Improvement of Generative Adversarial Network and Its Application in Bearing Fault Diagnosis: A Review

Ruan, Diwang; Chen, Xuran; Gühmann, Clemens; Yan, Jianping

doi:10.3390/lubricants11020074

Cited by 23 publications

(6 citation statements)

References 73 publications

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“…The number of adaptation steps is another parameter that can be optimized in the future work to generate a new anomaly index for facial deformities. Finally, different new deep learning models can be utilized in the future to improve the performance of the anomaly detection problem introduced in this work [64][65][66][67][68][69][70].…”

Section: Discussionmentioning

confidence: 99%

Unsupervised anomaly appraisal of cleft faces using a StyleGAN2-based model adaptation technique

et al. 2023

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A novel machine learning framework that is able to consistently detect, localize, and measure the severity of human congenital cleft lip anomalies is introduced. The ultimate goal is to fill an important clinical void: to provide an objective and clinically feasible method of gauging baseline facial deformity and the change obtained through reconstructive surgical intervention. The proposed method first employs the StyleGAN2 generative adversarial network with model adaptation to produce a normalized transformation of 125 faces, and then uses a pixel-wise subtraction approach to assess the difference between all baseline images and their normalized counterparts (a proxy for severity of deformity). The pipeline of the proposed framework consists of the following steps: image preprocessing, face normalization, color transformation, heat-map generation, morphological erosion, and abnormality scoring. Heatmaps that finely discern anatomic anomalies visually corroborate the generated scores. The proposed framework is validated through computer simulations as well as by comparison of machine-generated versus human ratings of facial images. The anomaly scores yielded by the proposed computer model correlate closely with human ratings, with a calculated Pearson’s r score of 0.89. The proposed pixel-wise measurement technique is shown to more closely mirror human ratings of cleft faces than two other existing, state-of-the-art image quality metrics (Learned Perceptual Image Patch Similarity and Structural Similarity Index). The proposed model may represent a new standard for objective, automated, and real-time clinical measurement of faces affected by congenital cleft deformity.

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Section: Discussionmentioning

confidence: 99%

Unsupervised anomaly appraisal of cleft faces using a StyleGAN2-based model adaptation technique

et al. 2023

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“…Some methods that should be considered include data transformations 6 and generative approaches. 71 Furthermore, modifying the objective function of the presented baseline model could allow the SpecAugment to be used, for example, by finding structures using self-supervised learning instead of supervised learning.…”

Section: Future Workmentioning

confidence: 99%

Transfer Learning for Anomaly Detection Using Bearings' Vibration Signals

Nieves-Avendano,

Deschrijver,

Van Hoecke

2023

The International Journal of Acoustics and Vibration

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Predictive maintenance is becoming increasingly important in the industry. Despite considerable advances in data collection and data-driven models, there are still limitations when deploying models in practice. One of the main limitations is the large datasets required to train these models. As a potential solution, transfer learning can be used to reuse knowledge acquired from large datasets for similar tasks under different conditions. This paper investigates the transferability of the specific anomaly detection scenario, an unsupervised learning task with heavily imbalanced label distribution. This paper uses a dataset generated from a bearing test platform in which bearings are run until failure under different operating conditions. A lightweight deep learning model, MobileNetV2, is employed to create a baseline model capable of detecting anomalies for a specific working condition. The model is then adapted using transfer learning to identify anomalies under new operating conditions with limited data accurately. The results show that the data for new conditions is insufficient to train an adequate model and that transfer learning can overcome this limitation. The adapted models can detect anomalies before the expert's knowledge reference value. Although this shows that transfer learning can detect anomalies earlier, the results must be evaluated carefully to avoid false positives. While anomaly detection aims to identify changes in feature distributions, transfer learning aims to align different feature distributions. Transfer learning for unsupervised learning has rarely been explored. To the best of our knowledge, this is one of the few works addressing it in the context of predictive maintenance for anomaly detection.

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“…As a result, collected data mostly pertain to normal operating conditions, with only a small amount of data related to faults [2,3]. S&I samples, compared to normal samples, have small sample sizes and imbalanced proportions [4]. Compared with the traditional method of fault diagnosis for balanced samples, the method of fault diagnosis in the case of S&I samples is of great significance for the application of intelligent fault diagnoses [5].…”

Section: Introductionmentioning

confidence: 99%

Improved Generative Adversarial Network for Bearing Fault Diagnosis with a Small Number of Data and Unbalanced Data

Qin,

Huang,

Pan

et al. 2024

Symmetry

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Fault data under real operating conditions are often difficult to collect, making the number of trained fault data small and out of proportion to normal data. Thus, fault diagnosis symmetry (balance) is compromised. This will result in less effective fault diagnosis methods for cases with a small number of data and data imbalances (S&I). We present an innovative solution to overcome this problem, which is composed of two components: data augmentation and fault diagnosis. In the data augmentation section, the S&I dataset is supplemented with a deep convolutional generative adversarial network based on a gradient penalty and Wasserstein distance (WDCGAN-GP), which solve the problems of the generative adversarial network (GAN) being prone to model collapse and the gradient vanishing during the training time. The addition of self-attention allows for a better identification and generation of sample features. Finally, the addition of spectral normalization can stabilize the training of the model. In the fault diagnosis section, fault diagnosis is performed through a convolutional neural network with coordinate attention (CNN-CA). Our experiments conducted on two bearing fault datasets for comparison demonstrate that the proposed method surpasses other comparative approaches in terms of the quality of data augmentation and the accuracy of fault diagnosis. It effectively addresses S&I fault diagnosis challenges.

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Improvement of Generative Adversarial Network and Its Application in Bearing Fault Diagnosis: A Review

Cited by 23 publications

References 73 publications

Unsupervised anomaly appraisal of cleft faces using a StyleGAN2-based model adaptation technique

Unsupervised anomaly appraisal of cleft faces using a StyleGAN2-based model adaptation technique

Transfer Learning for Anomaly Detection Using Bearings' Vibration Signals

Improved Generative Adversarial Network for Bearing Fault Diagnosis with a Small Number of Data and Unbalanced Data

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