Fault Detection and Identification in an IM270 Gas Turbine Using Measurements for Engine Control

Kurosaki, Masahiro; Morioka, Tadashi; Ebina, Kosuke; Maruyama, Masatoshi; Yasuda, Tomoshige; Endoh, Makoto

doi:10.1115/1.1787515

Cited by 7 publications

(2 citation statements)

References 5 publications

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“…The analysis and investigation of aeronautical engine data is widely investigated by industries and researchers (Chatterjee & Litt, 2003;Deng et al, 2020;Goebel et al, 2007;Kurosaki et al, 2004;Listou Ellefsen et al, 2019;Xu et al, 2020), comprising the collection of numerous information generated by several different sensors in specific positions. The diagnosis of this magnitude generates a large amount of data that usually present a multivariate characteristic.…”

Section: On the Use Of Pca In Turbofan Engine Degradation Datamentioning

confidence: 99%

Principal Component Analysis: An Overview and Applications in Multivariate Engineering Problems

Almeida¹,

Gomes²,

Balestrassi³

et al. 2022

Uncertainty Modeling: Fundamental Concepts and Models

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Section: On the Use Of Pca In Turbofan Engine Degradation Datamentioning

confidence: 99%

Principal Component Analysis: An Overview and Applications in Multivariate Engineering Problems

Almeida¹,

Gomes²,

Balestrassi³

et al. 2022

Uncertainty Modeling: Fundamental Concepts and Models

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show abstract

“…thrust loss, they use derived health conditions corresponding to engines with 3,000 and 6,000 operating hours, respectively. Kurosaki et al (2004) utilize sensor output data from the engine control unit (ECU) to develop their model for engine deterioration. By using only sensors that are already installed on the aircraft, they avoid the need for costly retrofits.…”

Section: Introductionmentioning

confidence: 99%

Engine Load Prediction during Take-Off for the V2500 Engine

Meissner¹,

Reichel

Schlick³

et al. 2020

PHME_CONF

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The aviation industry faces an ever increasing pressure to reduce its cost in order to gain competitive advantages. Since aircraft maintenance contributes strongly with about 17% to the overall direct operating cost (DOC), maintenance providers are required to continuously reduce their cost share as well. As a result, a lot of effort is put into the exploitation of the potential of emerging digitalization technologies to predict upcoming system faults and, therefore, reduce the projected maintenance impact. The detection of early stage faults and prediction of remaining useful lifetimes (RUL) for various systems, including aircraft engines as high-value assets, has been a focal point for many research activities already. A key aspect – necessary for an accurate prediction of future behavior – is the correct mapping of ambient conditions that have led to the respective system condition. Therefore, it is necessary to combine data information throughout an aircraft’s life from different stakeholders to gain valuable insights. However, as the aviation industry is strongly segregated with many parties involved, trying to gain their own competitive advantage, the required information about the operating condition is often not available to independent maintenance providers. Thus, modeling engine degradation often needs to rely on estimated nominal conditions, limiting the ability to precisely predict engine faults. With this paper, we will develop a model that allows users to estimate the experienced engine load during take-off by only using publicly available information, i.e. airport weather information reports and public flight data. The calculated engine load factors are computed in terms of an engine pressure ratio (EPR) derate. The results are benchmarked with the actual engine derate, obtained for different operators and various ambient conditions, to enable an identification of challenges for the load prediction and areas of improvement. The developed model will help to adjust engine failure projections according to the experienced ambient conditions and, therefore, supports the development of better engine degradation models.

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A new method to evaluate the influence coefficient matrix for gas path analysis

2009

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To create an online diagnostic system for a gas turbine, a well-formulated-influence coefficient matrix (ICM), a key component in a widely used existing theory, is essential. However, according to a recent research of the present authors, parameter deviations estimated by the traditional ICM contain unavoidable errors. These errors, called matching deviations, have been verified to be a consequence of the shifting operating point caused by component deterioration. Here we present a simple and accurate method which is still based on the widely accepted existing theory that accounts for component deterioration. Matching coefficients are introduced in this method to isolate and eliminate matching deviations. The ICM yielded by this method can improve the accuracy of both measurement prediction and parameter deviation estimations. By modeling a commercial power generation engine, a demonstration of the new method is presented and its limitation is discussed.

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Fault Detection and Identification in an IM270 Gas Turbine Using Measurements for Engine Control

Cited by 7 publications

References 5 publications

Principal Component Analysis: An Overview and Applications in Multivariate Engineering Problems

Principal Component Analysis: An Overview and Applications in Multivariate Engineering Problems

Engine Load Prediction during Take-Off for the V2500 Engine

A new method to evaluate the influence coefficient matrix for gas path analysis

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