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Research on deep learning in the field of mechanical equipment fault diagnosis image quality

机译:机械设备故障诊断图像质量领域的深度学习研究

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摘要

Image quality assessment (IQA) is an indispensable technique in computer vision, which is widely applied in image classification, image clustering. With the development of deep learning, deep neural network (DNN)-based methods have shown impressive performance. Thus, in this paper, we propose a novel method for mechanical equipment fault diagnosis based on IQA. More specifically, we first conduct data acquisition base on our practice. Afterwards, we leverage image processing method for removing noise. Subsequently, we leverage CNN-based method for image classification. Finally, different mechanical equipment images will be grouped into different categories and fault detection can be achieved. Extensive experiments demonstrate the effectiveness and robustness of our method. (C) 2019 Published by Elsevier Inc.
机译:图像质量评估(IQA)是计算机视觉中必不可少的技术,已广泛应用于图像分类,图像聚类。随着深度学习的发展,基于深度神经网络(DNN)的方法已显示出令人印象深刻的性能。因此,本文提出了一种基于IQA的机械设备故障诊断的新方法。更具体地说,我们首先根据我们的实践进行数据采集。此后,我们利用图像处理方法去除噪声。随后,我们利用基于CNN的方法进行图像分类。最后,将不同的机械设备图像分为不同的类别,并且可以实现故障检测。大量的实验证明了我们方法的有效性和鲁棒性。 (C)2019由Elsevier Inc.发布

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