scholarly journals Design Technology Research of Aircraft Engine Health Management (EHM) Technologies

2021 ◽  
Vol 06 (01) ◽  
pp. 9-23
Author(s):  
Wessam Abousada
Keyword(s):  
Health Management ◽  
Aircraft Engine ◽  
Technology Research ◽  
Design Technology

scholarly journals NASA aviation safety program: Aircraft Engine Health Management Data Mining Tools roadmap

2000 ◽  
Author(s):  
Jonathan S. Litt ◽  
Donald L. Simon ◽  
Claudia Meyer ◽  
Hans DePold ◽  
J. R. Curtiss ◽  
...  
Keyword(s):  
Data Mining ◽  
Health Management ◽  
Aircraft Engine ◽  
Aviation Safety ◽  
Safety Program ◽  
Mining Tools

Theory and Technology Research on the Software Health Management

2014 ◽  
Vol 670-671 ◽  
pp. 1503-1506
Author(s):  
Hui Qi Zhang ◽  
Qiu Ying Li ◽  
Hang Chao You
Keyword(s):  
Adverse Events ◽  
Health Monitoring ◽  
Health Management ◽  
External Environment ◽  
Software System ◽  
Technology Research ◽  
Health Concept ◽  
Software Health Management ◽  
Hardware Failures ◽  
Different Sources

The software health management (SWHM) is a new field, which mainly deals with some critical need to detect, diagnose, predict and mitigate adverse events caused by software related faults and failures. These faults come from different sources such as code errors, unpredicted faults, hardware failures or some problematic interaction with the external environment. This article discusses the latest development and research of software health concept and techniques. And the article summarizes the problem of current research, and proposes the developing trend. At last, we explain the process of software health monitoring taking the service software system as example.

Information Fusion Strategy for Aircraft Engine Health Management

2007 ◽  
Author(s):  
LiJie Yu ◽  
Dan Cleary ◽  
Mark Osborn ◽  
Vrinda Rajiv
Keyword(s):  
Intelligent Systems ◽  
Health Management ◽  
A Priori ◽  
Aircraft Engine ◽  
Fault Isolation ◽  
Diagnostic Process ◽  
Diagnostic Model ◽  
Fusion Model ◽  
Decision Module ◽  
Service Disruption

Modern aircraft engines are equipped with sophisticated sensing instruments to enable proactive condition monitoring and effective health management capability. Development of intelligent systems that efficiently process sensor and operational data both onboard and off-board, to provide maintenance personnel with timely decision support, is the key to minimize flight service disruption and reduce engine ownership cost. The goal of this research is to develop a practical approach and strategy to leverage various available information sources and modeling techniques to streamline the engine health management process and maximize system accuracy and efficiency. This paper demonstrates a flexible fusion architecture that encapsulates the key elements of the engine monitoring and diagnostic process, i.e., sensor trend analysis module for anomaly detection, feature selection and fault isolation module for root cause identification, a decision module for diagnostic model fusion and action determination, and finally, a feedback module for knowledge validation and continuous learning. At the core of this engine health management system is a diagnostic fusion model designed around a common fault hierarchy which captures both a priori probabilities and interactions among various engine faults isolated by different classification models. The fusion model will resolve conflicting assessments from individual diagnostic models and provide a more accurate and comprehensive engine state estimate.

Combining Lead Functions and Logistic Regression for Predicting Failures on an Aircraft Engine

2008 ◽  
Author(s):  
David J. Bryg ◽  
George Mink ◽  
Link C. Jaw
Keyword(s):  
Logistic Regression ◽  
Health Management ◽  
Roc Curves ◽  
Aircraft Engine ◽  
Management Systems ◽  
Lead Times ◽  
True Negative ◽  
Additional Measure ◽  
Seamless Integration ◽  
The Difference

