Research on bearing fault feature extraction based on singular value decomposition and optimized frequency band entropy

Li, Hua; Wu, Xing; Chen, Qing

doi:10.1016/j.ymssp.2018.08.056

Cited by 116 publications

(55 citation statements)

References 26 publications

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“…The operation ×n denotes the n-mode product [13]. G is the core tensor which has a special block-diagonal structure and the elements indicate the level of local interactions between the loading matrices and the corresponding latent vectors.T is the latent variable matrix and r t is the rth column of T. (1) r P and (2) r P can be gotten by singular value decomposition (SVD) [28][29][30].…”

Section: Proposed Methodsmentioning

confidence: 99%

Short-Term Load Forecasting with Tensor Partial Least Squares-Neural Network

Feng

Meng

2019

Energies

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Short-term load forecasting is very important for power systems. The load is related to many factors which compose tensors. However, tensors cannot be input directly into most traditional forecasting models. This paper proposes a tensor partial least squares-neural network model (TPN) to forecast the power load. The model contains a tensor decomposition outer model and a nonlinear inner model. The outer model extracts common latent variables of tensor input and vector output and makes the residuals less than the threshold by iteration. The inner model determines the relationship between the latent variable matrix and the output by using a neural network. This model structure can preserve the information of tensors and the nonlinear features of the system. Three classical models, partial least squares (PLS), least squares support vector machine (LSSVM) and neural network (NN), are selected to compare the forecasting results. The results show that the proposed model is efficient for short-term load and daily load peak forecasting. Compared to PLS, LSSVM and NN, the TPN has the best forecasting accuracy.

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Section: Proposed Methodsmentioning

confidence: 99%

Short-Term Load Forecasting with Tensor Partial Least Squares-Neural Network

Feng

Meng

2019

Energies

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“…The bearing used for testing was ER16K ball bearing. In previous studies [6], [15] and [49] a plethora of features that could be extracted from the vibrational data were studied, specifically from the time domain, frequency domain or the timefrequency domain using various signal-processing tools such as the Fourier transform, Hilbert transform, Wavelet transform, etc. The feature-extraction part can greatly enhance the results of the classification and there is a lot of studies emerging on this topic [50].…”

Section: Feature Extraction and Construction Of Classification Datasetsmentioning

confidence: 99%

“…However, since this paper represents the application of a classification method and its variants to one of the most dominant problems in the field of bearing and rotating-machinery fault detection we will simplify the feature-extraction process to only the statistical features of the vibrational signals in the time and frequency domains. This resulted in thirteen different most popular statistical features, judging by the literature [6], [15] and [49]. Features are given in Table 3, where x i is the i th amplitude of the acceleration signal, N is the number of samples in the signal, μ x is the mean value of the signal, σ x is the standard deviation of the signal, f i is the corresponding i th frequency amplitude.…”

Section: Feature Extraction and Construction Of Classification Datasetsmentioning

confidence: 99%

Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

Panić

Klemenc

Nagode

2020

SV-JME

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Condition monitoring and fault detection are nowadays popular topic. Different loads, enviroments etc. affect the components and systems differently and can induce the fault and faulty behaviour. Most of the approaches for the fault detection rely on the use of the good classification method. Gaussian mixture model based classification are stable and versatile methods which can be applied to a wide range of classification tasks. The main task is the estimation of the parameters in the Gaussian mixture model. Those can be estimated with various techniques. Therefore, the Gaussian mixture model based classification have different variants which can vary in performance. To test the performance of the Gaussian mixture model based classification variants and general usefulness of the Gaussian mixture model based classification for the fault detection, we have opted to use the bearing fault classification problem. Additionally, comparisons with other widely used non-parametric classification methods are made, such as support vector machines and neural networks. The performance of each classification method is evaluated by multiple repeated k-fold cross validation. From the results obtained, Gaussian mixture model based classification methods are shown to be competitive and efficient methods and usable in the field of fault detection and condition monitoring. Highlights• Gaussian-mixture-model-based classification was applied to the bearing-fault classification. • To discriminate the faulty from non-faulty bearings only simple statistics from vibrational data was used. • Two different datasets, the Case Western Rice University dataset and Bearing vibration data collected under time-varying rotational speed conditions dataset are used. • The Gaussian-mixture-model-based classification method showed to be a competitive and efficient method.

