Engine Health Monitoring Research Articles

Gas Path Health Monitoring for a Turbofan Engine Based on a Nonlinear Filtering Approach

Different approaches for gas path performance estimation of dynamic systems are commonly used, the most common being the variants of the Kalman filter. The extended Kalman filter (EKF) method is a popular approach for nonlinear systems which combines the traditional Kalman filtering and linearization techniques to effectively deal with weakly nonlinear and non-Gaussian problems. Its mathematical formulation is based on the assumption that the probability density function (PDF) of the state vector can be approximated to be Gaussian. Recent investigations have focused on the particle filter (PF) based on Monte Carlo sampling algorithms for tackling strong nonlinear and non-Gaussian models. Considering the aircraft engine is a complicated machine, operating under a harsh environment, and polluted by complex noises, the PF might be an available way to monitor gas path health for aircraft engines. Up to this point in time a number of Kalman filtering approaches have been used for aircraft turbofan engine gas path health estimation, but the particle filters have not been used for this purpose and a systematic comparison has not been published. This paper presents gas path health monitoring based on the PF and the constrained extend Kalman particle filter (cEKPF), and then compares the estimation accuracy and computational effort of these filters to the EKF for aircraft engine performance estimation under rapid faults and general deterioration. Finally, the effects of the constraint mechanism and particle number on the cEKPF are discussed. We show in this paper that the cEKPF outperforms the EKF, PF and EKPF, and conclude that the cEKPF is the best choice for turbofan engine health monitoring.

New Sensors and Techniques for the Structural Health Monitoring of Propulsion Systems

The ability to monitor the structural health of the rotating components, especially in the hot sections of turbine engines, is of major interest to aero community in improving engine safety and reliability. The use of instrumentation for these applications remains very challenging. It requires sensors and techniques that are highly accurate, are able to operate in a high temperature environment, and can detect minute changes and hidden flaws before catastrophic events occur. The National Aeronautics and Space Administration (NASA), through the Aviation Safety Program (AVSP), has taken a lead role in the development of new sensor technologies and techniques for the in situ structural health monitoring of gas turbine engines. This paper presents a summary of key results and findings obtained from three different structural health monitoring approaches that have been investigated. This includes evaluating the performance of a novel microwave blade tip clearance sensor; a vibration based crack detection technique using an externally mounted capacitive blade tip clearance sensor; and lastly the results of using data driven anomaly detection algorithms for detecting cracks in a rotating disk.

A Cost-Benefit Approach to Evaluating Engine Health Monitoring Systems


 
 
 Condition-based maintenance (CBM) enables fleet-level decisions that increase readiness, increase time between overhaul (TBO) and reduce inspections. Since engines account for a significant portion in overall maintenance cost drivers, detection of incipient faults is an important element of the overall CBM equation. The last few years have seen significant progress in design, development and deployment of engine health monitoring. In order for such potential health monitoring solutions to be operationally viable, they must integrate with existing engine designs and maintenance processes. That is, technical factors must be balanced against economic and operational benefits.
 A Cost-Benefit Analysis (CBA) is used to provide a comparison of alternative solutions that decision-makers can use to identify the most cost-effective approach to CBM. In this paper we describe our approach to developing the underlying value capture expressions for monetizing cost and benefits. We illustrate the approach to evaluate two options for mechanical components health monitoring techniques for a gas turbine engine. We conclude the paper with how CBA summary results can be presented to a decision maker.
 
 

In-Flight Isolation of Degraded Engine Components by Shape Comparison of Transient Outputs

A direct method of aircraft engine health monitoring is introduced that can isolate a degraded engine component in-flight. The method utilizes continuous wavelet transformations of transient engine sensory data to represent their shape attributes in the time scale domain. This enables contrasting the shapes of the current engine outputs with those previously collected from the engine. Continuous wavelet transforms also provide enhanced delineation of the engine transients in the time scale domain. This enables identification of minute differences between the outputs affected by component degradations and between the sensitivities of modeled outputs with respect to the health parameters or components. The presence of these differences is used in this method as evidence of degradation effects on the outputs and/or parameters or components effects on the outputs. The effectiveness of the proposed method is evaluated in engine simulations. The results indicate that with the suite of outputs currently available on-board 70% to 96% of the degraded components simulated can be isolated for new and older engines.

