Prospects for Aero Gas-turbine Diagnostics a Review

APPLIED ENERGY

Applied Energy 79 (2004) 109–126

www.elsevier.com/locate/apenergy

Prospects for aero gas-turbine diagnostics: a review Luca Marinai *, Douglas Probert, Riti Singh Department of Power, Propulsion and Aerospace Engineering, Cranfield University, Bedford MK43 0AL, UK Accepted 21 October 2003 Available online 27 February 2004

Abstract Despite inflating unit-fuel costs, the long-term prospects for the aircraft industry remain buoyant. Nevertheless reducing direct operating-costs is crucial to ensure competitive advantages for airlines and manufacturers, and so effective advanced engine-condition monitoring methodologies are desirable. Hence gas-path diagnostic methods are reviewed and the specifications for such effective tools deduced, together with pertinent future prospects.
2004 Elsevier Ltd. All rights reserved. Keywords: Performance; Diagnostics; Gas-path analysis; Aftermarket

1. Introduction At present, there are deep financial uncertainties in the civil aircraft market and therefore intense competition among airlines. Hence the development of advanced maintenance-techniques in order to reduce operating-costs [1,2]. Engine-related costs contribute a large fraction of the direct operating-costs (DOCs) of an aircraft, because the propulsion system requires a significant part of the overall maintenance effort that has to be expended for each aircraft – see Fig. 1. The world market for transportation by air is expanding, despite the difficulties and changes following the horrific terrorist attack on September 11th 2001 in New

*

Corresponding author. Tel.: +44-1234-750-111-526; fax: +44-1234-752-407. E-mail address: l.marinai.2001@cranfield.ac.uk (L. Marinai).

0306-2619/$ - see front matter
2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.apenergy.2003.10.005

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Abbreviations A/C aircraft AGARD Advisory Group for Aerospace Research and Development AI artificial intelligence AIAA American Institute of Aeronautics and Astronautics ANN artificial neural-network ASEE American Society of Engineering Education ASME American Society of Mechanical Engineers BBN Bayesian-belief network COMPASS condition-monitoring and performance analysis software system DOC direct operating-cost EKF extended Kalman-filter ES expert system FFBPNN feed-forward back-propagation neural-network FL fuzzy-logic FMPT fleet-management programme GA genetic-algorithm GPA gas-path analysis GT gas-turbine HPC high-pressure compressor HPT high-pressure turbine ICM influence coefficient matrix IEKF integrated extended Kalman-filter IFAC International Federation of Automatic Control IPC intermediate-pressure compressor IPT intermediate-pressure turbine ISABE International Society for Air-Breathing Engines KES knowledge-based engineering systems KF Kalman-filter LPT low-pressure turbine M number of measurements MCPHT maintenance cost per hour MFI multiple-fault isolation MLP multi-layer perceptron N number of performance parameters NASA National Aeronautics and Space Administration OEM original equipment manufacturer Pin inlet pressure Pout exit pressure PNN probabilistic neural-network RPK revenue arising from passenger-kilometres

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RPM SAE SFC SFI Tin Tout WLS

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revolutions per minute Society of Automotive Engineers specific fuel consumption single-fault isolation inlet temperature exit temperature weighted least-squares

Fig. 1. Typical costs as fraction of a civil aircraftÕs DOC [3].

York. Economic growth is the primary drive for increased travel by air. Airbus [4] predicts that, during the next 20 years, such world traffic, measured in revenue arising in US dollars from passenger-kilometres (RPKs), will increase at an average annual rate of 4.7%. By 2020, world annual RPKs will reach 8.3 trillion compared with the 3.3 trillion in 2000 – see Fig. 2. In the current financial climate, it would appear that the new generation of lowcost airlines will prosper, and, in response to increasingly severe cost-pressures and competition, established airlines are, and will be, driven even further to improve the efficiencies of their route networks and to use low-unit-cost aircraft [4]. Hence the present knock-on effect of severe competition among gas-turbine manufacturers. Previously the business was driven by technological progress (such as the introduction of cooled blades or the move from pure jets to turbofans). In the 1970s and 1980s, integration of engineering and manufacturing functions reduced costs. More recently, considerable interest has been devoted to lowering operating-costs by introducing improvements in engine reliability, achieving extended ‘‘life on wing’’ and upgrading maintenance schedules [5].

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Fig. 2. Predicted air-world traffic growth [4].

