A mixture of experts approach for SHM measurement processing

One of the greatest challenges in deploying structural health monitoring (SHM) systems is the need to manage the continuous stream of measurements obtained from tens or hundreds of installed sensors. In a practical system the analysis of these measurements must be performed in an automated and robust manner and be completed in real-time. As the first stage in this process, a neural computing based novelty detection system has been developed which is capable of modelling the basic behaviour of a structure and subsequently isolating noteworthy measurements. In this article we examine the trade-off between the system's need to adapt to normal changes in a structures behaviour over the long-term, with the need to maintain a reliable reference model so as to identify important events when they occur. It is demonstrated that extending the existing basic neural processing system, by introducing a 'mixture of experts' approach, can address the contradictory needs of adaptability and model stability. In addition, it is shown that this approach provides a means of incorporating detection of both short-term and long-term phenomena into a single integrated processing system.