Wireless Communication Networks for Gas Turbine Engine Testing

A new trend in the field of Aeronautical Engine Health Monitoring is the implementation of wireless sensor networks (WSNs) for data acquisition and condition monitoring to partially replace heavy and complex wiring harnesses, which limit the versatility of the monitoring process as well as creating practical deployment issues. Augmenting wired with wireless technologies will fuel opportunities for reduced cabling, faster sensor and network deployment, increased data acquisition flexibility, and reduced cable maintenance costs. However, embedding wireless technology into an aero engine (even in the ground testing application considered here) presents some very significant challenges, for example, a harsh environment with a complex RF transmission channel, high sensor density, and high data rate. In this paper we discuss the results of the Wireless Data Acquisition in Gas Turbine Engine Testing (WIDAGATE) project, which aimed to design and simulate such a network to estimate network performance and derisk the wireless techniques before the deployment.

A Study on Engine Health Monitoring in the Frequency Domain

Most of the techniques developed to date for module performance analysis rely on steady-state measurements from a single operating point to evaluate the level of deterioration of an engine. One of the major difficulties associated with this estimation problem comes from its underdetermined nature. It results from the fact that the number of health parameters exceeds the number of available sensors. Among the panel of remedies to this issue, a few authors have investigated the potential of using data collected during a transient operation of the engine. A major outcome of these studies is an improvement in the assessed health condition. The present study proposes a framework that formalizes this observation for a given class of input signals. The analysis is performed in the frequency domain, following the lines of system identification theory. More specifically, the mean-squared estimation error is shown to drastically decrease when using transient input signals. This study is conducted with an engine model representative of a commercial turbofan.

Application of wireless sensor networks to aircraft control and health management systems

Use of fly-by-wire technology for aircraft flight controls have resulted in an improved performance and reliability along with achieving reduction in control system weight. Implementation of full authority digital engine control has also resulted in more intelligent, reliable, light-weight aircraft engine control systems. Greater reduction in weight can be achieved by replacing the wire harness with a wireless communication network. The first step towards fly-by-wireless control systems is likely to be the introduction of wireless sensor networks (WSNs). WSNs are already finding a variety of applications for both safety-critical and nonsafety critical distributed systems. Some of the many potential benefits of using WSN for aircraft systems include weight reduction, ease of maintenance and an increased monitoring capability. This paper discusses the application of WSN for several aircraft systems such as distributed aircraft engine control, aircraft flight control, aircraft engine and structural health monitoring systems. A brief description of each system is presented along with a discussion on the technological challenges. Future research directions for application of WSN in aircraft systems are also discussed.

Assessment of an Anomaly Detector for Jet Engine Health Monitoring

The goal of module performance analysis is to reliably assess the health of the main components of an aircraft engine. A predictive maintenance strategy can leverage this information to increase operability and safety as well as to reduce costs. Degradation undergone by an engine can be divided into gradual deterioration and accidental events. Kalman filters have proven very efficient at tracking progressive deterioration but are poor performers in the face of abrupt events. Adaptive estimation is considered as an appropriate solution to this deficiency. This paper reports the evaluation of the detection capability of an adaptive diagnosis tool on the basis of simulated scenarios that may be encountered during the operation of a commercial turbofan engine. The diagnosis tool combines a Kalman filter and a secondary system that monitors the residuals. This auxiliary component implements a generalised likelihood ratio test in order to detect abrupt events.