2. New role for the gas-turbine after-sales market The gas-turbine business model has been based on revenue strictly related to the aftercare. Nowadays the airlines demand is for high quality fleet-management and comprehensive engine-aftercare service, based on an agreed prior to the engine delivery rate per engine flying hour. The engine is paid for during the period when the aircraft is in the air and so producing revenue. This transfers much of the technical risk from the airline to the gas-turbine manufacturer. Consequently improvements concerning the in-service operations of engines have already had significant impacts on the business [2]. Prolonging a gas-turbineÕs life could lead to a scenario in which the engine will not require a major service during the aircraftÕs life (e.g. 25 years). In the previous after-sales maintenance scenario, this would have resulted in the partial or complete loss of the engine-manufacturersÕ aftermarket business. So, companies would have had to make compensating higher profits on the original equipment sale [5]. The new business scenario could therefore be a win–win opportunity for airlines and manufacturers. ÔPower by the HoureÕ (trade mark held by Rolls-Royce) type of contracts, which includes the capital cost plus a blend of financing and maintenance after the engineÕs sale, are increasingly being demanded. Similarly the highly successful General ElectricÕs ÔMaintenance Cost per HoureÕ (MCPHe) contracts and Pratt and Whitney ÔFleet-management ProgrammeeÕ (FMPe) contracts offer longterm service agreements. These programmes provide engine maintenance on a flat rate per engine flight-hour basis, so enabling airlines to accurately forecast operating-costs, reduce cost of ownership and improve asset utilization [6,7]. In these circumstances, a key competitive advantage for manufacturers will be their understanding of this market and one consideration within this will be concerned with engine-condition monitoring methodologies. Among them, at present, partic-

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ular consideration should be given to gas-path diagnostics that plays a primary role in an aero-engine performance oriented business. Engine gas-path diagnoses have been recognised, for some time, as important means for making more informed decisions on the usage, maintenance, overhaul or replacement of the engine or one of its components. Deterioration can affect factors such as thrust (or power) and specific fuel-consumption (SFC). As a consequence of progressive performance-losses, operation of the engine can become cost ineffective or even unsafe. Therefore monitoring and maintenance techniques are employed to ensure that the gas-turbine operates both cost effectively and safely. 3. Gas-path diagnostic methodologies A survey of pertinent techniques, developed since the pioneer Urban [8] first explored these problems is now presented. The performance of an aero-engine deteriorates over time as a consequence of its componentsÕ degradation. The identification of the exact component(s) responsible for the performance loss facilitates the choice of the recovery action to be undertaken. An engine gas-path diagnostic process calculates changes in the magnitudes of the component performance parameters (e.g. efficiency and flow capacity) given a set of measurements (e.g. temperatures, pressures, shaft speed and fuel flow) through the engine. However accurate assessment is complicated by (i) only having relatively few measurements available, and (ii) errors in the measurements (e.g. due to uncertainties, noise and biases). The relationship between measurements and performance parameters can be expressed analytically as follows: z ¼ hðx; wÞ þ m þ b;

ð1Þ

where z is the measurement vector, x the performance parameters vector, w the vector of environment and power-setting parameters, m the measurement noise vector, b the sensor bias and hð Þ a vector valued non-linear function: hð Þ is provided by the simulation program [9]. A recent update of gas-path diagnostics methodologies is contained in the Von Karman Institute lecture series 2003-01 on gas-turbine condition monitoring and fault diagnosis edited by Mathioudakis and Sieverding [10]. Many pertinent tools have been devised during the last three decades and a critical review of the most used techniques and their applications now follows – see also Table 1 – highlighting similarities, differences and limitations. 3.1. Linear gas-path analysis with ICM inversion This is based on the assumption that the changes in the (independent) healthparameters are relatively small and the set of governing equations can be linearized around a given steady-state operating point. These linearized equations can be expressed in matrix form. z ¼ Hx

ð2Þ

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Table 1 Summary of GPA methodologies (X means that the methodology involves that feature) Strategy involved

Linear GPA with ICM inversion

Non-linear GPA with ICM inversion

Linear Kalmanfilter

Linear WLS

Non-linear Kalmanfilter

Non-linear model-based with GA

Artificial neural-networks

Bayesian-belief networks

Linear

Non-linear

Linear

Linear

Non-linear

Non-linear

Non-linear

Non-linear

X

X

X

X

X

X

X

X

X

X

X M