Application of Multiscale Learning Neural Network Based on CNN in Bearing Fault Diagnosis

Wang, Daichao; Guo, Qingwen; Yan, Shuicheng; Gao, Shengyao; Li, Yibin

doi:10.1007/s11265-019-01461-w

Cited by 82 publications

(41 citation statements)

References 25 publications

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“…Two-Dimensional (2-D) CNN was applied on the steady state current signals directly to find the fault level [39]. As reported in literature, 1-D and 2-D CNNs are mostly used in fault detection scenarios due to their high performance in feature extraction [40]. The mechanism of learning main features from any raw signal by using 2-D extraction features is illustrated in Figure 2.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Convolutional Neural Network-Based Inter-Turn Fault Diagnosis in LSPMSMs

et al. 2020

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Stator inter-turn fault diagnosis system for electric motors is of a considerable concern due to its significant effect on industrial production. In this paper, a new method for detecting the inter-turn fault and quantifying its severity in the line start permanent magnet synchronous motor (LSMPSM) is proposed. The new method depends on monitoring the stator current during steady-state period to detect the fault. The convolutional neural network (CNN) method is proposed to correlate the motor steady-state current with the status of the motor winding conditions and detect any presence of inter-turn faults. The data used in this study is extracted from both an experimental setup of a one-horsepower LSPMSM and the corresponding verified mathematical model through several testing cases under various loading conditions. One of the main features of the proposed technique is that it does not require separate feature extraction phase. The results indicate that the proposed technique is able to detect the inter-turn fault under different loading conditions varies from 0NM to 4NM with accuracy of 97.75% for all defined fault levels. The use of steady-state current for fault detection regardless of motor load enables the proposed technique to detect the fault online without disturbing the system functionality and reliability as well as without adding any extra hardware to the system. INDEX TERMS Convolutional neural network (CNN), diagnosis, fault detection, inter-turn fault, LSPMSM.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

Convolutional Neural Network-Based Inter-Turn Fault Diagnosis in LSPMSMs

et al. 2020

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“…2D-Rem-CNN model was trained and tested with Paderborn public bearing dataset. The same was used by Wang et al [43] to validate their developed fault diagnostic multi-scale network (MSN). The results are compared in Figure 14.…”

Section: Model Validation With Paderborn Public Bearing Datasetmentioning

confidence: 99%

A Novel 2-D Current Signal-Based Residual Learning With Optimized Softmax to Identify Faults in Ball Screw Actuators

et al. 2020

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Ball screw electro-mechanical actuators are commonly found in high precision motion control applications including aerospace systems as well as automated setups for industries. These actuators perform flight / application critical job and ball screw drives are responsible to provide precise linear motion while carrying thrust loading. A failure in ball screw drive may disturb positioning accuracy of overall system. At present, few techniques are available to monitor electro-mechanical actuators for aerospace and industrial systems. This paper provides a deep learning based intelligent technique to monitor condition of ball screw actuators. The proposed scheme utilizes modified residual learning scheme to extract features from two-dimensional transformed motor current signals. The current signal data was collected under different load domains in terms of magnitude and direction reversal. A 2D-Remanant-CNN (2D-Rem-CNN) model was developed for features extraction with proposed optimized softmax for classification of mechanical faults. The proposed technique was validated against different ball screw fault cases. The testing results prove the superiority of 2D-Rem-CNN model against different state of the art techniques. The proposed framework was also tested for system's stability under different load domains.

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“…CNN was first introduced to realize bearing fault diagnosis by O. Janssens in 2016 [1]. Thereafter, many improvements and variants have been proposed to strengthen the CNN's performance, such as 1D-CNN, 2D-CNN, multiscale CNN and adaptive CNN [2][3][4][5]. Though satisfying results have been obtained in the extensive research mentioned above, the premise to guarantee such results for data-driven methods is sufficient data for algorithm training.…”

Section: Introductionmentioning

confidence: 99%

Dynamics Modeling of Bearing with Defect in Modelica and Application in Direct Transfer Learning from Simulation to Test Bench for Bearing Fault Diagnosis

et al. 2022

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In data-driven bearing fault diagnosis, sufficient fault data are fundamental for algorithm training and validation. However, only very few fault measurements can be provided in most industrial applications, bringing the dynamics model to produce bearing response under defects. This paper built a Modelica model for the whole bearing test rig, including the test bearing, driving motor and hydraulic loading system. First, a five degree-of-freedom (5-DoF) model was proposed for the test bearing to identify the normal bearing dynamics. Next, a fault model was applied to characterize the defect position, defect size, defect shape and multiple defects. The virtual bearing test bench was first developed with OpenModelica and then called in Python with OMPython. For validation of the positive effect of the dynamics model in the direct transfer learning for bearing fault diagnosis, the simulation data from the Modelica model and experimental data from the Case Western Reserve University were fed separately or jointly to train a Convolution Neural Network (CNN). Then the well-trained CNN was transferred directly to achieve the fault diagnosis under the test set consisting of experiment data. Additionally, 157 features were extracted from both time-domain and frequency-domain and fed into CNN as input, and then four different validation cases were designed. The results confirmed the positive effect of simulation data in the CNN transfer learning, especially when the simulation data were added as auxiliary to experimental data, and improved CNN classification accuracy. Furthermore, it indicated that the simulation data from the bearing dynamics model could play a part in the actual experimental measurement when the collected data were insufficient.

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Application of Multiscale Learning Neural Network Based on CNN in Bearing Fault Diagnosis

Cited by 82 publications

References 25 publications

Convolutional Neural Network-Based Inter-Turn Fault Diagnosis in LSPMSMs

Convolutional Neural Network-Based Inter-Turn Fault Diagnosis in LSPMSMs

A Novel 2-D Current Signal-Based Residual Learning With Optimized Softmax to Identify Faults in Ball Screw Actuators

Dynamics Modeling of Bearing with Defect in Modelica and Application in Direct Transfer Learning from Simulation to Test Bench for Bearing Fault Diagnosis

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