Fault Detection and Diagnosis in Deep-trough Hydroponics using Intelligent Computational Tools

Abstract

The intelligent computational tools of feedforward neural networks and genetic algorithms are used to develop a real-time detection and diagnosis system of specific mechanical, sensor and plant (biological) failures in a deep-trough hydroponic system. The capabilities of the system are explored and validated. In the process of designing the fault detection neural network model, a new technique for neural network designing and training parameterisation is developed, based on the heuristic optimisation method of genetic algorithms. Sensor and actuator faults are detected and diagnosed in sufficient time that the fault detection model can be applied on-line as a reliable supervisor of the operation of an unattended deep-trough hydroponic system. Biological faults were not detected in general. It seems that the interaction between plants and their root-zone microenvironment is not equally balanced, as the condition of the plants is highly influenced by the conditions in their root zone microenvironment, while these microenvironment conditions (as they are represented by the measurable variables) are not influenced in the same degree by the conditions of the plants. Finally, the genetic algorithm system developed here can be successfully applied to a combinatorial problem such as deciding the best neural network architecture, activation functions and training algorithm for a specific model.