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论文类型：期刊论文

发表时间：2017-09-01

发表刊物：JOURNAL OF ENGINEERING MECHANICS

收录刊物：SCIE、EI

卷号：143

期号：9

ISSN号：0733-9399

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

摘要：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.