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

By Liangzhi Li, Kaoru Ota, Mianxiong Dong. IEEE Transactions on Industrial Informatics, 2018

392 citations recorded by the source corpus.

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.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/9a7228ce42d5cc0f2dfb676155ec0cf570f83f9b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Xu Chen
2. Zhi Zhou
3. Liekang Zeng
4. Xiaofei Wang
5. Yiwen Han
6. Dusist Niyato
7. En Li
8. Dacheng Tao

In Computer Vision and Pattern Recognition

1. Xu Chen
2. Jing Zhang
3. Dacheng Tao
4. Liekang Zeng
5. Xiaofei Wang
6. Zhi Zhou
7. Yiwen Han
8. En Li

In Industrial and Manufacturing Engineering

1. Jing Zhang
2. Rajkumar Buyya
3. Xu Chen
4. Dusist Niyato
5. Francesco Piccialli
6. Imran Ahmed
7. Gwanggil Jeon

Citations in the corpus

• Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2019, methodology): “For autonomous manufacturing inspection, DeepIns [113] uses DL and edge computing to guarantee performance and process”
• Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things (2020, background): “There has recently been rapid progress in deep learning, with these positive effects also impacting IoT data processing, e.g., streaming data analysis [55], mobile multimedia processing [56], manufacturing inspection [57], and health monitoring.”
• AI for Next Generation Computing: Emerging Trends and Future Directions (2022, neutral): “Literature reported [97, 98, 99] that deep learning approaches for placement robot tasks in fog, however further research is needed.”
• Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review (2023, methodology): “In DeepIns, data are collected by the edge sensors and have two exit points: one at the edge server and the other one at the cloud.”
• HealthFog: An Ensemble Deep Learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in Integrated IoT and Fog Computing Environments (2019, background): “[18] proposed Fog based Efficient Manufacture Inspection (FEMI) system using deep learning for smart industry to process a large amount of data in an efficient manner.”
• Edge Computing with Artificial Intelligence: A Machine Learning Perspective (2022, background): “[145] build a DL-based product quality classification system for production quality monitoring, so that products with quality defects can be quickly detected on the edge side.”
• Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing (2019, methodology): “DeepIns [15] proposes a manufacture inspection system for the smart industry using DNN model early exit.”
• Edge Computing in Industrial Internet of Things: Architecture, Advances and Challenges (2020, background): “Edge computing is gradually applied in some IIoT scenarios (e.g., remote equipment monitoring [15]–[17], predictive maintenance [18], and quality control [19]).”
• IoT in Smart Cities: A Survey of Technologies, Practices and Challenges (2021, methodology): “Other approaches suggested in [178,179] propose a fog computing method along with edge computing systems suggested in [180,181].”
• From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where (2022, background): “Researchers in [17] and [18] presented a CNN model for a real-time product”
• Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects (2019, methodology): “, 2018 [36] Detection of defect type and degree CNN”
• A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective (2018, background): “[151] proposed efficient and robust deep learning models to inspect and detect defects in manufactured products.”