Developing a pattern-based method for detecting defective sensors in an instrumented bridge
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It is important to assure the reliability of a structural health monitoring (SHM) system before interpreting the monitoring data for the detection of structural anomalies. Finding a malfunctioning component such as a sensor is an important step in that direction. Damage detection techniques in civil structures fall in the following two categories: data driven and structural model based. The data-driven methods provide a direct approach to damage assessment in a structure without creating any structural model (e.g., finite element model). Existence of damage and its location are interpreted by pattern matching of the data series of strain gauges, and temperature gauges at different time ranges. The objective of this study was to explore such methods, including the autoregressive exogenous model, and based on that, develop new techniques to detect defective sensors. As a case study, the SHM data from the Portage Creek Bridge, located in the BC, Canada were utilized to assess the conditions of a set of sensors in an instrumented pier, using methods developed based on the concepts of the sequential and binary search techniques. Continuous data sets of strain and temperature gauges were filtered and normalized. Defective sensors were detected by pattern matching of simulated and real data, using sensitivity analyses of the developed models.
KeywordsStructural health monitoring Statistical pattern recognition Sensor Strain Temperature Bridge
The work presented here formed a part of the first author’s Master’s thesis at Concordia University, Montreal, under the supervision of the second author. The authors are grateful for Prof. Aftab A. Mufti, President of ISIS Canada Research Network, University of Manitoba, Winnipeg, for providing encouragement and the monitoring data for the Portage Creek Bridge. The support of the Natural Sciences and Engineering Research Council of Canada is also gratefully acknowledged.
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