Avoiding Human Error
Jan Eric Larsson · 2000
Human error is a common source of accidents in complex plants. We believe that many human errors really are caused by lack of intelligence in the instrumentation and control systems, putting the operators in situations, which humans realistically cannot be expected to cope with. Through history, several computer-based algorithms have been proposed and used for automated sensor fault detection, alarm analysis, and fault diagnosis, to support human operators. The main problem with such algorithms are that they demand a large effort to build, validate, and especially rebuild when the plant is changed. We propose the use of algorithms based on Multilevel Flow Models (MFM), which are graphical models of goals and functions of technical systems. MFM provides a good basis for computer-based supervision and diagnosis, especially in real -time applications, were fast execution and guaranteed worst-case response times are essential. The expressive power of MFM is similar to that of rule -based expert systems, while the explicit representation of means-end knowledge and the graphical nature of the models make the knowledge engineering effort less and the execution efficiency higher than that of standard expert systems. If MFM -based measurement validation and alarm analysis had been used, the Three -Mile Island incident would not have happened.