Generative Adversarial Neural Network for Unsupervised Bearing Fault Detection

2021 ◽  
Vol 263 (3) ◽  
pp. 3643-3648
Author(s):  
Gyuwon Kim ◽  
Seungchul Lee
Keyword(s):  
Economic Loss ◽  
Reconstruction Error ◽  
Fault Diagnostics ◽  
Safety Hazards ◽  
Generative Adversarial Network ◽  
Time Frequency ◽  
Bearing Fault ◽  
Electrical Systems ◽  
Adversarial Network ◽  
Short Time

Detecting bearing faults in advance is critical for mechanical and electrical systems to prevent economic loss and safety hazards. As part of the recent interest in artificial intelligence, deep learning (DL)-based principles have gained much attention in intelligent fault diagnostics and have mainly been developed in a supervised manner. While these works have shown promising results, several technical setbacks are inherent in a supervised learning setting. Data imbalance is a critical problem as faulty data is scarce in many cases, data labeling is tedious, and unseen cases of faults cannot be detected in a supervised framework. Herein, a generative adversarial network (GAN) is proposed to achieve unsupervised bearing fault diagnostics by utilizing only the normal data. The proposed method first adopts the short-time Fourier transform (STFT) to convert the 1-D vibration signals into 2-D time-frequency representations to use as the input to our (DL) framework. Subsequently, a GAN-based latent mapping is constructed using only the normal data, and faulty signals are detected using an anomaly metric comprised of a discriminator error and an image reconstruction error. The performance of our method is verified using a classic rotating machinery dataset (Case Western Reserve bearing dataset), and the experimental results demonstrate that our method can not only detect the faults but can also cluster the faults in the latent space with high accuracy.

Deep semi-supervised generative adversarial fault diagnostics of rolling element bearings

2019 ◽  
Vol 19 (2) ◽  
pp. 390-411 ◽  
Author(s):  
David Benjamin Verstraete ◽  
Enrique López Droguett ◽  
Viviana Meruane ◽  
Mohammad Modarres ◽  
Andrés Ferrada
Keyword(s):  
Multidimensional Data ◽  
Data Streaming ◽  
Rolling Element Bearings ◽  
Fault Diagnostics ◽  
Generative Adversarial Network ◽  
Vibration Data ◽  
Adversarial Network ◽  
Multidimensional Datasets ◽  
Rolling Element ◽  
Public Datasets

With the availability of cheaper multisensor suites, one has access to massive and multidimensional datasets that can and should be used for fault diagnosis. However, from a time, resource, engineering, and computational perspective, it is often cost prohibitive to label all the data streaming into a database in the context of big machinery data, that is, massive multidimensional data. Therefore, this article proposes both a fully unsupervised and a semi-supervised deep learning enabled generative adversarial network-based methodology for fault diagnostics. Two public datasets of vibration data from rolling element bearings are used to evaluate the performance of the proposed methodology for fault diagnostics. The results indicate that the proposed methodology is a promising approach for both unsupervised and semi-supervised fault diagnostics.

scholarly journals GMT-WGAN: An Adversarial Sample Expansion Method for Ground Moving Targets Classification

2021 ◽  
Vol 14 (1) ◽  
pp. 123
Author(s):  
Xin Yao ◽  
Xiaoran Shi ◽  
Yaxin Li ◽  
Li Wang ◽  
Han Wang ◽  
...  
Keyword(s):  
Feature Learning ◽  
Expansion Method ◽  
Classification Performance ◽  
Moving Target ◽  
Moving Targets ◽  
Target Classification ◽  
Deep Convolutional Neural Networks ◽  
Generative Adversarial Network ◽  
Time Frequency ◽  
Adversarial Network

In the field of target classification, detecting a ground moving target that is easily covered in clutter has been a challenge. In addition, traditional feature extraction techniques and classification methods usually rely on strong subjective factors and prior knowledge, which affect their generalization capacity. Most existing deep-learning-based methods suffer from insufficient feature learning due to the lack of data samples, which makes it difficult for the training process to converge to a steady-state. To overcome these limitations, this paper proposes a Wasserstein generative adversarial network (WGAN) sample enhancement method for ground moving target classification (GMT-WGAN). First, the micro-Doppler characteristics of ground moving targets are analyzed. Next, a WGAN is constructed to generate effective time–frequency images of ground moving targets and thereby enrich the sample database used to train the classification network. Then, image quality evaluation indexes are introduced to evaluate the generated spectrogram samples, with an aim to verify the distribution similarity of generated and real samples. Afterward, by feeding augmented samples to the deep convolutional neural networks with good generalization capacity, the classification performance of the GMT-WGAN is improved. Finally, experiments conducted on different datasets validate the effectiveness and robustness of the proposed method.

