Hybrid Fault Detection and Recovery in Air Handling Unit Temperature Sensors Based on Sensor Signal processing using Field Programmable Analog Arrays Model

In Air conditioning systems (HVAC), including heating, cooling and ventilation systems use much of the total energy consumed in commercial buildings. Air Handling Unit (AHU) is one of the most important parts in air conditioning systems. In a survey of the UK buildings, data has shown that 25-50% of energy is wasted due to errors in air-conditioned buildings. If these errors are identified in the early stages before unacceptable damage occur and recognized, this range can be reduced to below 15%.
Along with the evolution of energy-efficient air conditioning in commercial buildings, a wide range of diagnostics and fault detection methods continuously increased in the fields of air conditioning. Different techniques are developed using computer with the low installation cost for years to increase real-time detection of errors.
In this study, a method of fault recovery in temperature sensor of air handling unit has been proposed using simulation of field programing analog array (FPAA) and genetic algorithms as evolutionary algorithms. It is assumed that multiple sensors are working and the output is the average temperature sensors. In faulty situation, using the fault recovery method, the system is able to work properly with one of the sensors. The study consists of two parts, which is a part related to bias, scale and drift fault detection using time series method, noise fault detection using the sample standard deviation, constant fault detection using a voter and the another part is fault recovery using FPAA.