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“…The vibration signal processing techniques used in rolling-element bearing fault diagnosis mainly include time-domain analysis [2], frequency-domain analysis [3] and time-frequency analysis [4][5][6][7]. The wavelet analysis [4], short-time Fourier transform (STFT) [5], empirical mode decomposition [6] and singular value decomposition [7] are commonly used methods in time-frequency analysis of vibration signals of rolling-element bearing. The machine learning method used in rolling-element bearing fault diagnosis firstly extracts fault features from vibration signals, and then maps the extracted fault features into the fault type of rolling-element bearing.…”

Section: Introductionmentioning

confidence: 99%

Rolling-Element Bearing Fault Diagnosis Using Improved LeNet-5 Network

Wan

Chen

et al. 2020

Sensors

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To address the problems of low recognition accuracy, slow convergence speed and weak generalization ability of traditional LeNet-5 network used in rolling-element bearing fault diagnosis, a rolling-element bearing fault diagnosis method using improved 2D LeNet-5 network is put forward. The following improvements to the traditional LeNet-5 network are made: the convolution and pooling layers are reasonably designed and the size and number of convolution kernels are carefully adjusted to improve fault classification capability; the batch normalization (BN) is adopted after each convolution layer to improve convergence speed; the dropout operation is performed after each full-connection layer except the last layer to enhance generalization ability. To further improve the efficiency and effectiveness of fault diagnosis, on the basis of improved 2D LeNet-5 network, an end-to-end rolling-element bearing fault diagnosis method based on the improved 1D LeNet-5 network is proposed, which can directly perform 1D convolution and pooling operations on raw vibration signals without any preprocessing. The results show that the improved 2D LeNet-5 network and improved 1D LeNet-5 network achieve a significant performance improvement than traditional LeNet-5 network, the improved 1D LeNet-5 network provides a higher fault diagnosis accuracy with a less training time in most cases, and the improved 2D LeNet-5 network performs better than improved 1D LeNet-5 network under small training samples and strong noise environment.Sensors 2020, 20, 1693 2 of 23 clustering [10], back propagation neural network (BPNN) [11], etc. The traditional rolling-element bearing fault diagnosis methods have been widely used, but with the increasing complexity of vibration signals, these methods have a certain limitation; however, the deep learning methods have a greater advantage in analyzing complicated and non-stationary vibration signals.The deep learning methods can automatically extract fault features from vibration signals [12], recently there are many researches are conducted on rolling-element bearing fault diagnosis using deep learning. Yin et al. [13] extracted the original features of vibration signals through time-domain analysis, frequency-domain analysis and wavelet transform, and obtained the low-dimensional features from 38 original features using the nonlinear global algorithm, and the low-dimensional features array is input into the deep belief network (DBN) to evaluate the performance status of rolling-element bearing. Liu et al. [14] obtained the spectrogram of vibration signals through STFT, used the stacked sparse auto-encoder (SAE) to automatically extract fault features, and employed the softmax regression to identify the fault type of rolling-element bearing. Liu et al. [15] used the recurrent neural network (RNN) to classify the faults of rolling-element bearing, and adopted the gated recurrent unit based denoising auto-encoder to enhance fault classification accuracy. Among different deep learning methods, compared with DBN, ...

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Research on bearing fault feature extraction based on singular value decomposition and optimized frequency band entropy

Cited by 116 publications

References 26 publications

Short-Term Load Forecasting with Tensor Partial Least Squares-Neural Network

Short-Term Load Forecasting with Tensor Partial Least Squares-Neural Network

Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

Rolling-Element Bearing Fault Diagnosis Using Improved LeNet-5 Network

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