Tool Condition Monitoring Method Based on Generative Adversarial Network for Data Augmentation

2021 ◽  
Author(s):  
Yongqing Wang ◽  
Mengmeng Niu ◽  
Kuo Liu ◽  
Honghui Wang ◽  
Mingrui Shen ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Data Augmentation ◽  
Tool Condition Monitoring ◽  
Generative Adversarial Network ◽  
Monitoring Method ◽  
Data Set ◽  
Learning Network ◽  
Adversarial Network ◽  
Tool Condition ◽  
Deep Learning Network

Abstract In the process of parts processing, due to the real working conditions and data acquisition equipment, the collected working data of tools are actually limited. Meanwhile, the tool usually works in the normal state, so it is prone to cause the problem of unbalanced data set, which restricts the accuracy of tool condition monitoring. Aiming at this problem, this paper proposes a tool condition monitoring method based on generative adversarial network (GAN) for data augmentation. Specifically, first collect original samples data during processing in different tool conditions, then the collected sample data is input into GAN, and the generator of GAN can generate new samples which has similar distribution with original samples from tool condition signals data, finally the real samples and generated samples are combined to train deep learning network to predict tool conditions. Experimental results show that the proposed method can significantly improve the accuracy of tool condition monitoring. This paper compares and visualizes the impact of the training data set on the classification ability of the deep learning network model. In addition, some traditional methods are used for comparison, and F1 measure is introduced to evaluate the quality of the results. The results show that this method is better than the Adaptive Synthetic Sampling (Adasyn), add-noise, and resampling.

scholarly journals Data Augmentation Using Generative Adversarial Network for Automatic Machine Fault Detection Based on Vibration Signals

2021 ◽  
Vol 11 (5) ◽  
pp. 2166
Author(s):  
Van Bui ◽  
Tung Lam Pham ◽  
Huy Nguyen ◽  
Yeong Min Jang
Keyword(s):  
Fault Detection ◽  
Data Augmentation ◽  
Model Performance ◽  
Original Data ◽  
Fault Classification ◽  
Training Process ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Adversarial Network ◽  
Machine Fault

In the last decade, predictive maintenance has attracted a lot of attention in industrial factories because of its wide use of the Internet of Things and artificial intelligence algorithms for data management. However, in the early phases where the abnormal and faulty machines rarely appeared in factories, there were limited sets of machine fault samples. With limited fault samples, it is difficult to perform a training process for fault classification due to the imbalance of input data. Therefore, data augmentation was required to increase the accuracy of the learning model. However, there were limited methods to generate and evaluate the data applied for data analysis. In this paper, we introduce a method of using the generative adversarial network as the fault signal augmentation method to enrich the dataset. The enhanced data set could increase the accuracy of the machine fault detection model in the training process. We also performed fault detection using a variety of preprocessing approaches and classified the models to evaluate the similarities between the generated data and authentic data. The generated fault data has high similarity with the original data and it significantly improves the accuracy of the model. The accuracy of fault machine detection reaches 99.41% with 20% original fault machine data set and 93.1% with 0% original fault machine data set (only use generate data only). Based on this, we concluded that the generated data could be used to mix with original data and improve the model performance.

Underwater Acoustic Target Recognition Based on Generative Adversarial Network Data Augmentation

2021 ◽  
Vol 263 (2) ◽  
pp. 4558-4564
Author(s):  
Minghong Zhang ◽  
Xinwei Luo
Keyword(s):  
Data Augmentation ◽  
Target Recognition ◽  
Training Data ◽  
Small Samples ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Underwater Acoustic ◽  
Adversarial Network ◽  
Acoustic Target ◽  
The Impact

Underwater acoustic target recognition is an important aspect of underwater acoustic research. In recent years, machine learning has been developed continuously, which is widely and effectively applied in underwater acoustic target recognition. In order to acquire good recognition results and reduce the problem of overfitting, Adequate data sets are essential. However, underwater acoustic samples are relatively rare, which has a certain impact on recognition accuracy. In this paper, in addition of the traditional audio data augmentation method, a new method of data augmentation using generative adversarial network is proposed, which uses generator and discriminator to learn the characteristics of underwater acoustic samples, so as to generate reliable underwater acoustic signals to expand the training data set. The expanded data set is input into the deep neural network, and the transfer learning method is applied to further reduce the impact caused by small samples by fixing part of the pre-trained parameters. The experimental results show that the recognition result of this method is better than the general underwater acoustic recognition method, and the effectiveness of this method is verified.