Structural Health Management in the NAVY

There is a critical need for integrated system health management (ISHM) approaches to asset maintenance. Ideally, ISHM methodologies would track the system usage and the associated loads, monitor the system degradation and materials state, monitor relevant environmental parameters and their effects on system degradation, detect insipient system damage, diagnose failure mode, predict future system performance, and recommend maintenance actions. Even though there has been considerable progress in many subareas of ISHM over the past years, there is still ample room for future improvements in all technological aspects affecting ISHM. In fact, progress in ISHM has not been uniform. Some subsystems have experienced a far greater degree of development than others. For example, engine and machinery health monitoring and diagnostics, due to its criticality, has evolved at a faster pace than structural health monitoring. This article will review some of the aspects that need to be addressed in order to make structural health monitoring (SHM) of military systems a reality in the near future.

Jet Engine Health Signal Denoising Using Optimally Weighted Recursive Median Filters

The removal of noise and outliers from health signals is an important problem in jet engine health monitoring. Typically, health signals are time series of damage indicators, which can be sensor measurements or features derived from such measurements. Sharp or sudden changes in health signals can represent abrupt faults and long term deterioration in the system is typical of gradual faults. Simple linear filters tend to smooth out the sharp trend shifts in jet engine signals and are also not good for outlier removal. We propose new optimally designed nonlinear weighted recursive median filters for noise removal from typical health signals of jet engines. Signals for abrupt and gradual faults and with transient data are considered. Numerical results are obtained for a jet engine and show that preprocessing of health signals using the proposed filter significantly removes Gaussian noise and outliers and could therefore greatly improve the accuracy of diagnostic systems.

헬리콥터 터보축 엔진의 온라인 상태진단 프로그램 연구

본 연구는 헬리콥터용 터보축엔진의 온라인 상태진단을 위해 퍼지-신경망 알고리즘을 제안하였고 GUI 형태의 SIMULINK프로그램으로 개발하였다. 진단 프로그램 개발을 위해 look-up 테이블 형식 기본 성능 모듈로 계산시간을 줄였고 실시간 성능 데이터를 획득하기위해 신호 생성 모듈을 사용하였다. 이 프로그램은 계측성능상태를 모니터링하기위한 온라인 상태모니터링 프로그램과 계측데이터와 퍼지를 이용한 정성적인 상태진단과 신경 회로망을 이용한 정량적인 상태진단으로 이루어진다. 제안된 온라인 진단 프로그램은 헬리콥터엔진의 상태모니터링에 적용 가능여부를 확인하기 위하여 터보샤프트 엔진을 대상으로 검증하였다. This work proposes a GUI-type on-line diagnostic program using SIMULINK and Fuzzy-Neuro algorithms for a helicopter turboshaft engine. During development of the diagnostic program, a look-up table type base performance module for reducing computer calculating time and a signal generation module for simulating real time performance data are used. This program is composed of the on-line condition monitoring program to monitor on-line measuring performance condition, the fuzzy inference system to isolate the faults from measuring data and the neural network to quantify the isolated faults. The reliability and capability of the proposed on-line diagnostic program were confirmed through application to the helicopter engine health monitoring.

Development of On-line Performance Diagnostic Program of a Helicopter Turboshaft Engine

Gas turbine performance diagnostics is a method for detecting, isolating and quantifying faults in gas turbine gas path components. On-line precise fault diagnosis can promote greatly reliability and availability of gas turbine in real time operation. This work proposes a GUI-type on-line diagnostic program using SIMULINK and Fuzzy-Neuro algorithms for a helicopter turboshaft engine. During development of the diagnostic program, a look-up table type base performance module are used for reducing computer calculating time and a signal generation module for simulating real time performance data. This program is composed of the on-line condition monitoring program to monitor on-line measuring performance condition, the fuzzy inference system to isolate the faults from measuring data and the neural network to quantify the isolated faults. Evaluation of the proposed on-line diagnostic program is performed through application to the helicopter engine health monitoring.

