Advances in Wireless Damage Detection for Structural Health Monitoring ; Edistysaskelia vaurioiden langattomaan ilmaisemiseen rakenteiden kunnonvalvonnassa

Aalto University, P.O. Box 11000, FI-00076 Aalto www.aalto.fi Author Janne Toivola Name of the doctoral dissertation Advances in Wireless Damage Detection for Structural Health Monitoring Publisher School of Science Unit Department of Information and Computer Science Series Aalto University publication series DOCTORAL DISSERTATIONS 80/2014 Field of research Computer and Information Science Manuscript submitted 16 October 2013 Date of the defence 18 June 2014 Permission to publish granted (date) 15 May 2014 Language English Monograph Article dissertation (summary + original articles) Abstract One of the fundamental tasks in structural health monitoring is to extract relevant information about a structure, such as a bridge or a crane, and reach statistical decisions about the existence of damages in the structure. Recent advances in wireless sensor network technology has offered new possibilities for acquiring and processing structural health monitoring data automatically.One of the fundamental tasks in structural health monitoring is to extract relevant information about a structure, such as a bridge or a crane, and reach statistical decisions about the existence of damages in the structure. Recent advances in wireless sensor network technology has offered new possibilities for acquiring and processing structural health monitoring data automatically. The purpose of this dissertation is to explore various data processing methods for detecting previously unobserved deviation in measurements from accelerometer sensors, based on natural vibration of structures. Part of the processing is projected to be performed on resource constrained wireless sensors to ultimately reduce the cost of measurements. Data processing in the proposed detection systems is divided into following general stages: feature extraction, dimensionality reduction, novelty detection, and performance assessment. Several methods in each of the stages are proposed and benchmarked in offline experiments with multiple accelerometer data sets. The methods include, for example, the Goertzel algorithm, random projection, tensor decomposition, collaborative filtering, nearest neighbor classification, and evaluating detection accuracy in terms of receiver operating characteristic curves. Significant reductions are achieved in the amount of data transmitted from sensors and input to statistical classifiers, while maintaining some of the classification accuracy. However, the sensitivity and specificity in detection are worse than those of centralized methods operating on raw sensor data. The work proposed and evaluated several combinations of data processing stages for wireless damage detection. While better than random detection accuracy can be achieved with very small amount of data per accelerometer sensor, challenges remain in reaching specificity required in practical applications.