- Performing efficient and robust Fault and Detection Isolation (FDI)
- Resolving a constrained mixed integer nonlinear optimization program (MINLP)
- Optimal test design and sensor selection
- Consistent improvement of the accuracy and robustness of fault detection
The widespread use of electronic technologies and the growing level of complexity of these techniques challenge the ability to efficiently perform FDI with the objective of quickly and accurately diagnose system faults.
These techniques are of great importance since the inability to isolate faults can impact system maintenance cost, safety, reliability and performance. They are employed in different fields, going from rotary machines in refineries, robot manipulator for automotive industry to aircraft control systems, satellites, and space stations.
Optimal test design and sensor selection for FDI is an essential task to monitor and manage the system’s health. The authors formulate the FDI test design problem as a MINLP which allows to determine the optimal set of test settings and sensors maximizing the information collected on faults.
The convincing performance and the multi-start feature have led the authors to chose Artelys Knitro to solve these complex models. The calculated test designs lead to a consistent improvement of the accuracy and robustness of fault detection.