An Integrated Architecture for Aircraft Engine Performance Monitoring and Fault Diagnostics: Engine Test Results

This paper presents a model-based architecture for performance trend monitoring and gas path fault diagnostics designed for analyzing streaming transient aircraft engine measurement data. The technique analyzes residuals between sensed engine outputs and model predicted outputs for fault detection and isolation purposes. Diagnostic results from the application of the approach to test data acquired from an aircraft turbofan engine are presented. The approach is found to avoid false alarms when presented nominal fault-free data. Additionally, the approach is found to successfully detect and isolate gas path seeded-faults under steady-state operating scenarios although some fault misclassifications are noted during engine transients. Recommendations for follow-on maturation and evaluation of the technique are also presented

On-Line Transient Engine Diagnostics in a Kalman Filtering Framework, ASME June 6-9, 2005 Dow NOTE: Approved for public release by NAVAIR, Joint Strike Fight

ABSTRACT A common assumption made in the performance assessment of a turbine engine for aircraft propulsion consists in restricting the data processing to steady-state data. This especially holds for on-board performance monitoring of a commercial aircraft which spends up to 90% of the time in cruise flight where such conditions are satisfied. The present contribution is intended to investigate the ability of a diagnosis method to process unsteady data rather than steady-state data. The aim of this unsteady approach is to strongly reduce the time and the efforts spent to obtain a reliable diagnosis. In order to assess the improvements in terms of diagnosis efficiency and engine operability, the resulting diagnostic method is tested for different degradations that can be expected on commercial turbofans. The results are also compared to those obtained from cruise flight steady-state data in order to balance the two approaches

A way to deal with model-plant mismatch for a reliable diagnosis in transient operation

Least-squares health parameter identification techniques, such as the Kalman filter, have been extensively used to solve diagnosis problems. Indeed, such methods give a good estimate provided that the discrepancies between the model prediction and the measurements are Zero-mean, white, Gaussian random variables. In a turbine engine diagnosis, however this assumption does not always hold due to the presence of biases in the model. This is especially true for a transient operation. As a result, the estimated parameters tend to diverge from their actual values, which strongly degrades the diagnosis. The purpose of this contribution is to present a Kalman filter diagnosis tool where the model biases are treated as an additional random measurement error. The new methodology is tested on simulated transient data representative of a current turbofan engine configuration. While relatively simple to implement, the newly developed diagnosis tool exhibits a much better accuracy than the original Kalman filter in the presence of model biases

Combining classification techniques with Kalman filters for aircraft engine diagnostics

A diagnostic method consisting of a combination of Kalman filters and Bayesian Belief Network (BBN) is presented. A soft-constrained Kalman filter uses a priori information derived by a BBN at each time step, to derive estimations of the unknown health parameters. The resulting algorithm hers improved identification capability in comparison to the stand-alone Kalman filter. The paper focuses on a way of combining the information produced by the BBN with the Kalman filter. An extensive set of fault cases is used to test the method on a typical civil turbofan layout. The effectiveness of the method is thus demonstrated, and its advantages over individual constituent methods are presented

Simulation and optimization of a CHP biomass plant and district heating network

Biomass Combined Heat and Power (CHP) plants connected to district heating (DH) networks are recognized nowadays as a very good opportunity to increase the share of renewable sources into energy systems. However, as CHP plants are not optimized for electricity production, their operation is profitable only if a sufficient heat demand is available throughout the year. Most of the time, pre-feasibility studies are based on peak power demand and business plans only assume monthly or yearly consumption data. This approach usually turns out to overestimate the number of operating hours or oversize the plant capacity. This contribution presents a methodology intended to be simple and effective that provides accurate estimations of economical, environmental and energetic performances of CHP plants connected to district heating networks. A quasi-steady state simulation model of a CHP plant combined with a simulation model of the district heating network installed on the Campus of the University in Liège (Belgium) is used as an application framework to demonstrate the effectiveness of the selected approach. Based on the developed model and actual consumption data, several scenarios for energy savings are considered and ranked. The potential energy savings and resulting energy costs are estimated enabling more general conclusions to be drawn on the opportunity of using district heating networks in urban districts for Western Europe countries