Abstract
Since the 1980s, the manufacturing environment has seen the introduction of numerous sensor/actuator bus and network protocols for automation systems, which led to increased manufacturing productivity, improved inter- changeability of devices from different vendors, facilitated flexibility and reconfigurability for various applications and improved reliability, while reducing installation and maintenance costs. However, such heightened manufacturing integration facilitated by industrial networks also leads to dramatic consequences of improper or degraded network operation. This paper presents a novel Network Health Management system that provides diagnostic and prognostic information for DeviceNet at the device and network level. It extracts features from analog waveform communication signals, as well as logic and timing features from digital packet logs. These features are used to evaluate the network system performance degradation by applying multidimensional clustering techniques. In addition, this work proposes a hybrid prognostics structure using combined physical and logic layer features to provide fault location information that cannot easily be realized with analog or digital data independently. Furthermore, an intermittent connection diagnostic algorithm which analyzes patterns of interrupted and error packets on the network was developed. This tool can be used as a packet source identification method which uses joint analog features and digital information inferred from analog waveforms. A test-bed was constructed and the experiments of network impedance mismatch, cable degradation, and intermittent connections were conducted in laboratory environment. Experimental results show that the proposed system can detect degradations of the network and identify the location of the intermittent connection successfully. Field tests performed in an industrial environment were also conducted and their results are discussed.
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Lei, Y., Djurdjanovic, D., Barajas, L. et al. DeviceNet network health monitoring using physical layer parameters. J Intell Manuf 22, 289–299 (2011). https://doi.org/10.1007/s10845-009-0291-9
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DOI: https://doi.org/10.1007/s10845-009-0291-9