Signal Generation using 1d Deep Convolutional Generative Adversarial Networks for Fault Diagnosis of Electrical Machines

Sabir, Russell; Rosato, Daniele; Hartmann, Sven; Gühmann, Clemens

doi:10.1109/icpr48806.2021.9413119

Cited by 17 publications

(11 citation statements)

References 13 publications

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“…The rest of the architecture maintains the standard GAN architecture, allowing the synthesis of audio tracks with unsupervised training GAN. This approximation has also been followed by Sabir et al [109] for augmenting DC current signals, using samples of current signal with a frequency of 100 Hz during 16 s. The proposed work used the deep convolutional GAN (DCGAN) architecture as a base and changes the original convolutions to 1D convolutions. In particular, this work has two different GANs, one that generates healthy signals and the other is in charge of generating faulty data.…”

Section: D Convolutional Ganmentioning

confidence: 99%

Data Augmentation techniques in time series domain: a survey and taxonomy

Iglesias

Talavera

González-Prieto

et al. 2023

Neural Comput & Applic

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With the latest advances in deep learning-based generative models, it has not taken long to take advantage of their remarkable performance in the area of time series. Deep neural networks used to work with time series heavily depend on the size and consistency of the datasets used in training. These features are not usually abundant in the real world, where they are usually limited and often have constraints that must be guaranteed. Therefore, an effective way to increase the amount of data is by using data augmentation techniques, either by adding noise or permutations and by generating new synthetic data. This work systematically reviews the current state of the art in the area to provide an overview of all available algorithms and proposes a taxonomy of the most relevant research. The efficiency of the different variants will be evaluated as a central part of the process, as well as the different metrics to evaluate the performance and the main problems concerning each model will be analysed. The ultimate aim of this study is to provide a summary of the evolution and performance of areas that produce better results to guide future researchers in this field.

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Section: D Convolutional Ganmentioning

confidence: 99%

Data Augmentation techniques in time series domain: a survey and taxonomy

Iglesias

Talavera

González-Prieto

et al. 2023

Neural Comput & Applic

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“…Table 1 provides a summary of those methods and their applications. Ö Wafer [2,4] Ö Assembly and test [5] Ö Ö Conveyor belt [17] Ö Electrical machines [18] Ö Solar cells [19] Ö Non-destructive testing [20,21] Ö…”

Section: -4 / S Mou • Invited Paper Sid 2022 Digest • 975mentioning

confidence: 99%

“…For example, Tang et al [19] used FID and MMD to compare the performance of WGAN and DCGAN in generated synthetic defect solar cell images. Sabir et al [18] used DCGAN to generate a 1-dimensional faulty signal of electrical machines, and Fréchet inception distance (FID) [36] is used to evaluate generated signal quality. Yan [26] combined the CGAN [16] and WGAN [14] to synthesize multiple types of chiller fault samples.…”

Section: Evaluation Metrics For Generated Defectsmentioning

confidence: 99%

See 1 more Smart Citation

72‐4: Invited Paper: Synthetic Defect Generation for Display Front‐of‐Screen Quality Inspection: A Survey

Mou

Cao

Hong

et al. 2022

Symp Digest of Tech Papers

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Display front‐of‐screen (FOS) quality inspection is essential for the mass production of displays in the manufacturing process. However, the severe imbalanced data, especially the limited number of defective samples, has been a long‐standing problem that hinders the successful application of deep learning algorithms. Synthetic defect data generation can help address this issue. This paper reviews the state‐of‐the‐art synthetic data generation methods and the evaluation metrics that can potentially be applied to display FOS quality inspection tasks.

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“…In [ 11 ], researchers created adversarial networks (DCGAN, LSGAN, and WGAN) to overcome an insufficient number of images for their training model. GANs and DCGANs have been used to establish systems by which to monitor one-dimensional current waveforms [ 12 ]. GANs have been used to increase the accuracy of CNNs for the diagnosis of bladder cancer [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Integration of Deep Learning Network and Robot Arm System for Rim Defect Inspection Application

Mao

Chiu

Lin

et al. 2022

Sensors

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Automated inspection has proven to be the most effective approach to maintaining quality in industrial-scale manufacturing. This study employed the eye-in-hand architecture in conjunction with deep learning and convolutional neural networks to automate the detection of defects in forged aluminum rims for electric vehicles. RobotStudio software was used to simulate the environment and path trajectory for a camera installed on an ABB robot arm to capture 3D images of the rims. Four types of surface defects were examined: (1) dirt spots, (2) paint stains, (3) scratches, and (4) dents. Generative adversarial network (GAN) and deep convolutional generative adversarial networks (DCGAN) were used to generate additional images to expand the depth of the training dataset. We also developed a graphical user interface and software system to mark patterns associated with defects in the images. The defect detection algorithm based on YOLO algorithms made it possible to obtain results more quickly and with higher mean average precision (mAP) than that of existing methods. Experiment results demonstrated the accuracy and efficiency of the proposed system. Our developed system has been shown to be a helpful rim defective detection system for industrial applications.

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Signal Generation using 1d Deep Convolutional Generative Adversarial Networks for Fault Diagnosis of Electrical Machines

Cited by 17 publications

References 13 publications

Data Augmentation techniques in time series domain: a survey and taxonomy

Data Augmentation techniques in time series domain: a survey and taxonomy

72‐4: Invited Paper: Synthetic Defect Generation for Display Front‐of‐Screen Quality Inspection: A Survey

Integration of Deep Learning Network and Robot Arm System for Rim Defect Inspection Application

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