Steady-State and Transient Performance Modeling of Smart UAV Propulsion System Using SIMULINK

Because an aircraft gas turbine operates under various flight conditions that change with altitude, flight velocity, and ambient temperature, the performance estimation that considers the flight conditions must be known before developing or operating the gas turbine. More so, for the unmanned aerial vehicle (UAV) where the engine is activated by an onboard engine controller in emergencies, the precise performance model including the estimated steady-state and transient performance data should be provided to the engine control system and the engine health monitoring system. In this study, a graphic user interface (GUI) type steady-state and transient performance simulation model of the PW206C turboshaft engine that was adopted for use in the Smart UAV was developed using SIMULINK for the performance analysis. For the simulation model, first the component maps including the compressor, gas generator turbine, and power turbine were inversely generated from the manufacturer’s limited performance deck data by the hybrid method. For the work and mass flow matching between components of the steady-state simulation, the state-flow library of SIMULINK was applied. The proposed steady-state performance model can simulate off-design point performance at various flight conditions and part loads, and in order to evaluate the steady-state performance model their simulation results were compared with the manufacturer’s performance deck data. According to comparison results, it was confirmed that the steady-state model agreed well with the deck data within 3% in all flight envelopes. In the transient performance simulation model, the continuity of mass flow (CMF) method was used, and the rotational speed change was calculated by integrating the excess torque due to the transient fuel flow change using the Runge–Kutta method. In this transient performance simulation, the turbine overshoot was predicted.

On-Condition Maintenance for Nonmodular Jet Engines: An Experience

Using updated knowledge and gained experience in engine control and maintenance, a specific on-condition maintenance concept of RD-33 engines installed on MiG-29s was developed. The engines had several built-in limitations: number of starts, number of hours at the maximum power and reheat, number of hours at the special regime of elevated temperatures, and time between overhauls (TBOs), that is, number of flight hours. During field data collection and analysis, it was found that engines worked with different working loads and different levels of life consumption. Hence, it was concluded that the limitation of TBO, expressed in terms of flight hours, do not represent actual engine health condition and that a new way of monitoring actual load needs to be introduced. An analysis of all flight profiles was carried out and a specific relation between flight hours and total accumulated cycles (TACs) was established. For this purpose, a distributed expert system in relation-operation unit—Air Force Technical Institute—overhaul depot was introduced. Each of the three participants has its own level of responsibility in the engine health monitoring, engine maintenance, and engine health condition decision-making process. Nondestructive inspection, remote visual inspection, spectral oil analysis, fault tolerant control techniques of hot engine parts, engine electronic control unit, airplane information-display system, engine performance trend monitoring, vibration monitoring, and postflight data analysis play key roles in the concept. It has been applied in practice since 1994; all faults were discovered right in time, and there were not any critical situations in flight. Detected faults were isolated and assessed for severity, so that the remaining useful life could be estimated. The original TBO was safely extended on the basis of TAC of up to more than 50% of the originally prescribed TBO hours, while maintaining the same safe margin.

Coupling principal component analysis and Kalman filtering algorithms for on-line aircraft engine diagnostics

Engine health monitoring has been an area of intensive research for more than three decades. Numerous methods have been developed with the goal of performing an accurate assessment of the engine condition. It is generally accepted that a practical implementation of a monitoring tool will rely on a combination of several techniques. In this framework, the present contribution proposes an original approach for coupling two diagnostic tools in order to enhance the capability of an engine health monitoring system. One tool is based on a principal component analysis scheme and the other is based on a Kalman filter technique. The three methodologies are compared and the benefit of the combined tool is demonstrated on simulated fault cases which can be expected in a commercial turbofan layout.

Fuzzy logic‐based automated engine health monitoring for commercial aircraft

PurposeThe purpose of this paper is to develop an automated engine health monitoring system (AEHMS) for commercial aircraft.Design/methodology/approachThe AEHMS is developed by using fuzzy logic. The input of the fuzzy logic is engine performance parameters gathered from aircraft for every flight during cruise. The fuzzy rule inference system for different engine faults is based on expert knowledge and real life data in the Turkish Airlines fleet. The very smallest is used for defuzzification, since it provides a more meaningful result than others. The complete loop of engine health monitoring (EHM) is automatically performed by the programs and Fuzzy Logic Toolbox in MATLAB. The system produces output values between 0 – faulty and 1 – not faulty for every fault or deterioration on a time series. The program triggers an alert if any output exceeds a specified value. Finally, the method is utilized for monitoring the engines in the Turkish Airlines fleet.FindingsHealth monitoring has been a very popular subject to increase aircraft availability with the minimum maintenance cost. Fuzzy logic is a very useful method for automated health monitoring strategies.Research limitations/implicationsIt does not provide long‐term engine maintenance decisions such as scheduling overhaul times, predicting the remaining life of the engine components.Originality/valueThe paper provides a robust method for EHM with the application to real aircraft data. The AEHMS can greatly simplify the EHM system for airlines and minimizes its drawbacks, such as extra labor hours, human error and requirement for engineering expertise.

