to open an era of intelligent fault diagnosis. Deep residual networks are the state-of-the-art deep learning models which can continuously improve performance by deepening the network structures. However, in vibration-based fault diagnosis, the transient property instability of vibration signal usually calls for time–frequency analysis methods, and the characters of time–frequency matrices are distinct from standard images, which brings some natural limitations for the diagnosis performance of deep learning algorithms. To handle this issue, an enhanced deep residual network named the multilevel correlation stack-deep residual network is proposed in this article. Wavelet packet transform is used to preprocess the sensor signal, and then the proposed multilevel correlation stack-deep residual network uses kernels with different shapes to fully dig various kinds of useful information from any local regions of the processed input. Experiments on two rolling bearing datasets are carried out. Test results show that the multilevel correlation stack-deep residual network exhibits a more satisfactory classification performance than original deep residual networks and other similar methods, revealing significant potentials for realistic fault diagnosis applications.

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.

Rolling bearing fault detection approach based on improved dispersion entropy and AFSA optimized SVM

2020 ◽  
pp. 002072092094058
Author(s):  
Wuqiang Liu ◽  
Jinxing Shen ◽  
Xiaoqiang Yang
Keyword(s):  
Fault Detection ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Classification Model ◽  
Coarse Grained ◽  
Fault Classification ◽  
Support Vector ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm

The support vector machine (SVM) does not have a fixed parameter selection method and the manual selection of parameters is difficult to determine the validity, which affects the accuracy of recognition. simultaneously, The existing coarse-grained approach cannot effectively analyze the high-frequency components of time series. In view of the shortcomings of the above method, we put forward a new technique of rolling bearings for fault detection, which combines wavelet packet dispersion entropy (WPDE) and artificial fish swarm algorithm (AFSA) optimize support vector machines (AFSA-SVM). First of all, wavelet packet is devoted to decompose the original vibration signal into components of different frequency bands. Secondly, the dispersion entropy (DE) are calculated for each of the obtained frequency band components to acquire more comprehensive and complete fault information. Afterward, Input feature samples into the SVM model for training, and AFSA is used to optimize the parameters of SVM to obtain the optimal value so as to establish the best classification model. Finally, the prepared test set is input into AFSA-SVM for fault classification. The achievement of bearing detection experiments show that this approach can accurately and quickly identify fault types.

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.

Blind Source Separation for Under-Determined Mixtures Based on Time-Frequency Analysis

2016 ◽  
Vol 693 ◽  
pp. 1350-1356 ◽  
Author(s):  
Hong Kun Li ◽  
Hong Yi Liu ◽  
Chang Bo He
Keyword(s):  
Blind Source Separation ◽  
Frequency Analysis ◽  
Source Separation ◽  
Vibration Signal ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Diagnosis And Classification ◽  
Source Signals

Blind source separation (BSS) is an effective method for the fault diagnosis and classification of mixture signals with multiple vibration sources. The traditional BSS algorithm is applicable to the number of observed signals is no less to the source signals. But BSS performance is limit for the under-determined condition that the number of observed signals is less than source signals. In this research, we provide an under-determined BSS method based on the advantage of time-frequency analysis and empirical mode decomposition (EMD). It is suitable for weak feature extraction and pattern recognition. Firstly, vibration signal is decomposed by using EMD. The number of source signals are estimated and the optimal observed signals are selected according to the EMD. Then, the vibration signal and the optimal observed signals are used to construct the multi-channel observed signals. In the end, BSS based on time-frequency analysis are used to the constructed signals. Gearbox signals are used to verify the effectiveness of this method.

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