Multisensor Integration and Fusion Model That Uses a Fuzzy Inference System

Abstract

Abstract The main objective of this work is the development of an intelligent multisensor integration and fusion model that uses fuzzy logic. Measurement data from different types of sensors with different resolutions is integrated and fused together based on the confidence in them derived from information not typically used in traditional data fusion methods. Examples of such information are operating temperature, frequency range, fatigue cycles, etc. These are fed as additional inputs to a fuzzy inference system (FIS) that has predefined membership functions for each of these variables. The output of the FIS are weights that are assigned to the different sensor measurement data that reflect the confidence in the sensor’s behavior and performance. A modular approach is adopted for the data fusion system. It allows adding or deleting a sensor, along with its fuzzy logic controller (FLC), anytime without affecting the entire data fusion system. This paper presents a preliminary model that fuses the data from three different types of sensors that monitor the strain at a single location in a cantilever beam. This will be later extended to sensors that will be fixed at different locations on the same beam. The resulting model will then be tested on a sub-scale composite bridge for a smart structural health monitoring system. The results from the proposed work are a stepping stone towards the development of generic autonomous sensor models that are capable of data interpretation, self-calibration, data fusion from other sources and even learning so as to improve their performance with time.