scholarly journals Data Augmentation for Electricity Theft Detection Using Conditional Variational Auto-Encoder

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4291
Author(s):  
Xuejiao Gong ◽  
Bo Tang ◽  
Ruijin Zhu ◽  
Wenlong Liao ◽  
Like Song
Keyword(s):  
Latent Variables ◽  
Data Augmentation ◽  
Sampling Technique ◽  
Smart Meters ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Electricity Theft ◽  
Adversarial Network ◽  
Low Dimensional ◽  
Power Curves

Due to the strong concealment of electricity theft and the limitation of inspection resources, the number of power theft samples mastered by the power department is insufficient, which limits the accuracy of power theft detection. Therefore, a data augmentation method for electricity theft detection based on the conditional variational auto-encoder (CVAE) is proposed. Firstly, the stealing power curves are mapped into low dimensional latent variables by using the encoder composed of convolutional layers, and the new stealing power curves are reconstructed by the decoder composed of deconvolutional layers. Then, five typical attack models are proposed, and the convolutional neural network is constructed as a classifier according to the data characteristics of stealing power curves. Finally, the effectiveness and adaptability of the proposed method is verified by a smart meters’ data set from London. The simulation results show that the CVAE can take into account the shapes and distribution characteristics of samples at the same time, and the generated stealing power curves have the best effect on the performance improvement of the classifier than the traditional augmentation methods such as the random oversampling method, synthetic minority over-sampling technique, and conditional generative adversarial network. Moreover, it is suitable for different classifiers.

Tool Wear Monitoring Using Microphone Signals and Recurrence Quantification Analysis when Drilling Composites

2013 ◽  
Vol 711 ◽  
pp. 239-244 ◽  
Author(s):  
Eshetu D. Eneyew ◽  
M. Ramulu
Keyword(s):  
Condition Monitoring ◽  
Cutting Tool ◽  
Flank Wear ◽  
Recurrence Quantification Analysis ◽  
Tool Condition Monitoring ◽  
Monitoring Method ◽  
Online Tool ◽  
Tool Condition ◽  
Quantification Analysis

The quality of the hole produced during the drilling of composite materials is one of the controlling factors for the resulting joint strength and integrity of the structural component. Quality of the hole depends on the condition of the cutting tool. Continuous cutting tool condition monitoring method is vital to accomplish the desired hole quality. To address this concern, an online tool condition monitoring technique using a simple audio microphone as a sensor is developed and Recurrence Quantification Analysis (RQA) methodology was used as a signal analysis tool to predict the tool condition in terms of flank wear. A series of experimental drilling operation was carried out on uni-directional carbon fiber reinforced plastic (CFRP) composite. It was found that the amplitude of the microphone signal decreases with the increase of the tool flank wear. In addition, from the selected eight RQA output variables, six of them show an increasing trend with the increase of the measured flank wear, whereas, two of them show a decreasing trend with the increase of tool wear. The same trend has been observed in both set of experiments. These results demonstrate that, this novel approach is an effective and economical online tool condition monitoring method.

scholarly journals State Monitoring Method for Tool Wear in Aerospace Manufacturing Processes Based on a Convolutional Neural Network (CNN)

Aerospace ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 335
Author(s):  
Wei Dai ◽  
Kui Liang ◽  
Bin Wang
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Convolutional Neural Network ◽  
Condition Monitoring ◽  
Tool Condition Monitoring ◽  
Manufacturing Processes ◽  
State Monitoring ◽  
Monitoring Method ◽  
Tool Condition ◽  
Aerospace Manufacturing

In the aerospace manufacturing field, tool conditions are essential to ensure the production quality for aerospace parts and reduce processing failures. Therefore, it is extremely necessary to develop a suitable tool condition monitoring method. Thus, we propose a tool wear process state monitoring method for aerospace manufacturing processes based on convolutional neural networks to recognize intermediate abnormal states in multi-stage processes. There are two innovations and advantages of the proposed approach: one is that the criteria for judging abnormal conditions are extended, which is more useful for practical application. The other is that the proposed approach solved the influence of feature-to-recognition stability. Firstly, the tool wear level was divided into different state modes according to the probability density interval based on the kernel density estimation (KDE), and the corresponding state modes were connected to obtain the point-to-point control limit. Then, the state recognition model based on a convolutional neural network (CNN) was developed, and the sensitivity of the monitoring window was considered in the model. Finally, open-source datasets were used to verify the feasibility of the proposed method, and the results demonstrated the applicability of the proposed method in practice for tool condition monitoring.

