Multisensor Fusion Using Fuzzy Logic for an Integrated Smart Health Monitoring System

Abstract

This paper presents a generic model for an integrated smart health monitoring system for infrastructures using multisensor fusion and condition assessment sheets. Though various techniques for health monitoring have been discussed extensively in the literature, little attention has been given to obtain high quality data from the measurement and sensing system by using an intrinsic knowledge base. The method proposed in this paper uses measurement data from different types of sensors with different resolutions and fuses it 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 outputs 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. The time history of problems and solutions taken to correct them are stored as a condition assessment sheet (CAS) that shows the health of each sensor and the entire measurement system at a glance. This work finds applications in the health management of civil infrastructures, power plants, airplanes and rocket/shuttle test facilities.