scholarly journals Adversarial Attack for SAR Target Recognition Based on UNet-Generative Adversarial Network

2021 ◽  
Vol 13 (21) ◽  
pp. 4358
Author(s):  
Chuan Du ◽  
Lei Zhang
Keyword(s):  
Target Recognition ◽  
Automatic Target Recognition ◽  
Reconstruction Error ◽  
Sar Images ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Scattering Centers ◽  
Weak Scattering ◽  
Adversarial Examples ◽  
Adversarial Attack

Some recent articles have revealed that synthetic aperture radar automatic target recognition (SAR-ATR) models based on deep learning are vulnerable to the attacks of adversarial examples and cause security problems. The adversarial attack can make a deep convolutional neural network (CNN)-based SAR-ATR system output the intended wrong label predictions by adding small adversarial perturbations to the SAR images. The existing optimization-based adversarial attack methods generate adversarial examples by minimizing the mean-squared reconstruction error, causing smooth target edge and blurry weak scattering centers in SAR images. In this paper, we build a UNet-generative adversarial network (GAN) to refine the generation of the SAR-ATR models’ adversarial examples. The UNet learns the separable features of the targets and generates the adversarial examples of SAR images. The GAN makes the generated adversarial examples approximate to real SAR images (with sharp target edge and explicit weak scattering centers) and improves the generation efficiency. We carry out abundant experiments using the proposed adversarial attack algorithm to fool the SAR-ATR models based on several advanced CNNs, which are trained on the measured SAR images of the ground vehicle targets. The quantitative and qualitative results demonstrate the high-quality adversarial example generation and excellent attack effectiveness and efficiency improvement.

scholarly journals Speech Dereverberation Using Deep Learning Algorithm

2021 ◽  
pp. 127-133
Author(s):  
Dr. S. Saraswathi ◽  
S. Ramya
Keyword(s):  
Learning Algorithm ◽  
Generative Adversarial Network ◽  
Convolutional Networks ◽  
Speech Dereverberation ◽  
Fully Convolutional Networks ◽  
Adversarial Network ◽  
Recent Success ◽  
Deep Learning Algorithm ◽  
Reverb Challenge ◽  
Short Time

This paper focuses on speech derverberation using a single microphone. We investigate the applicability of fully convolutional networks (FCN) to enhance the speech signal represented by short-time Fourier transform (STFT) images in light of their recent success in many image processing applications. We present two variants: a "U-Net," which is an encoder-decoder network with skip connections, and a generative adversarial network (GAN) with the U-Net as the generator, which produces a more intuitive cost function for training. To assess our method, we used data from the REVERB challenge and compared our results to those of other methods tested under the same conditions. In most cases, we discovered that our method outperforms the competing methods.

scholarly journals Through-the-Wall Micro-Doppler De-Wiring Technique via Cycle-Consistent Adversarial Network

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 124
Author(s):  
Shuoguang Wang ◽  
Ke Miao ◽  
Shiyong Li ◽  
Qiang An
Keyword(s):  
Human Motion ◽  
Ultra Wideband ◽  
Activity Detection ◽  
Generative Adversarial Network ◽  
Time Frequency ◽  
Adversarial Network ◽  
Uwb Radar ◽  
Frequency Representation ◽  
Human Motion Monitoring ◽  
Human Activity Detection

The radar penetrating technique has aroused a keen interest in the research community, due to its superior abilities for through-the-wall indoor human motion monitoring. Micro-Doppler signatures in this situation play a significant role in recognition and classification for human activities. However, the live wire buried in the wall introduces additive clutters to the spectrograms. Such degraded spectrograms drastically affect the performance of behind-the-wall human activity detection. In this paper, an ultra-wideband (UWB) radar system is utilized in the through-the-wall scenario to get the feature enhanced micro-Doppler signature called range-max time-frequency representation (R-max TFR). Then, a recently introduced Cycle-Consistent Generative Adversarial Network (Cycle GAN) is employed to realize the end-to-end de-wiring task. Cycle GAN can learn the mapping between spectrograms with and without the live wire effect. To minimize the wiring clutters, a loss function called identity loss is introduced in this work. Finally, the proposed de-wiring approach is evaluated through classification. The results show that the proposed Cycle GAN architecture outperforms other state-of-art de-wiring methods.