scholarly journals Spectra Recognition Model for O-type Stars Based on Data Augmentation

2021 ◽  
Vol 8 ◽  
Author(s):  
Wen-Yu Yang ◽  
Ke-Fei Wu ◽  
A-Li Luo ◽  
Zhi-Qiang Zou
Keyword(s):  
Neural Network ◽  
Data Augmentation ◽  
Network Models ◽  
Neural Network Models ◽  
Generative Adversarial Network ◽  
Validation Data ◽  
Data Set ◽  
Recognition Model ◽  
Adversarial Network ◽  
The Neural Network

It is an ongoing issue in astronomy to recognize and classify O-type spectra comprehensively. The neural network is a popular recognition model based on data. The number of O-stars collected in LAMOST is <1% of AFGK stars, and there are only 127 O-type stars in the data release seven version. Therefore, there are not enough O-type samples available for recognition models. As a result, the existing neural network models are not effective in identifying such rare star spectra. This paper proposed a novel spectra recognition model (called LCGAN model) to solve this problem with data augmentation, which is based on Locally Connected Generative Adversarial Network (LCGAN). The LCGAN introduced the locally connected convolution and two timescale update rule to generate O-type stars' spectra. In addition, the LCGAN model adopted residual and attention mechanisms to recognize O-type spectra. To evaluate the performance of proposed models, we conducted a comparative experiment using a stellar spectral data set, which consists of more than 40,000 spectra, collected by the large sky area multi-object fiber spectroscopic telescope (LAMOST). The experimental results showed that the LCGAN model could generate meaningful O-type spectra. In our validation data set, the recognition accuracy of the data enhanced recognition model can reach 93.67%, 8.66% higher than that of the non-data enhanced identification model, which lays a good foundation for further analysis of astronomical spectra.

scholarly journals A deep learning segmentation strategy that minimizes the amount of manually annotated images

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 256
Author(s):  
Thierry Pécot ◽  
Alexander Alekseyenko ◽  
Kristin Wallace
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Training Data ◽  
Artificial Data ◽  
Deep Convolutional Neural Networks ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Segmentation Strategy ◽  
Adversarial Network ◽  
The Impact

Deep learning has revolutionized the automatic processing of images. While deep convolutional neural networks have demonstrated astonishing segmentation results for many biological objects acquired with microscopy, this technology's good performance relies on large training datasets. In this paper, we present a strategy to minimize the amount of time spent in manually annotating images for segmentation. It involves using an efficient and open source annotation tool, the artificial increase of the training data set with data augmentation, the creation of an artificial data set with a conditional generative adversarial network and the combination of semantic and instance segmentations. We evaluate the impact of each of these approaches for the segmentation of nuclei in 2D widefield images of human precancerous polyp biopsies in order to define an optimal strategy.

scholarly journals Optimization of Cardiac Magnetic Resonance Synthetic Image Based on Simulated Generative Adversarial Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ying Fu ◽  
MinXue Gong ◽  
Guang Yang ◽  
JinRong Hu ◽  
Hong Wei ◽  
...  
Keyword(s):  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Data Augmentation ◽  
Small Sample ◽  
Training Data ◽  
Synthetic Image ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Adversarial Network ◽  
Convolution Model

The generative adversarial network (GAN) has advantage to fit data distribution, so it can achieve data augmentation by fitting the real distribution and synthesizing additional training data. In this way, the deep convolution model can also be well trained in the case of using a small sample medical image data set. However, some certain gaps still exist between synthetic images and real images. In order to further narrow those gaps, this paper proposed a method that applies SimGAN on cardiac magnetic resonance synthetic image optimization task. Meanwhile, the improved residual structure is used to deepen the network structure to improve the performance of the optimizer. Lastly, the experiments will show the good result of our data augmentation method based on GAN.

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