Parallel computing and network analytics for fast Industrial Internet-of-Things (IIoT) machine information processing and condition monitoring

• Published in: Journal of manufacturing systems Vol. 46; pp. 282 - 293
• Main Authors: Kan, Chen, Yang, Hui, Kumara, Soundar
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2018
• Subjects: Big data analytics; Industrial Internet of Things (IIoT); Large-scale network; Machine condition monitoring; Parallel computing; Power consumption; Large-scale network; Big data analytics; Parallel computing; Power consumption; Machine condition monitoring; Industrial Internet of Things (IIoT)
• ISSN: 0278-6125; 1878-6642
• DOI: 10.1016/j.jmsy.2018.01.010

•This paper introduces a stochastic approach (rather than conventional deterministic algorithms) to significantly improve the computational efficiency of network embedding.•This paper develops a fast parallel algorithm (rather than traditional serial computing) to enable the embedding of large-scale machine networks in the context of IIoT.•The developed parallel-computing algorithm efficiently and effectively characterizes the variations of machine signatures for network modeling and monitoring. Rapid advancement in sensing, communication, and mobile technologies brings a new wave of Industrial Internet of Things (IIoT). IIoT integrates a large number of sensors for smart and connected monitoring of machine conditions. Sensor observations contain rich information on operational signatures of machines, thereby providing a great opportunity for machine condition monitoring and control. However, realizing the full potential of IIoT depends to a great extent on the development of new methodologies using big data analytics. This paper presents a new methodology for large-scale IIoT machine information processing, network modeling, condition monitoring, and fault diagnosis. First, we introduce a dynamic warping algorithm to characterize the dissimilarity of machine signatures (e.g., power profiles during operations). Second, we develop a stochastic network embedding algorithm to construct a large-scale network of IIoT machines, in which the dissimilarity between machine signatures is preserved in the network node-to-node distance. When the machine condition varies, the location of the corresponding network node changes accordingly. As such, node locations will reveal diagnostic information about machine conditions. However, the network embedding algorithm is computationally expensive in the presence of large amounts of IIoT-enabled machines. Therefore, we further develop a parallel computing scheme that harnesses the power of multiple processors for efficient network modeling of large-scale IIoT-enabled machines. Experimental results show that the developed algorithm efficiently and effectively characterizes the variations of signatures in both cycle-to-cycle and machine-to-machine scales. This new approach shows strong potentials for optimal machine scheduling and maintenance in the context of large-scale IIoT.