Estimation Methods in Training of ANNs for Robust Fault Diagnosis

2014 ◽  
pp. 47-68
Author(s):  
Marcin Mrugalski
Keyword(s):  
Fault Diagnosis ◽  
Estimation Methods

Health Assessment and Fault Diagnosis for the Heat Exchanger of the Aircraft Environmental Control System Based on STF-LR

2015 ◽  
Vol 764-765 ◽  
pp. 294-299
Author(s):  
Jian Ma ◽  
Chen Lu ◽  
Hong Mei Liu
Keyword(s):  
Control System ◽  
Heat Exchanger ◽  
Fault Diagnosis ◽  
Environmental Control ◽  
Fault Simulation ◽  
Health Assessment ◽  
Estimation Methods ◽  
Root Cause ◽  
Degradation Data ◽  
Parameter Estimation Methods

The aircraft environmental control system (ECS) is a critical aircraft system that provides the appropriate environmental conditions to ensure the safe transport of air passengers and equipment. The functionality and reliability of ECS have elicited an increasing amount of attention in recent years. The heat exchanger is a particularly significant component of ECS because its failure reduces the system’s efficiency and can lead to catastrophic consequences. Health assessment and fault diagnosis for the heat exchanger are necessary to perform maintenance and prevent risks in a timely manner. This paper presents fault-related parameter estimation methods based on strong tracking filter (STF) and logistic regression (LR) algorithm for heat exchanger health assessment and root cause classification, respectively. Heat exchanger fault simulation is conducted to generate performance degradation data, through which the proposed methods are validated. Results demonstrate that the proposed methods are capable of providing stable, effective, and accurate heat exchanger health assessment and root cause classification.

scholarly journals Quadrotor Accelerometer and Gyroscope Sensor Fault Diagnosis Using Nonlinear Adaptive Estimation Methods

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Remus Avram ◽  
Xiaodong Zhang ◽  
Jonathan Muse
Keyword(s):  
Fault Diagnosis ◽  
Real Time ◽  
Adaptive Estimation ◽  
State Equation ◽  
Fault Detection And Isolation ◽  
Estimation Methods ◽  
Sensor Fault ◽  
Test Environment ◽  
Estimation Scheme ◽  
Gyroscope Sensor

This paper presents the design, analysis, and real-time experimental evaluation results of a nonlinear sensor fault diagnosis scheme for quadrotor unmanned air vehicles (UAV). The objective is to detect, isolate, and estimate sensor bias faults in accelerometer and gyroscope measurements. Based on the quadrotor dynamics and sensor models under consideration, the effects of sensor faults are represented as virtual actuator faults in the quadrotor state equation. Two nonlinear diagnostic estimators are designed to provide structured residualsfor fault detection and isolation. Additionally, after the fault is detected and isolated, a nonlinear adaptive estimation scheme is employed for estimating the unknown fault magnitude.The proposed fault diagnosis scheme is capable of handling simultaneous faults in the accelerometer and gyroscope measurements. The effectiveness of the fault diagnosis method is demonstrated using an indoor real-time quadrotor UAV test environment.

The Impact of Missing and Error-Prone Auxiliary Information on Sparse-Matrix Sub-Population Parameter Estimates

Methodology ◽  
2015 ◽  
Vol 11 (3) ◽  
pp. 89-99 ◽  
Author(s):  
Leslie Rutkowski ◽  
Yan Zhou
Keyword(s):  
Sparse Matrix ◽  
Small Body ◽  
Auxiliary Information ◽  
Poor Quality ◽  
Quality Data ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Population Parameter ◽  
Conditioning Model ◽  
The Impact

Abstract. Given a consistent interest in comparing achievement across sub-populations in international assessments such as TIMSS, PIRLS, and PISA, it is critical that sub-population achievement is estimated reliably and with sufficient precision. As such, we systematically examine the limitations to current estimation methods used by these programs. Using a simulation study along with empirical results from the 2007 cycle of TIMSS, we show that a combination of missing and misclassified data in the conditioning model induces biases in sub-population achievement estimates, the magnitude and degree to which can be readily explained by data quality. Importantly, estimated biases in sub-population achievement are limited to the conditioning variable with poor-quality data while other sub-population achievement estimates are unaffected. Findings are generally in line with theory on missing and error-prone covariates. The current research adds to a small body of literature that has noted some of the limitations to sub-population estimation.

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