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Indexed by:期刊论文

Date of Publication:2017-09-01

Journal:JOURNAL OF ENGINEERING MECHANICS

Included Journals:SCIE、EI

Volume:143

Issue:9

ISSN No.:0733-9399

Key Words:Weighted principal-component analysis; Fault sensitivity; Bayesian inference; Contribution analysis; Sensor-fault diagnosis; Structural health monitoring

Abstract:It is essential to diagnose, i.e., detect and isolate, potential sensor faults for structural health monitoring to guarantee reliable condition evaluations. This paper proposes an innovative method called weighted principal-component analysis for sensor-fault detection and isolation. It is first illustrated that the fault sensitivity of each principal direction of traditional principal-component analysis is different from others for the same fault occurring in a certain sensor. Then, a fault-sensitive factor is theoretically derived to quantify the fault sensitivities. Based on that, a weighted fault-detection statistic determined according to the difference in fault sensitivities is developed and shown to have enhanced fault-detection ability. Bayesian inference is used to integrate all the weighted statistics corresponding to all the sensors to quickly judge whether a sensor fault occurred. Meanwhile, contribution analysis is used to establish a fault isolation index to identify the specific faulty sensor. Case studies using numerical simulation and a benchmark model demonstrate that the new proposed method is excellent and superior to the traditional approach. (C) 2017 American Society of Civil Engineers.