The increasing demand for performance and durability of advanced aerospace systems has increased the need for health management of these systems. Effective health management involves seamless integration of failure diagnostics, failure prediction, part life estimation, and maintenance logistics. These capabilities have only partially been implemented in current health management systems. Hence the effectiveness of current management systems has not achieved its potential. To achieve the goal of effective prognostic and health management (PHM), promising technologies from various disciplines must be integrated. One of these technologies is logistic regression. In this method, aircraft engine takeoff data is combined with control system fault information and by introducing lead times prior to the fault. Lead times of 1, 7, 14, and 30 days were analyzed using logistic regression on the difference from expected thermodynamic values. The resulting equations give probability of failure over time. An example using real engine data from GE-F414 engines to predict engine stall and anti-ice valve failures are presented. The results show good predictability of these events between a week and a month in advance. For example, for the event of an imminent anti-ice valve failure, the true-negative fraction was 99.6% and the positive-predictive value was 93.1%. This methodology can be combined with an engine health monitoring (EHM) system to provide prognostic failure predictions. Receiver Operator Characteristic (ROC) curves were evaluated as an additional measure of the quality of the predictions. These ROC curves show that there is prognostic value with this approach. This methodology can be updated and refined with additional data. As the results get more refined, the reliability of the fleet can increase, costs can be reduced, and safety increased.

Aircraft engine health management via stochastic modelling of flight data interrelations

2012 ◽  
Vol 16 (1) ◽  
pp. 70-81 ◽  
Author(s):  
D. Dimogianopoulos ◽  
J. Hios ◽  
S. Fassois
Keyword(s):  
Health Management ◽  
Stochastic Modelling ◽  
Aircraft Engine ◽  
Flight Data

scholarly journals A Unified Nonlinear Adaptive Approach for Detection and Isolation of Engine Faults

2010 ◽  
Author(s):  
Liang Tang ◽  
Xiaodong Zhang ◽  
Jonathan A. DeCastro ◽  
Luis Farfan-Ramos ◽  
Donald L. Simon
Keyword(s):  
Health Management ◽  
System Development ◽  
Aircraft Engine ◽  
Fault Detection And Isolation ◽  
Specific Response ◽  
Sensor Faults ◽  
Actuator Faults ◽  
Unified Framework ◽  
Adaptive Estimator ◽  
Component Faults

A challenging problem in aircraft engine health management (EHM) system development is to detect and isolate faults in system components (i.e., compressor, turbine), actuators, and sensors. Existing nonlinear EHM methods often deal with component faults, actuator faults, and sensor faults separately, which may potentially lead to incorrect diagnostic decisions and unnecessary maintenance. Therefore, it would be ideal to address sensor faults, actuator faults, and component faults under one unified framework. This paper presents a systematic and unified nonlinear adaptive framework for detecting and isolating sensor faults, actuator faults, and component faults for aircraft engines. The fault detection and isolation (FDI) architecture consists of a parallel bank of nonlinear adaptive estimators. Adaptive thresholds are appropriately designed such that, in the presence of a particular fault, all components of the residual generated by the adaptive estimator corresponding to the actual fault type remain below their thresholds. If the faults are sufficiently different, then at least one component of the residual generated by each remaining adaptive estimator should exceed its threshold. Therefore, based on the specific response of the residuals, sensor faults, actuator faults, and component faults can be isolated. The effectiveness of the approach was evaluated using the NASA C-MAPSS turbofan engine model, and simulation results are presented.

scholarly journals Integration of On-Line and Off-Line Diagnostic Algorithms for Aircraft Engine Health Management

2007 ◽  
Author(s):  
Takahisa Kobayashi ◽  
Donald L. Simon
Keyword(s):  
Gas Turbine ◽  
Health Management ◽  
Diagnostic Algorithm ◽  
Aircraft Engine ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Simulation Environment ◽  
Diagnostic Algorithms ◽  
Engine Model ◽  
On Line

This paper investigates the integration of on-line and off-line diagnostic algorithms for aircraft gas turbine engines. The on-line diagnostic algorithm is designed for in-flight fault detection. It continuously monitors engine outputs for anomalous signatures induced by faults. The off-line diagnostic algorithm is designed to track engine health degradation over the lifetime of an engine. It estimates engine health degradation periodically over the course of the engine’s life. The estimate generated by the off-line algorithm is used to “update” the on-line algorithm. Through this integration, the on-line algorithm becomes aware of engine health degradation, and its effectiveness to detect faults can be maintained while the engine continues to degrade. The benefit of this integration is investigated in a simulation environment using a nonlinear engine model.

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