Information Fusion Kalman Filter for Two-Sensor System with Time-Delayed Measurements

Liu, Jinfang; Deng, Zili

doi:10.1016/j.proeng.2012.01.015

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Kalman Filter Equations1

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Cited by 4 publications

(2 citation statements)

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“…In this section a brief mathematical background for KF and data fusion through KF algorithm is presented [36][37][38].…”

Section: Mathematical Backgroundmentioning

confidence: 99%

See 1 more Smart Citation

Integrated Fault Evaluation Through Fusion Algorithm Supported by Kalman Filter

Mousavi¹,

Kara²,

Şeker³

2020

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An Integrated Fault Evaluation (IFE) process is proposed in this study. It includes Sensor Validation (SV), Fault Detection (FD) and Fault Source Identification (FSI). The proposed algorithm employs data fusion algorithm enhanced by Kalman filter (KF). As the case study, vibration signals representing different aging states of an induction motor are used. The vibration data collected from two identical sensors with different measurement and process noises are achieved. Through the statistical and frequency domain characteristics, IFE is realized. The most prominent contribution of the study is the capability of distinction between the aging of the system and the process problems. For this aim, a rate representing the healthiness, which can discern the impact of the process noise and system aging, is calculated.

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“…In this section a brief mathematical background for KF and data fusion through KF algorithm is presented [36][37][38].…”

Section: Mathematical Backgroundmentioning

confidence: 99%

“…The KF algorithm contains the prediction stage and the measurement updates. The equations of these stages are presented as below [36][37][38].…”

Section: Kalman Filter Equationsmentioning

confidence: 99%