Rolling Bearing Fault Degree Recognition Based on Ensemble Empirical Mode Decomposition and Support Vector Regression

2013 ◽  
Vol 333-335 ◽  
pp. 550-554 ◽  
Author(s):  
Chang Qing Shen ◽  
Fei Hu ◽  
Zhong Kui Zhu ◽  
Fan Rang Kong
Keyword(s):  
Fault Diagnosis ◽  
Support Vector Regression ◽  
Empirical Mode Decomposition ◽  
Economic Loss ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Bearing Fault ◽  
Mode Decomposition

The research in bearing fault diagnosis has been attracting great attention in the past decades. Development of feasible fault diagnosis procedures to prevent failures that could cause huge economic loss timely is necessary. The whole life of the bearing is also a developing process for some sensitive features related to the fault trend. In this paper, a new scheme based on ensemble empirical mode decomposition (EEMD) and support vector regression (SVR) to conduct bearing fault degree recognition is proposed. This analysis first extracts the sensitive features from the intrinsic mode functions (IMFs) produced by EEMD which is a potential time-frequency analysis method, and then constructs an intelligent nonlinear model with input feature vectors extracted from the IMFs and defect size as output. Through validation of experimental data, the results indicated that the bearing fault degree could be effectively and precisely recognized.

scholarly journals Lightweight Convolutional Neural Network and Its Application in Rolling Bearing Fault Diagnosis under Variable Working Conditions

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4827 ◽  
Author(s):  
Hengchang Liu ◽  
Dechen Yao ◽  
Jianwei Yang ◽  
Xi Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Rolling Bearing ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Time Domain Signal ◽  
The One ◽  
Short Time

The rolling bearing is an important part of the train’s running gear, and its operating state determines the safety during the running of the train. Therefore, it is important to monitor and diagnose the health status of rolling bearings. A convolutional neural network is widely used in the field of fault diagnosis because it does not require feature extraction. Considering that the size of the network model is large and the requirements for monitoring equipment are high. This study proposes a novel bearing fault diagnosis method based on lightweight network ShuffleNet V2 with batch normalization and L2 regularization. In the experiment, the one-dimensional time-domain signal is converted into a two-dimensional Time-Frequency Graph (TFG) using a short-time Fourier transform, though the principle of graphics to enhance the TFG dataset. The model mainly consists of two units, one for extracting features and one for spatial down-sampling. The building units are repeatedly stacked to construct the whole model. By comparing the proposed method with the origin ShuffleNet V2, machine learning model and state-of-the-art fault diagnosis model, the generalization of the proposed method for bearing fault diagnosis is verified.

scholarly journals Time-Frequency Analysis and Target Recognition of HRRP Based on CN-LSGAN, STFT, and CNN

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jianghua Nie ◽  
Yongsheng Xiao ◽  
Lizhen Huang ◽  
Feng Lv
Keyword(s):  
Least Squares ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Target Recognition ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Feature Learning ◽  
Generative Adversarial Network ◽  
Time Frequency ◽  
Adversarial Network

Aiming at the problem of radar target recognition of High-Resolution Range Profile (HRRP) under low signal-to-noise ratio conditions, a recognition method based on the Constrained Naive Least-Squares Generative Adversarial Network (CN-LSGAN), Short-time Fourier Transform (STFT), and Convolutional Neural Network (CNN) is proposed. Combining the Least-Squares Generative Adversarial Network (LSGAN) with the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), the CN-LSGAN is presented and applied to the HRRP denoise. The frequency domain and phase features of HRRP are gained by STFT in order to facilitate feature learning and also match the input data format of the CNN. These experimental results show that the CN-LSGAN has better data augmentation performance and can effectively avoid the model collapse compared to the generative adversarial network (GAN) and LSGAN. Also, the method has better recognition performance than the one-dimensional CNN method and the Long Short-Term Memory (LSTM) network method.

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