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A hybrid computing solution and resource scheduling strategy for edge computing in smart manufacturing

- Li, Xiaomin, Wan, Jiafu, Dai, Hong-Ning, Imran, Muhammad, Xia, Min, Celesti, Antonio

  • Authors: Li, Xiaomin , Wan, Jiafu , Dai, Hong-Ning , Imran, Muhammad , Xia, Min , Celesti, Antonio
  • Date: 2019
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Industrial Informatics Vol. 15, no. 7 (2019), p. 4225-4234
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  • Description: At present, smart manufacturing computing framework has faced many challenges such as the lack of an effective framework of fusing computing historical heritages and resource scheduling strategy to guarantee the low-latency requirement. In this paper, we propose a hybrid computing framework and design an intelligent resource scheduling strategy to fulfill the real-time requirement in smart manufacturing with edge computing support. First, a four-layer computing system in a smart manufacturing environment is provided to support the artificial intelligence task operation with the network perspective. Then, a two-phase algorithm for scheduling the computing resources in the edge layer is designed based on greedy and threshold strategies with latency constraints. Finally, a prototype platform was developed. We conducted experiments on the prototype to evaluate the performance of the proposed framework with a comparison of the traditionally-used methods. The proposed strategies have demonstrated the excellent real-time, satisfaction degree (SD), and energy consumption performance of computing services in smart manufacturing with edge computing. © 2005-2012 IEEE.

Data-driven prognosis method using hybrid deep recurrent neural network

- Xia, Min, Zheng, Xi, Imran, Muhammad, Shoaib, Muhammad

  • Authors: Xia, Min , Zheng, Xi , Imran, Muhammad , Shoaib, Muhammad
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Applied Soft Computing Journal Vol. 93, no. (2020), p.
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  • Description: Prognostics and health management (PHM) has attracted increasing attention in modern manufacturing systems to achieve accurate predictive maintenance that reduces production downtime and enhances system safety. Remaining useful life (RUL) prediction plays a crucial role in PHM by providing direct evidence for a cost-effective maintenance decision. With the advances in sensing and communication technologies, data-driven approaches have achieved remarkable progress in machine prognostics. This paper develops a novel data-driven approach to precisely estimate the remaining useful life of machines using a hybrid deep recurrent neural network (RNN). The long short-term memory (LSTM) layers and classical neural networks are combined in the deep structure to capture the temporal information from the sequential data. The sequential sensory data from multiple sensors data can be fused and directly used as input of the model. The extraction of handcrafted features that relies heavily on prior knowledge and domain expertise as required by traditional approaches is avoided. The dropout technique and decaying learning rate are adopted in the training process of the hybrid deep RNN structure to increase the learning efficiency. A comprehensive experimental study on a widely used prognosis dataset is carried out to show the outstanding effectiveness and superior performance of the proposed approach in RUL prediction. © 2020 Elsevier B.V.
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