Multisensor integration and fusion model that uses a fuzzy inference system

The main objective of this work is the development of an intelligent multis ensor integration and fusion model that uses fuzzy logic. Measurement data from different types of sensors with different resolutions are integrated a nd fused together based on the confidence in them derived from information not typically used in traditional data fusion methods. Examples of such inf ormation are operating temperature, frequency range, fatigue cycles, etc. T hese are fed as additional inputs to a fuzzy inference system (FIS) that ha s predefined membership functions for each of these variables. The output o f 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 pre liminary model that fuses the data from three different types of sensors th at 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 t he same beam. The results from the proposed work are a stepping stone towar d the development of generic autonomous sensor models that are capable of d ata interpretation, self-calibration, data fusion from other sources, and e ven learning so as to improve their performance with time. This work is aim ed at the development of smart structural health monitoring systems, but ha s applications in diverse fields such as robotics, controls, target trackin g, and biomedical imaging.