JETA: A knowledge-based approach to aircraft gas turbine engine maintenance

Halasz, Michael; Davidson, Philippe; Abu-Hakima, Suhayya; Phan, Sieu

doi:10.1007/bf00058574

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…As we mentioned in the introduction, CNIBL can be used in conjunction with a knowledgebased system for gas turbine diagnosis [1]. For example, CNIBL can be used to generate preliminary diagnoses (diagnostic hypotheses), which will be further refined by interaction with the knowledge-based system.…”

Section: Practical Evaluationmentioning

confidence: 99%

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Contextual normalization applied to aircraft gas turbine engine diagnosis

Turney

Halasz

1993

Appl Intell

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Diagnosing faults in aircraft gas turbine engines is a complex problem. It involves several tasks, including rapid and accurate interpretation of patterns in engine sensor data. We have investigated contextual normalization for the development of a software tool to help engine repair technicians with interpretation of sensor data. Contextual normalization is a new strategy for employing machine learning. It handles variation in data that is due to contextual factors, rather than the health of the engine. It does this by normalizing the data in a context-sensitive manner. This learning strategy was developed and tested using 242 observations of an aircraft gas turbine engine in a test cell, where each observation consists of roughly 12,000 numbers, gathered over a 12 second interval. There were eight classes of observations: seven deliberately implanted classes of faults and a healthy class. We compared two approaches to implementing our learning strategy: linear regression and instance-based learning. We have three main results. (1) For the given problem, instance-based learning works better than linear regression. (2) For this problem, contextual normalization works better than other common forms of normalization. (3) The algorithms described here can be the basis for a useful software tool for assisting technicians with the interpretation of sensor data.

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Section: Practical Evaluationmentioning

confidence: 99%

“…If the data are sufficiently interesting, they may be added to the library. This software could advise a technician directly, or it could be used in conjunction with a knowledge-based system, such as the system described in [1].…”

Section: Introductionmentioning

confidence: 99%