Integrated Fault Evaluation Through Fusion Algorithm Supported by Kalman Filter

Abstract

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.

Safety Management Solution for Health Monitoring User Terminals Based on Trusted Computing

“…is would prevent tragedies like major personal accidents [1][2][3][4][5][6]. In recent years, intelligent products capable of actively monitoring health anywhere, anytime have attracted much attention [7][8][9][10][11][12]. ese products collect various human health data via user terminals.…”