Computational Fluid Dynamic Modeling for Engine Diagnosis

Parthasarathy, Girija; Mylaraswamy, Dinkar

doi:10.1115/gt2003-38567

Cited by 3 publications

(4 citation statements)

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“…For example, combustor efficiency B can be expressed as a function of droplet size d and spray angle S . Parthasarathy and Mylaraswamy (2003) give additional details about these equations. Similarly, the compressor and turbine efficiencies use aerodynamic parameters such as tip clearance T , surface roughness S and blade profile P .…”

Section: Definition Of Performance Parametersmentioning

confidence: 99%

“…Since data is limited, it is important that the classifier be able to learn the underlying pattern and avoid over-fitting. Sambasivan et al (2007) describe a benchmarking approach that measures the sensitivity of a classifier with respect to data perturbations and threshold. As illustrated in Figure 5, this approach provides a simple visualization to eliminate sensitive classifiers for performance-based diagnosis.…”

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confidence: 99%

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Engine performance trending: practical insights

Mylaraswamy

Olson

Nwadiogbu

2009

Transactions of the Institute of Measurement and Control

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A vehicle health usage monitoring system (HUMS) makes it possible to create an accurate picture of the current and expected condition of a propulsion engine. Gas turbine engines, the focus of this paper, convert fuel energy into useful mechanical work by following a well-defined thermodynamic cycle. In this paper, the authors present the principles of engine performance trending, describing them with examples. In engine performance trending, the underlying thermodynamic cycle is analysed using three key steps: cycle characterization using noisy sensor data, interpreting deviations with respect to engine faults and trending the deviations to assess future behaviour. In practical terms, engine performance trending exposes the correct analytic choice based on data and customer requirements. The first half of this paper describes a hierarchy of techniques for characterizing the underlying thermodynamic cycle, from simple energy balance to more complex aerodynamic analysis. Here, we discuss pattern recognition algorithms (eg, fuzzy logic and principal component analysis) for interpreting deviations and the principles of predictive trending. In the second half of the paper, we give examples to describe the practical customization needed to meet the requirements for HUMS. In our final remarks, we discuss open research issues, primarily in the area of constrained parameter estimation, as well as practical validation of engine performance trending. This paper is intended for HUMS practitioners in academia and industry.

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Section: Definition Of Performance Parametersmentioning

confidence: 99%

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confidence: 99%

Engine performance trending: practical insights

Mylaraswamy

Olson

Nwadiogbu

2009

Transactions of the Institute of Measurement and Control

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“…The last few years have seen significant progress in design, development and deployment of engine health monitoring solutions (Jaw 2005, Mylaraswamy et al 2009). This progress is exemplified both with respect to novel sensor development (Uluyol 2010), accurate algorithms and increased coverage of engine components being monitored (Parthasarathy 2011). In order for such potential health monitoring solutions to be operationally viable, they must integrate with existing engine designs and maintenance processes.…”

Section: Introductionmentioning

confidence: 99%

A Cost-Benefit Approach to Evaluating Engine Health Monitoring Systems

2012

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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.

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“…Although some LRUs may not be considered to be engine OEM parts, they nevertheless contribute to the prognostic health of the propulsion system. Consequently, any accurate estimate of propulsion remaining useful life calculation from a CBM (Condition Based Maintenance) perspective must account for all such LRUs (Parthasarathy et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Gas Turbine Engine LRUs Using the Startup Characteristics

Kim¹,

Uluyol²,

Parthasarathy³

et al. 2012

PHM_CONF

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This paper introduces a feature-extraction method to characterize gas turbine engine dynamics. The extracted features are used to develop a fault diagnosis and prognosis method for startup related sub-systems in gas turbine engines - the starter system, the ignition system and the fuel delivery system. The startup of a gas turbine engine from ignition to idle speed is very critical not only for achieving a fast and efficient startup without incurring stall, but also for health monitoring of many subsystems involved. During startup, an engine goes through a number of phases during which various components become dominant. The proposed approach physically monitors the relevant phases of a startup by detecting distinct changes in engine behavior as it manifests in such critical variables as the core speed and the gas temperature. The startup process includes several known milestones, such as starter-on, light-off, peak gas temperature, and idle. As each of these is achieved, different engine components come into play and the dynamic response of the engine changes. Monitoring engine speed and exhaust gas temperature and their derivatives provides valuable insights into engine behavior. The approach of the fault diagnosis system is as follows. The engine startup profiles of the core speed (N2) and the gas temperature are obtained and processed into a compact data set by identifying critical-to-characterization instances. The principal component analysis is applied to a number of parameters, and the fault is detected and mapped into three engine component failures which are the starter system failure, the ignition system failure, and the fuel delivery system failure.

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Computational Fluid Dynamic Modeling for Engine Diagnosis

Cited by 3 publications

References 0 publications

Engine performance trending: practical insights

Engine performance trending: practical insights

A Cost-Benefit Approach to Evaluating Engine Health Monitoring Systems

Fault Diagnosis of Gas Turbine Engine LRUs Using the Startup Characteristics

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