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

In this paper, we present systematically experimental and analytical evaluations on design of remote fault diagnosis systems for a PLC based automated system. In order to investigate the factors of remote architecture, operator's skill level, and fault nature's effect on diagnosis performance, comprehensive experiment evaluation and statistical analysis were conducted. The experiment compared three levels of remote architecture, two levels of operators' diagnosis performance on four typical faults in automated system. After 24 runs of experiment, performance evaluation including detection time, amount of information search, number of diagnostic tests tried, 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, we concluded that: 1) the architecture 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 proposed evaluation approach is systematic; it can be applied on design and evaluation of diagnostics systems on other automated systems.

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