Fault Diagnosis of Rotating Machinery Based on Combination of Deep Belief Network and One-dimensional Convolutional Neural Network

Reliability of high precision linear motion system is one of the main concerns in industrial and military systems. The performance and repeatability of these systems are influenced by their respective linear drives and load bearings. A fault in these members severely affects the safe working of overall system. This paper gives a reliable intelligent approach to detect and classify faults for linear motion systems based on deep learning methods. Accuracy in faults identification is highly dependent on improved features extraction. For this purpose, a novel Residual Twin CNN (ResT-CNN) is proposed that uses combination of 1-D and 2-D CNN in parallel learning which improves features extraction performance; followed by knowledge base-Remnant-PCA (Kb-Rem-PCA) architecture in combination with multi-class support vector machine (Mc-SVM). This novel hybrid combination proved very effective in accurate faults identification. The performance of proposed methodology was also validated by IMS-UC (Intelligent Maintenance Systems-University of Cincinnati) public bearing dataset. The results confirm the effectiveness of proposed scheme in comparison to existing state of the art techniques.

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“…W represents width of kernel and K l i (j ) gives j th weight of the kernel. The output of general model [46] can be given as in Eq. (6):…”

Section: B Proposed Rest-cnn Architecturementioning

confidence: 99%

“…where, b is the bias of each layer. More literature details are included in [46]. The mentioned CNN model has been used in various combinations to solve engineering problems.…”

Section: B Proposed Rest-cnn Architecturementioning

confidence: 99%

An Intelligent Hybrid Scheme for Identification of Faults in Industrial Ball Screw Linear Motion Systems

et al. 2021

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“…The optimizer is an optimization algorithm used to update the model parameters iteratively based on the training dataset by calculating the error to minimize the loss function [45]. Adaptive moment estimation is an optimizer that is fast to converge, efficient in learning model parameters, and adequately solves practical deep learning problems [49,50]. Equations ( 9)-( 13) demonstrate the model parameters update using adaptive moment estimation optimizer, and the value of hyperparameters used in the Adam optimizer is presented in Table 5:…”

Section: Hyperparameters In the Convolutional Neural Networkmentioning

confidence: 99%

One-Dimensional Convolutional Neural Network with Adaptive Moment Estimation for Modelling of the Sand Retention Test

et al. 2021

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Stand-alone screens (SASs) are active sand control methods where compatible screens and slot sizes are selected through the sand retention test (SRT) to filter an unacceptable amount of sand produced from oil and gas wells. SRTs have been modelled in the laboratory using computer simulation to replicate experimental conditions and ensure that the selected screens are suitable for selected reservoirs. However, the SRT experimental setups and result analyses are not standardized. A few changes made to the experimental setup can cause a huge variation in results, leading to different plugging performance and sand retention analysis. Besides, conducting many laboratory experiments is expensive and time-consuming. Since the application of CNN in the petroleum industry attained promising results for both classification and regression problems, this method is proposed on SRT to reduce the time, cost, and effort to run the laboratory test by predicting the plugging performance and sand production. The application of deep learning has yet to be imposed in SRT. Therefore, in this study, a deep learning model using a one-dimensional convolutional neural network (1D-CNN) with adaptive moment estimation is developed to model the SRT with the aim of classifying plugging sign (screen plug, the screen does not plug) as well as to predict sand production and retained permeability using a varying sand distribution, SAS, screen slot size, and sand concentration as inputs. The performance of the proposed 1D-CNN model for the slurry test shows that the prediction of retained permeability and the classification of plugging sign achieved robust accuracy with more than a 90% value of R2, while the prediction of sand production achieved 77% accuracy. In addition, the model for the sand pack test achieved 84% accuracy in predicting sand production. For comparative model performance, gradient boosting (GB), K-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) were also modelled on the same datasets. The results showed that the proposed 1D-CNN model outperforms the other four machine learning models for both SRT tests in terms of prediction accuracy.

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“…The final key feature parameters are selected in this process. As for feature-based pattern classification methods, they include support vector machines, decision trees, random forests, and artificial neural networks [ 9 , 10 ]. As narrow models, however, the traditional fault diagnosis methods find it in no position to perfectly represent the nonlinear relationship between fault signals and fault categories.…”

Section: Introductionmentioning

confidence: 99%

Rotating machinery fault diagnosis based on a novel lightweight convolutional neural network

Yan

Liu

et al. 2021

PLoS ONE

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The advancement of Industry 4.0 and Industrial Internet of Things (IIoT) has laid more emphasis on reducing the parameter amount and storage space of the model in addition to the automatic and accurate fault diagnosis. In this case, this paper proposes a lightweight convolutional neural network (LCNN) method for intelligent fault diagnosis of rotating machinery, which can largely satisfy the need of less parameter amount and storage space as well as high accuracy. First, light-weight convolution blocks are constructed through basic elements such as spatial separable convolutions with the aim to effectively reduce model parameters. Secondly, the LCNN model for the intelligent fault diagnosis is constructed via lightweight convolution blocks instead of the tradi-tional convolution operation. Finally, to address the “black box” problem, the entire network is visualized through Tensorboard and t-distribution stochastic neighbor embedding. The results demonstrate that when the number of lightweight convolutional blocks reaches 6, the diagnosis accuracy of the LCNN model exceeds 99.9%. And the proposed model has become the most robust with parameters significantly decreasing. Furthermore, the proposed LCNN model has realized accurate, automatic, and robust fault diagnosis of rotating machinery, which makes it more suitable for deployment under the IIoT context.

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Fault Diagnosis of Rotating Machinery Based on Combination of Deep Belief Network and One-dimensional Convolutional Neural Network

Cited by 70 publications

References 46 publications

An Intelligent Hybrid Scheme for Identification of Faults in Industrial Ball Screw Linear Motion Systems

An Intelligent Hybrid Scheme for Identification of Faults in Industrial Ball Screw Linear Motion Systems

One-Dimensional Convolutional Neural Network with Adaptive Moment Estimation for Modelling of the Sand Retention Test

Rotating machinery fault diagnosis based on a novel lightweight convolutional neural network

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