Residual-based fault detection and isolation in control environment agriculture

The detection and isolation of various faults in controlled environment agriculture is a notoriously complicated task, since various biological, sensory, and mechanical phenomena may interact with each other. In the present work, a residual-based approach is presented which enables the detection, isolation and quantification of different types of faults. For this purpose, observers are designed that can approximate the residuals despite model inaccuracies and measurement noise. The approach is demonstrated through experiments in a small-scale vertical farming unit whereby it is possible to distinguish between different fault types during operation.