The Fisher Information Matrix as a Relevant Tool for Sensor Selection in Engine Health Monitoring

Engine health monitoring has been an area of intensive research for many years. Numerous methods have been developed with the goal of determining a faithful picture of the engine condition. On the other hand, the issue of sensor selection allowing an efficient diagnosis has received less attention from the community. The present contribution revisits the problem of sensor selection for engine performance monitoring within the scope of information theory. To this end, a metric that integrates the essential elements of the sensor selection problem is defined from the Fisher information matrix. An example application consisting in a commercial turbofan engine illustrates the enhancement that can be expected from a wise selection of the sensor set.

Optimization of Sensor Locations and Sensitivity Analysis for Engine Health Monitoring Using Minimum Interference Algorithms

The global optimization of sensor locations and a sensitivity analysis based on the minimization of interferences due to wireless communications between sensors are studied in the presence of additive white Gaussian noise (AWGN). We used a Gram matrix approach for robust determination of sensor locations by minimizing the interferences (maximizing the signal strength) among sensors for engine health monitoring systems. In order to solve the problem of optimum placement, an iterative algorithm for maximizing the determinant of the Gram matrix is proposed and implemented. The sensitivity criterion proposed in this paper is the spectral number of the Frobenius norm of the Gram matrix associated with sensor readings. We derived the necessary conditions under which the number of sensors and the optimal sensor locations will remain unchanged when the data measured for sensitivity analysis is affected by AWGN. Our theoretical results are verified by simulations providing details concerning numerical implementations.

Static and dynamic novelty detection methods for jet engine health monitoring

Novelty detection requires models of normality to be learnt from training data known to be normal. The first model considered in this paper is a static model trained to detect novel events associated with changes in the vibration spectra recorded from a jet engine. We describe how the distribution of energy across the harmonics of a rotating shaft can be learnt by a support vector machine model of normality. The second model is a dynamic model partially learnt from data using an expectation-maximization-based method. This model uses a Kalman filter to fuse performance data in order to characterize normal engine behaviour. Deviations from normal operation are detected using the normalized innovations squared from the Kalman filter.

Decision support system on the grid

Aero-engines are extremely reliable machines and operational failures are rare. However, currently great effort is being put into reducing the number of in-flight engine shutdowns, aborted take-offs and flight delays through the use of advanced engine health monitoring technology. This represents a benefit to society through reduced delays, reduced anxiety and reduced cost of ownership of aircraft. This is reflected in a change of emphasis within aero-engine companies where, instead of selling engines to customers, there is a fundamental shift to adoption of power-by-the-hour contracts. In these contracts, airlines make fixed regular payments based on the hours flown and the engine manufacturer retains responsibility for maintaining the engine. To support this new approach, improvements in in-flight monitoring of engines are being introduced with the collection of much more detailed data on the operation of the engine. At the same time advances have been made in Internet technologies providing a worldwide network of computers that can be used to access and process that data. The explosion of available knowledge within those large datasets also presents its own problems and here it is necessary to work on advanced decision support systems to identify the useful information in the data and provide knowledge-based advice between individual aircraft, airline repair and overhaul bases, world-wide data warehouses and the engine manufacturer. This paper presents a practical framework in which to build such a system that is inherent in the emerging Grid computing paradigm that provides the necessary computing resources. A demonstrator system already developed and implemented in the UK E-Science Grid project, DAME, is introduced.