Deep Learning for Smart Industry: Efficient Manufacture Inspection System With Fog Computing

TLDR

The rapid growth of IoT sensors in industry generates large data volumes, and manufacturing inspection must detect product defects across many assembly lines, posing real‑time processing challenges. The study proposes a deep‑learning classification model to robustly detect defective products with higher accuracy. The system uses fog computing by offloading computation from a central server to fog nodes, enabling real‑time processing of large data volumes. Experiments show the fog‑adapted CNN improves computing efficiency and the inspection model simultaneously identifies defect type and severity, demonstrating robustness and efficiency.

Abstract

With the rapid development of Internet of things devices and network infrastructure, there have been a lot of sensors adopted in the industrial productions, resulting in a large size of data. One of the most popular examples is the manufacture inspection, which is to detect the defects of the products. In order to implement a robust inspection system with higher accuracy, we propose a deep learning based classification model in this paper, which can find the possible defective products. As there may be many assembly lines in one factory, one huge problem in this scenario is how to process such big data in real time. Therefore, we design our system with the concept of fog computing. By offloading the computation burden from the central server to the fog nodes, the system obtains the ability to deal with extremely large data. There are two obvious advantages in our system. The first one is that we adapt the convolutional neural network model to the fog computing environment, which significantly improves its computing efficiency. The other one is that we work out an inspection model, which can simultaneously indicate the defect type and its degree. The experiments well prove that the proposed method is robust and efficient.