Release Time:2019-03-11  Hits:

Indexed by: Journal Article

Date of Publication: 2017-09-01

Journal: JOURNAL OF ENGINEERING MECHANICS

Included Journals: EI、SCIE

Volume: 143

Issue: 9

ISSN: 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.