Bearing Fault Diagnosis Method Based on EEMD and LSTM

Ping, Zou; Hou, Baocun; Jiang, Lei; Zhang, Zhenji

doi:10.15837/ijccc.2020.1.3780

Cited by 49 publications

(14 citation statements)

References 25 publications

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“…Abed et al [12] proposed a robust approach for fault diagnosis of Brushless DC Motors through feature extraction and reduction using discrete wavelet transform (DWT) and orthogonal fuzzy neighborhood discriminant analysis (OFNDA) from vibration and current signals and RNN model was used for classification of faults. P. Zou et al [13] focused on empirical mode decomposition (EMD) method which was combined with LSTM to obtain kurtosis value by extracting intrinsic mode functions (IMF) components and long-term dependencies from vibration signals to monitor the health status of an electrical machine. Cao, Lixiao et al [14] constructed a fault diagnosis framework by extracting ten time-domain statistical features from vibration signals under varying load conditions and these features were fed into deep Bi-directional LSTM to identify the faults of Wind Turbine Gearbox.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid 1D-CNN-Bi-LSTM based Model with Spatial Dropout for Multiple Fault Diagnosis of Roller Bearing

Choudakkanavar¹,

Mangai²

2022

IJACSA

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Fault diagnosis of roller bearings is a crucial and challenging task to ensure the smooth functioning of modern industrial machinery under varying load conditions. Traditional fault diagnosis methods involve preprocessing of the vibration signals and manual feature extraction. This requires domain expertise and experience in extracting relevant features to accurately detect the fault. Hence, it is of great significance to implement an intelligent fault diagnosis method that involves appropriate automatic feature learning and fault identification. Recent research has shown that deep learning is an effective technique for fault diagnosis. In this paper, a hybrid model based on 1D-CNN (One-Dimensional Convolution Neural Networks) with Bi-LSTM (Bi-directional Long-Short Term Memory) is proposed to classify 12 different fault types. Firstly, vibration signals are given as input to 1D-CNN to extract intrinsic features from the input signals. Then, the extracted features are fed into a Bi-LSTM model to identify the faults. The performance of the proposed method is enhanced by applying Softsign activation function in the Bi-LSTM layer and Spatial Dropout in the neural network. To analyze the effectiveness of the proposed method, Case Western Reserve University (CWRU) bearing data is considered for experimentation. The results demonstrated that the proposed model has attained an accuracy of 99.84% in classifying the various faults. The superiority of the proposed method is verified by comparing the predictive accuracy of the proposed method with the existing fault diagnosis methods.

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Section: Related Workmentioning

confidence: 99%

A Hybrid 1D-CNN-Bi-LSTM based Model with Spatial Dropout for Multiple Fault Diagnosis of Roller Bearing

Choudakkanavar¹,

Mangai²

2022

IJACSA

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“…13 That is to say, the gating mechanism is added to RNN, so that the problems of vanishing gradient and explosion gradient are well handled. 14 Du et al 2 proposed a bearing fault diagnosis method based on deep wavelet convolutional auto-encoder and LSTM, which realized high accuracy of bearing fault identification; Zhou et al 15 completed the self-adaptive extraction of vibration signal features by constructing a GRU–RNN bearing diagnosis model, while effectively avoiding the vanishing gradient problem, and realized the classification of bearing fault status; Chen et al 16 directly took the raw vibration data of the bearings as the input of the deep LSTM, making full use of the excellent processing ability of LSTM to time series; Rui et al 17 constructed a hybrid network structure based on GRU for bearing fault detection; Khorram et al 18 used equivalent temporal sequences as the input of a novel convolutional LSTM RNN to detect the bearing fault with the highest accuracy in the shortest possible time; Zou et al 19 proposed a new ensemble empirical mode decomposition (EEMD)–LSTM bearing fault diagnosis method, which combines the signal preprocessing technology with the EEMD method that can get clear fault feature signals, and LSTM technology to extract fault features automatically that improves the efficiency of fault feature extraction.…”

