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DeviceNet reliability assessment using physical and data link layer parameters
Author(s) -
Lei Yong,
Djurdjanovic Dragan,
Ni Jun
Publication year - 2010
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.1131
Subject(s) - reliability (semiconductor) , testbed , node (physics) , computer science , network packet , automation , reliability engineering , flexibility (engineering) , application layer , computer network , engineering , physics , mathematics , structural engineering , mechanical engineering , power (physics) , statistics , quantum mechanics , operating system , software deployment
Abstract Since the 1990s, the increasing deployments of networked automation systems led to increased manufacturing productivity, improved interchangeability of devices from different vendors, facilitated flexibility and reconfigurability for various applications and improved reliability, while reducing installation and maintenance costs. However, the reliability of a network has great impact on the reliability of a networked automation system. This paper presents a novel network reliability assessment method that provides diagnostic and prognostic information for DeviceNet. This work proposes a hybrid network error analysis method using combined physical and datalink layer features to provide complete communication log information. Furthermore, a network/node time to failure (bus‐off) prediction algorithm was developed based on the analysis of the patterns of the interrupted packets on the network. The method developed in this study can be used for network reliability evaluation and diagnosis, facilitating better network maintenance decision making. A laboratory testbed was constructed and the experiments on network and node time to failure were conducted to demonstrate the concept. Experimental results show that the proposed method can fully reconstruct the communication log, and predict the network/node bus‐off time successfully. Copyright © 2010 John Wiley & Sons, Ltd.