FPGA-Based Methodology for Detecting Positional Accuracy Degradation in Industrial Robots

Galan-Uribe, Ervin; Morales-Velazquez, Luis; Osornio-Ríos, Roque Alfredo

doi:10.3390/app13148493

Cited by 4 publications

(1 citation statement)

References 50 publications

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“…To avoid structural failure and severe loss, it is necessary to quickly detect damage in composite structures to prolong their service life. PHM technology provides early damage detection and helps avoid the deterioration of various industrial systems [7][8][9][10][11][12]. Recently, techniques for PHM based on Machine Learning (ML) and Deep Learning (DL) using vibration signals have been continuously adopted for fault diagnosis in various structures.…”

Section: Introductionmentioning

confidence: 99%

Delamination Detection Framework for the Imbalanced Dataset in Laminated Composite Using Wasserstein Generative Adversarial Network-Based Data Augmentation

Kim,

Azad,

Song

et al. 2023

Applied Sciences

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As laminated composites are applied more commonly, Prognostics and Health Management (PHM) techniques for the maintenance of composite systems are also attracting attention. However, applying PHM techniques to a composite system is challenging due to the data imbalance problem from the lack of failure data and unpredictable failure cases. Despite numerous studies conducted to address this limitation, including techniques like data augmentation and transfer learning, significant challenges remain. In this study, the Wasserstein Generative Adversarial Network (WGAN) model using a time-series data augmentation technique is proposed as a solution to the data imbalance problem. To ensure the performance of the WGAN model, time-series data augmentation of experimental data is executed with a frequency analysis. After that, a One-Dimensional Convolutional Neural Network (1D CNN) is used for fault diagnosis in laminated composites, validating the performance improvement after data augmentation. The proposed data augmentation significantly elevated the performance of the 1D CNN classification model compared to its non-augmented counterpart. Specifically, the accuracy increased from 89.20% to 91.96%. The precision improved remarkably from 29.76% to 74.10%, and its sensitivity rose from 33.33% to 94.39%. Collectively, these enhancements highlight the vital role of data augmentation in improving fault diagnosis performance.

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Section: Introductionmentioning

confidence: 99%

Delamination Detection Framework for the Imbalanced Dataset in Laminated Composite Using Wasserstein Generative Adversarial Network-Based Data Augmentation

Kim,

Azad,

Song

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

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Detection of Positional Accuracy Degradation of Industrial Robots Based on Kinematic Analysis

Yang,

Qi,

Xie

et al. 2023

2023 IEEE International Conference on Electrical, Automation and Computer Engineering (ICEACE)

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Self-Adaptable Software for Pre-Programmed Internet Tasks: Enhancing Reliability and Efficiency

Martínez García,

Martínez Rodríguez,

Pérez Zúñiga

2024

Applied Sciences

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In the current digital landscape, artificial intelligence-driven automation has revolutionized efficiency in various areas, enabling significant time and resource savings. However, the reliability and efficiency of software systems remain crucial challenges. To address this issue, a generation of self-adaptive software has emerged with the ability to rectify errors and autonomously optimize performance. This study focuses on the development of self-adaptive software designed for pre-programmed tasks on the Internet. The software stands out for its self-adaptation, automation, fault tolerance, efficiency, and robustness. Various technologies such as Python, MySQL, Firebase, and others were employed to enhance the adaptability of the software. The results demonstrate the effectiveness of the software, with a continuously growing self-adaptation rate and improvements in response times. Probability models were applied to analyze the software’s effectiveness in fault situations. The implementation of virtual cables and multiprocessing significantly improved performance, achieving higher execution speed and scalability. In summary, this study presents self-adaptive software that rectifies errors, optimizes performance, and maintains functionality in the presence of faults, contributing to efficiency in Internet task automation.

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FPGA-Based Methodology for Detecting Positional Accuracy Degradation in Industrial Robots

Cited by 4 publications

References 50 publications

Delamination Detection Framework for the Imbalanced Dataset in Laminated Composite Using Wasserstein Generative Adversarial Network-Based Data Augmentation

Delamination Detection Framework for the Imbalanced Dataset in Laminated Composite Using Wasserstein Generative Adversarial Network-Based Data Augmentation

Detection of Positional Accuracy Degradation of Industrial Robots Based on Kinematic Analysis

Self-Adaptable Software for Pre-Programmed Internet Tasks: Enhancing Reliability and Efficiency

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