Artificial intelligence techniques applied to vehicle management system diagnostics

Cochran, Keith

doi:10.1109/dasc.1991.177201

Cited by 1 publication

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ref. [70] used the facts to describe the status of the fuel system's components and other associated information (names and locations). For example, if a particular fact was determined as "true", a failure may exist.…”

Section: Reasoning-based Diagnosticsmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Fault Diagnosis of an Aircraft Fuel System Using Machine Learning—A Literature Review

King

Jennions

2023

Machines

View full text Add to dashboard Cite

The fuel system, which aims to provide sufficient fuel to the engine to maintain thrust and power, is one of the most critical systems in the aircraft. However, possible degradation modes, such as leakage and blockage, can lead to component failure, affect performance, and even cause serious accidents. As an advanced maintenance strategy, Condition Based Maintenance (CBM) can provide effective coverage, by combining state-of-the-art sensors with data acquisition and analysis techniques to guide maintenance before the asset’s degradation becomes serious. Artificial Intelligence (AI), particularly machine learning (ML), has proved effective in supporting CBM, for analyzing data and generating predictions regarding the asset’s health condition, thus influencing maintenance plans. However, from an engineering perspective, the output of ML algorithms, usually in the form of data-driven neural networks, has come into question in practice, as it can be non-intuitive and lacks the ability to provide unambiguous engineering signals to maintainers, making it difficult to trust. Engineers are interested in a deterministic decision-making process and how it is being revealed; algorithms should be able to certify and convince engineers to approve recommended actions. Explainable AI (XAI) has emerged as a potential solution, providing some of the logic on how the output is derived from the input given, which may help users understand the diagnostic result of the algorithm. In order to inspire and advise data scientists and engineers who are about to develop and use AI approaches in fuel systems, this paper explores the literature of experiment, simulation, and AI-based diagnostics for the fuel system to make an informed statement as to the progress that has been made in intelligent fault diagnostics for fuel systems, emphasizing the necessity of giving unambiguous engineering signals to maintainers, as well as highlighting potential areas for future research.

show abstract

Section: Reasoning-based Diagnosticsmentioning

confidence: 99%

“…Ref. [70] discussed the Vehicle Management System (VMS). The VMS was developed by McDonnell Aircraft using the programming language CLIPS developed by NASA in 1985.…”

Section: Reasoning-based Diagnosticsmentioning

confidence: 99%

Intelligent Fault Diagnosis of an Aircraft Fuel System Using Machine Learning—A Literature Review

King

Jennions

2023

Machines

View full text Add to dashboard Cite

show abstract

Artificial intelligence techniques applied to vehicle management system diagnostics

Cited by 1 publication

References 0 publications

Intelligent Fault Diagnosis of an Aircraft Fuel System Using Machine Learning—A Literature Review

Intelligent Fault Diagnosis of an Aircraft Fuel System Using Machine Learning—A Literature Review

Contact Info

Product

Resources

About