Diagnosis based on reliability analysis using monitors and sensors

We develop a process for using monitors or sensors to optimize diagnostic decision trees (DDTs) generated for large systems. We present algorithms for optimizing the diagnosis process, which combines evidence data captured from monitors or sensors into the diagnostic tree generation process to produce DDTs. Since evidence data can be extracted from monitors and sensors, we developed a method for sensor modeling. Our method allows modeling monitors or sensors as an abstract layer on top of a systems fault tree model. This method of modeling allows the designer to graphically link monitors or sensors to the components that they monitor, without impacting the reliability analysis. We use a real system from the industry to demonstrate the practicality and effectiveness of our algorithms and methods.