Hybrid Kalman Filter Approach for Aircraft Engine In-Flight Diagnostics: Sensor Fault Detection Case

2006 ◽  
Author(s):  
Takahisa Kobayashi ◽  
Donald L. Simon
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Linear Models ◽  
Detection System ◽  
Piecewise Linear ◽  
Diagnostic System ◽  
Aircraft Engine ◽  
Operating Conditions ◽  
Sensor Fault ◽  
Sensor Fault Detection

In this paper, a diagnostic system based on a uniquely structured Kalman filter is developed for its application to inflight fault detection of aircraft engine sensors. The Kalman filter is a hybrid of a nonlinear on-board engine model (OBEM) and piecewise linear models. The utilization of the nonlinear OBEM allows the reference health baseline of the diagnostic system to be updated, through a relatively simple process, to the health condition of degraded engines. Through this health baseline update, the diagnostic effectiveness of the in-flight sensor fault detection system is maintained as the health of the engine degrades over time. The performance of the sensor fault detection system is evaluated in a simulation environment at several operating conditions during the cruise phase of flight.

Hybrid Kalman Filter Approach for Aircraft Engine In-Flight Diagnostics: Sensor Fault Detection Case

2006 ◽  
Vol 129 (3) ◽  
pp. 746-754 ◽  
Author(s):  
Takahisa Kobayashi ◽  
Donald L. Simon
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Linear Models ◽  
Detection System ◽  
Piecewise Linear ◽  
Diagnostic System ◽  
Aircraft Engine ◽  
Operating Conditions ◽  
Sensor Fault ◽  
Sensor Fault Detection

In this paper, a diagnostic system based on a uniquely structured Kalman filter is developed for its application to in-flight fault detection of aircraft engine sensors. The Kalman filter is a hybrid of a nonlinear on-board engine model (OBEM) and piecewise linear models. The utilization of the nonlinear OBEM allows the reference health baseline of the diagnostic system to be updated, through a relatively simple process, to the health condition of degraded engines. Through this health baseline update, the diagnostic effectiveness of the in-flight sensor fault detection system is maintained as the health of the engine degrades over time. The performance of the sensor fault detection system is evaluated in a simulation environment at several operating conditions during the cruise phase of flight.

Sensor fault detection, isolation and system reconfiguration based on extended Kalman filter for induction motor drives

2013 ◽  
Vol 7 (7) ◽  
pp. 607-617 ◽  
Author(s):  
Xinan Zhang ◽  
Gilbert Foo ◽  
Mahinda Don Vilathgamuwa ◽  
King Jet Tseng ◽  
Bikramjit Singh Bhangu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Induction Motor ◽  
Extended Kalman Filter ◽  
Motor Drives ◽  
Sensor Fault ◽  
Induction Motor Drives ◽  
System Reconfiguration ◽  
Sensor Fault Detection

scholarly journals Aircraft Engine On-Line Diagnostics Through Dual-Channel Sensor Measurements: Development of a Baseline System

2008 ◽  
Author(s):  
Takahisa Kobayashi ◽  
Donald L. Simon
Keyword(s):  
Fault Detection ◽  
Aircraft Engine ◽  
Fault Detection And Isolation ◽  
Channel Measurements ◽  
Sensor Fault ◽  
Engine Model ◽  
Dual Channel ◽  
Sensor Fault Detection ◽  
Baseline System ◽  
On Line

In this paper, a baseline system which utilizes dual-channel sensor measurements for aircraft engine on-line diagnostics is developed. This system is composed of a linear on-board engine model (LOBEM) and fault detection and isolation (FDI) logic. The LOBEM provides the analytical third channel against which the dual-channel measurements are compared. When the discrepancy among the triplex channels exceeds a tolerance level, the FDI logic determines the cause of the discrepancy. Through this approach, the baseline system achieves the following objectives: 1) anomaly detection, 2) component fault detection, and 3) sensor fault detection and isolation. The performance of the baseline system is evaluated in a simulation environment using faults in sensors and components.

scholarly journals Aircraft Engine On-Line Diagnostics Through Dual-Channel Sensor Measurements: Development of an Enhanced System

2008 ◽  
Author(s):  
Takahisa Kobayashi ◽  
Donald L. Simon
Keyword(s):  
Fault Detection ◽  
Diagnostic System ◽  
Aircraft Engine ◽  
Fault Detection And Isolation ◽  
Channel Measurements ◽  
Engine Model ◽  
Dual Channel ◽  
Sensor Fault Detection ◽  
Baseline System ◽  
On Line

In this paper, an enhanced on-line diagnostic system which utilizes dual-channel sensor measurements is developed for the aircraft engine application. The enhanced system is composed of a nonlinear on-board engine model (NOBEM), the hybrid Kalman filter (HKF) algorithm, and fault detection and isolation (FDI) logic. The NOBEM provides the analytical third channel against which the dual-channel measurements are compared. The NOBEM is further utilized as part of the HKF algorithm which estimates measured engine parameters. Engine parameters obtained from the dual-channel measurements, the NOBEM, and the HKF are compared against each other. When the discrepancy among the signals exceeds a tolerance level, the FDI logic determines the cause of discrepancy. Through this approach, the enhanced system achieves the following objectives: 1) anomaly detection, 2) component fault detection, and 3) sensor fault detection and isolation. The performance of the enhanced system is evaluated in a simulation environment using faults in sensors and components, and it is compared to an existing baseline system.

ON-LINE SENSOR FAULT DETECTION SYSTEM FOR A SMART WHEELCHAIR

2006 ◽  
Vol 39 (15) ◽  
pp. 91-96
Author(s):  
Gianluca Ippoliti ◽  
Sauro Longhi ◽  
Andrea Monteriù
Keyword(s):  
Fault Detection ◽  
Detection System ◽  
Sensor Fault ◽  
Sensor Fault Detection ◽  
On Line ◽  
Smart Wheelchair
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