Application Combining VMD and ResNet101 in Intelligent Diagnosis of Motor Faults

Lin, Shih-Lin

doi:10.3390/s21186065

Cited by 18 publications

(14 citation statements)

References 39 publications

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“…ResNet101 has shown excellent static image FE ability in various research [26], so it is used as the benchmark network for spatial flow and temporal flow FE. Then, a 3DCNN is obtained by expanding the traditional 2D ResNet101, as in Fig.…”

Section: Figure 5 Structure Of the Ms-3dcnn Modulementioning

confidence: 99%

The Application of Deep Convolution Neural Network in Volleyball Video Behavior Recognition

Liang

2022

IEEE Access

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The purpose is to solve the problems of solid subjectivity in the traditional manual analysis of volleyball game videos and the traditional Human Behavior Recognition (HBR) algorithm, such as excessive calculation, high hardware conditions, and poor long-stream video modeling ability. Firstly, this paper expounds on the relevant theories. Secondly, a fusion Convolutional Neural Network (CNN)-based HBR model is implemented by combining two-stream CNN (TSCNN), Three-Dimensional (3D) CNN, and Long Short-Term Memory (LSTM) network. Notably, the LSTM has an excellent long-term Dynamic Information Extraction (DIE) ability. Finally, the public dataset is selected to verify the model's volleyball-game-videooriented HBR performance. The experimental results corroborate that: (1) The parameters of the proposed fusion-CNN-based HBR model are determined as follows: the number of video segments is three, the average method is used for feature fusion, and then the HBR accuracy is the highest when the fusion ratio of spatial feature map and temporal feature map is 4:6, and the learning rate is 0.0014. (2) The average HBR accuracy of the proposed fusion-CNN-based HBR model on three different datasets is 4%, 2.7%, and 3% higher than that of other popular networks, respectively. The improvement effect of the model is remarkable, and it is suitable for the study of Human Behavior Analysis (HBA) in volleyball match videos. Finally, the proposed HBR model can provide more accurate results for volleyball video-based HBR, which is significant in promoting the rapid development of volleyball sports.

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Section: Figure 5 Structure Of the Ms-3dcnn Modulementioning

confidence: 99%

The Application of Deep Convolution Neural Network in Volleyball Video Behavior Recognition

Liang

2022

IEEE Access

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“…Therefore, the de-noising process under various production conditions is crucial for feature extraction. The adaptive method [31][32][33][34][35][36][37][38][39][40][41] can strengthen the system's robustness in actual production, and improve the utilization rate and yield of the process equipment. Adaptive methods can be implemented differently depending on the nature of the target object being predicted.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Machinery Fault Diagnosis Method Based on Adaptive Deep Convolutional Neural Network: Using Dental Milling Cutter Malfunction Classifications as an Example

Chen

Lin

et al. 2023

Applied Sciences

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Intelligent machinery fault diagnosis is one of the key technologies for the transformation and competitiveness of traditional factories. Complex production environments make it difficult to maintain good prediction performance using traditional methods. This paper proposes a deep convolutional neural network combined with an adaptive environmental noise method to achieve robust fault classification. The proposed method uses six-dimensional physical signals for data fusion and feature fusion, extracts obvious features and enhances subtle features, and uses continuous wavelets and Gramian angular fields to transform signals with different physical and frequency characteristics into time–frequency maps and two-dimensional images. The fusion technology of different signals can provide comprehensive features for fault prediction, improving upon the blind spots of traditional methods to extract features, and then perform prediction and classification through deep convolutional neural networks. In the experiment, the tool failure classification of the dental milling machine is used as a verification case. The results show that the prediction accuracy of the proposed method is nearly 100%, much better than other comparison methods. In addition, white noise was added in the experiment to verify the noise immunity of the model. The results show that the accuracy of the proposed method is 99%, which is better than other comparison methods in terms of accuracy and robustness, proving the effectiveness of the proposed method for fault diagnosis and classification.

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“…VMD technique is used to decompose the input signal into different intrinsic mode functions (IMFs) where each IMFs has a dominant frequency. In this case, if the effects of two dominant frequencies emerge close to each other, separation and isolation of these two effects cannot be carried out properly [24][25][26]. Therefore, in the no-load condition (low slips) in which the RAF characteristic frequency appears close to the supply frequency, the detection of faults in the electrical signature, where RAF modulates with supply frequency, is complicated.…”

Section: Introductionmentioning

confidence: 99%

A Novel Mode Un-Mixing Approach in Variational Mode Decomposition for Fault Detection in Wound Rotor Induction Machines

et al. 2023

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Condition monitoring of induction machines (IMs) with the aim of increasing the machine’s lifetime, improving the efficiency and reducing the maintenance cost is necessary and inevitable. Among different types of methods presented for mechanical and electrical fault tracing in induction machines, stator current signature analysis has attracted great attention in recent decades. This popularity is mainly due to the non-invasive nature of this technique. A non-recursive method named variational mode decomposition (VMD) is used for the decomposition of any signal into several intrinsic mode functions (IMFs). This technique can be employed for detection of faulty components in a current signature. However, mode mixing of extracted IMFs makes the mechanical and electrical fault detection of IMs complicated, especially in the case where fault indices emerge close to the supply frequency. To achieve this, we rectify the signal of stator current prior to applying VMD. The main advantage of the presented approach is allowing the fault indices to be properly demodulated from the main frequency to avoid mode mixing phenomenon. The method shows that the dominant frequencies of the current signal can be isolated in each IMFs, appropriately. The proposed strategy is validated to detect the rotor asymmetric fault (RAF) in a wound rotor induction machine (WRIM), in both transient and steady-state conditions.

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Application Combining VMD and ResNet101 in Intelligent Diagnosis of Motor Faults

Cited by 18 publications

References 39 publications

The Application of Deep Convolution Neural Network in Volleyball Video Behavior Recognition

The Application of Deep Convolution Neural Network in Volleyball Video Behavior Recognition

Intelligent Machinery Fault Diagnosis Method Based on Adaptive Deep Convolutional Neural Network: Using Dental Milling Cutter Malfunction Classifications as an Example

A Novel Mode Un-Mixing Approach in Variational Mode Decomposition for Fault Detection in Wound Rotor Induction Machines

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