Systems and methods for cognitive control of data acquisition for efficient fault diagnosis

摘要

Remote sensing techniques are being increasingly used for periodic structural health monitoring of vast infrastructures. Conventionally, analysis of visual and other signals captured from sensing devices are used to diagnose faults. Such data collection and analysis is expensive in terms of both computational overheads as well as towards robotic maneuvering of data collection systems, such as a UAV. In accordance with the present disclosure, the data acquisition system is modeled as an intelligent situated agent that autonomously controls data gathering and analysis activities through a cognitive cycle of perception-recognition-action, in order to optimize the cost of efforts in identifying faults that may exist. Also, a reactive, economical planning algorithm around Qualitative Bayesian Network (QBN) that controls the sequence of data collection and analysis has been implemented.