Study of factors impacting remote diagnosis performance on a PLC based automated system

Abstract

In this paper, we present systematically experimental and analytical evaluations on design of remote fault diagnosis systems for a programmable logic controller (PLC) based automated system. In order to investigate the factors of remote architecture, operator's skill level, and fault's effect on diagnosis performance, comprehensive experimental evaluations, statistical analysis and survey were conducted. The experiment compared three levels of remote architectures, two levels of operators’ skill levels on four typical faults in an automated system. Performance evaluation including detection time, amount of information search, number of diagnostic tests, number of asked questions, number of system runs, and performance score, were extracted from the experiment record. Two-stage statistical analysis including (1) analysis of variance (ANOVA) and (2) least significant difference (LSD) paired comparison was conducted on the performance evaluation data. From the statistical analysis results and expert survey, we concluded that: (1) the architecture sophistication eased the diagnosis on the faults that are related to the measurement signals, and (2) the diagnosis performance also increased with the sophistication of the architecture, but (3) operator's skill level did not significantly affect the diagnosis performance. The survey results on troubleshooters’ opinions and preferences about the diagnosis were also summarized, which can be applied for improvement on design of remote diagnosis system. The proposed evaluation approach is systematic; it can be applied on design and evaluation of diagnostics systems on other PLC based automated systems such as heating, ventilation, and air conditioning (HVAC), robotics assembly.