An End-to-End Model Based on Improved Adaptive Deep Belief Network and Its Application to Bearing Fault Diagnosis

Xie, Jiaqi; Du, Guifu; Shen, Changqing; Chen, Nan; Chen, Liang; Zhu, Zhongkui

doi:10.1109/access.2018.2877447

Cited by 62 publications

(37 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If there are a small number of particles, the search range of the PSO algorithm is small, which makes it difficult to obtain solutions that meet the expected goals. The eigenvalues extracted when the sliding window size is 360 are used as the input data of the PSO-ANN model, and different particle swarm numbers (10,20,30,40,50, and 60, respectively) are used to initialize the PSO-ANN model for multifeature fusion fault prediction. The relationship between the number of iterations of the PSO algorithm initialized by different numbers of particles and the loss value of the ANN is shown in Figure 9 (the maximum number of iterations of the PSO algorithm was uniformly set to 100).…”

Section: Feature-level Fusion Fault Prediction Experiments Based On a mentioning

confidence: 99%

“…Presently, widely-used mechanical fault prediction methods employ artificial neural networks (ANNs) [4][5][6], support vector machines (SVMs) [7,8], deep learning [9][10][11], and other artificial intelligence (AI) technologies. For example, Ben et al [12] proposed the use of empirical mode decomposition and energy entropy for feature extraction, which was combined with an ANN for multifeature fusion to make bearing fault predictions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data

Huang

Liu

2019

Sensors

View full text Add to dashboard Cite

Vibration sensing data is an important resource for mechanical fault prediction, which is widely used in the industrial sector. Artificial neural networks (ANNs) are important tools for classifying vibration sensing data. However, their basic structures and hyperparameters must be manually adjusted, which results in the prediction accuracy easily falling into the local optimum. For data with high levels of uncertainty, it is difficult for an ANN to obtain correct prediction results. Therefore, we propose a multifeature fusion model based on Dempster-Shafer evidence theory combined with a particle swarm optimization algorithm and artificial neural network (PSO-ANN). The model first used the particle swarm optimization algorithm to optimize the structure and hyperparameters of the ANN, thereby improving its prediction accuracy. Then, the prediction error data of the multifeature fusion using a PSO-ANN is repredicted using multiple PSO-ANNs with different single feature training to obtain new prediction results. Finally, the Dempster-Shafer evidence theory was applied to the decision-level fusion of the new prediction results preprocessed with prediction accuracy and belief entropy, thus improving the model's ability to process uncertain data. The experimental results indicated that compared to the K-nearest neighbor method, support vector machine, and long short-term memory neural networks, the proposed model can effectively improve the accuracy of fault prediction.

show abstract

Section: Feature-level Fusion Fault Prediction Experiments Based On a mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data

Huang

Liu

2019

Sensors

View full text Add to dashboard Cite

show abstract

“…In the data acquisition step, vibration signals, motor current signals, temperature signals and acoustic emission signals are frequently used for analysis [ 5 , 6 ]. In the feature extraction step, statistical time domain features such as root mean square, skewness as well as kurtosis [ 7 ] and frequency domain features exposed by Fourier transform [ 8 ] are the common choices to feed to the diagnosis models. To some extent, these features can effectively distinguish the primary differences between various health conditions.…”

Section: Introductionmentioning

confidence: 99%

A Novel End-To-End Fault Diagnosis Approach for Rolling Bearings by Integrating Wavelet Packet Transform into Convolutional Neural Network Structures

Xiong

Zhou

et al. 2020

Sensors

View full text Add to dashboard Cite

Accidental failures of rotating machinery components such as rolling bearings may trigger the sudden breakdown of the whole manufacturing system, thus, fault diagnosis is vital in industry to avoid these massive economical costs and casualties. Since convolutional neural networks (CNN) are poor in extracting reliable features from original signal data, the time-frequency analysis method is usually called for to transform 1D signal into a 2D time-frequency coefficient matrix in which richer information could be exposed more easily. However, realistic fault diagnosis applications face a dilemma in that signal time-frequency analysis and fault classification cannot be implemented together, which means manual signal conversion work is also needed, which reduces the integrity and robustness of the fault diagnosis method. In this paper, a novel network named WPT-CNN is proposed for end-to-end intelligent fault diagnosis of rolling bearings. WPT-CNN creatively uses the standard deep neural network structure to realize the wavelet packet transform (WPT) time-frequency analysis function, which seamlessly integrates fault diagnosis domain knowledge into deep learning algorithms. The overall network architecture can be trained with gradient descent backpropagation algorithms, indicating that the time-frequency analysis module of WPT-CNN is also able to learn the dataset characteristics, adaptively representing signal information in the most suitable way. Two experimental rolling bearing fault datasets were used to validate the proposed method. Testing results showed that WPT-CNN obtained the testing accuracies of 99.73% and 99.89%, respectively, in two datasets, which exhibited a better and more reliable diagnosis performance than any other existing deep learning and machine learning methods.

show abstract

“…Data-driven fault diagnosis methods based on machine learning have also been intensively developed in recent years [18]. Fault classifiers based on decision trees (DT) [19], [20], support vector machine (SVM) [21], [22], k-nearest neighbor (k-NN) [23], [24], convolutional neural network (CNN) [25]- [27] and deep belief networks (DBN) [28], [29] are well applied to deal with bearing fault detection. All mentioned machine learning based methods require historical failure data for training, which is hard to obtain in industry.…”

Section: Introductionmentioning

confidence: 99%

Health Indicator for Low-Speed Axial Bearings Using Variational Autoencoders

et al. 2020

View full text Add to dashboard Cite

This paper proposes a method for calculating a health indicator (HI) for low-speed axial rolling element bearing (REB) health assessment by utilizing the latent representation obtained by variational inference using Variational Autoencoders (VAEs), trained on each speed reference in the dataset. Further, versatility is added by conditioning on the speed, extending the VAE to a conditional VAE (CVAE), thereby incorporating all speeds in a single model. Within the framework, the coefficients of autoregressive (AR) models are used as features. The dimensionality reduction inherent in the proposed method lowers the need of expert knowledge to design good condition indicators. Moreover, the suggested methodology allows for setting the probability of false alarms when encoding new data points to the latent variable space using the trained model. The effectiveness of the proposed method is validated based on two different datasets: from a workshop test of an offshore drilling machine and from an in-house test rig for axial bearings. In both datasets, the HI is exceeding the warning and alarm levels with a probability of false alarm (PFA) of 10 −6 , and the method is most effective at lower shaft speeds. INDEX TERMS Bearing fault detection, condition monitoring, unsupervised learning, variational autoencoder, conditional variational autoencoder, generative models.

show abstract

An End-to-End Model Based on Improved Adaptive Deep Belief Network and Its Application to Bearing Fault Diagnosis

Cited by 62 publications

References 27 publications

Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data

Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data

A Novel End-To-End Fault Diagnosis Approach for Rolling Bearings by Integrating Wavelet Packet Transform into Convolutional Neural Network Structures

Health Indicator for Low-Speed Axial Bearings Using Variational Autoencoders

Contact Info

Product

Resources

About