Section: Introductionmentioning

confidence: 99%

A new fault diagnosis model of rolling element bearing based on a recurrent neural network

Song

Zhu

Sun

2021

Proceedings of the Institution of Mechanical Engineers, Part E:

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The rolling element bearing is an important part of mechanical equipment, it has various kinds of malfunctions, the location of the fault may occur in the inner ring, outer ring, or rolling element of the bearing. Therefore, traditional methods of classification are difficult to classify and identify effectively. To improve the accuracy of bearing fault diagnosis, the deep learning method is used to diagnose the fault of the rolling element bearing. In this paper, the long short-term memory and gated recurrent unit are combined to build a bearing fault diagnosis model. On the other hand, this paper adjusts the hidden layer structure and optimizes the network parameters to establish a better long short-term memory–gated recurrent unit–long short-term memory diagnostic model and classify the fault types of bearings with Softmax. The model proposed in this paper can effectively diagnose the bearing fault under the bearing data set of Case Western Reserve University and the University of Cincinnati. Compared with the traditional long short-term memory and the gated recurrent unit, the model proposed in this paper has high accuracy in fault diagnosis as well as certain reliability and generalization ability.

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“…To solve the problem of the unknown type and characteristic frequency of rolling bearings in actual engineering applications, Yang et al [13] adopted the EEMD and stochastic resonance technology, established the cut-off frequency criterion and spectral amplification factor to select the effective component and verified the effectiveness of the proposed method through three rolling bearings experiments with different fault types. To improve the efficiency and accuracy of fault diagnosis, Zou et al [14] combined signal preprocessing, EEMD and the long-short-term memory (LSTM) algorithm to implement fault diagnosis of rolling bearings. Although EEMD overcomes the modal aliasing problem of EMD, it still has the endpoint effects [14].…”

Section: Introductionmentioning

confidence: 99%

“…To improve the efficiency and accuracy of fault diagnosis, Zou et al [14] combined signal preprocessing, EEMD and the long-short-term memory (LSTM) algorithm to implement fault diagnosis of rolling bearings. Although EEMD overcomes the modal aliasing problem of EMD, it still has the endpoint effects [14]. Cheng et al [15], [16] proposed local characteristic-scale decomposition (LCD), which could adaptively decompose complex multi-component signals into a series of single-component intrinsic scale components and effectively extract fault feature information.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rolling Bearing Based on GA-VMD and Improved WOA-LSSVM

Chen

Han

et al. 2020

IEEE Access

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To improve the fault identification accuracy of rolling bearings due to the problems of parameter optimization and low convergence accuracy, a novel fault diagnosis method for rolling bearings combining wavelet threshold de-noising, genetic algorithm optimization variational mode decomposition (GA-VMD) and the whale optimization algorithm based on the von Neumann topology optimization least squares support vector machine (VNWOA-LSSVM) is proposed in this manuscript. First, wavelet threshold de-noising is used to preprocess the vibration signal to reduce the noise and improve the signal-to-noise ratio (SNR). Second, a genetic algorithm (GA) is utilized to optimize the parameters of variational mode decomposition (VMD), and optimized VMD is adopted to extract the fault feature information. The VNWOA-LSSVM fault diagnosis model is built to train and identify the fault feature vectors. The proposed method is validated by experimental data. The results show that this method can not only effectively diagnose various damage positions and extents of rolling bearings but also has good identification accuracy.INDEX TERMS Wavelet threshold de-noising, genetic algorithm, variational modal decomposition, von Neumann topology, rolling bearing.

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Bearing Fault Diagnosis Method Based on EEMD and LSTM

Cited by 49 publications

References 25 publications

A Hybrid 1D-CNN-Bi-LSTM based Model with Spatial Dropout for Multiple Fault Diagnosis of Roller Bearing

A Hybrid 1D-CNN-Bi-LSTM based Model with Spatial Dropout for Multiple Fault Diagnosis of Roller Bearing

A new fault diagnosis model of rolling element bearing based on a recurrent neural network

Fault Diagnosis of Rolling Bearing Based on GA-VMD and Improved WOA-LSSVM

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