Jet engine life prediction systems integrated with prognostics health management

Integrated Systems Health Management includes fault detection, fault diagnosis (or fault isolation), and fault prognosis. We define prognosis to be detecting the precursors of a failure, and predicting how much time remains before a likely failure. Algorithms that use the data-driven approach to prognosis learn models directly from the data, rather than using a hand-built model based on human expertise. This paper surveys past work in the datadriven approach to prognosis. It also includes related work in data-driven fault detection and diagnosis, and in model-based diagnosis and prognosis, particularly as applied to space systems.

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“…The proposed architecture includes an offboard PHM system (OBPHM), which will use data mining techniques. 10,16,17…”

Section: Data-driven Prognosismentioning

confidence: 99%

A Survey of Data-Driven Prognostics

2005

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“…For example, undetected flaws during inspection can cause the initiation and growth of cracks earlier than expected. This would effectively reduce the remaining lifetime of the component, and possibly cause unpredictable failures with potentially catastrophic outcomes before the designed maintenance period [1][2][3][4]. Moreover, despite exceptional advancements in high temperature superalloys, internal cooling and the application of thermal barrier coatings (TBCs) have remained the norm in high temperature turbine blades, allowing high engine thermodynamic efficiency and relatively long durability [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Numerical assessment of blade deflection and elongation for improved monitoring of blade and TBC damage

Dyke

Nganbe

2021

Eng. Res. Express

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The reliability of turbine blades is largely maintained by damage tolerance approach based on monitoring and pre-set periodic inspections. This can result in unnecessary downtimes, premature part retirement and unforeseeable failures. Therefore, there is growing interest in systems that can reliably detect damages in real‐time. However, many current sensors are based on blade tip clearance and time of arrival. The first primarily correlates with relatively predictable long-term creep deformation and ensuing blade elongation, while the second can be related to blade deflection. Therefore, this research comparatively assesses the two parameters. For this purpose, TBC defects, representative for coating spallation, and notches, representative for blunted blade cracks, are investigated. Overall, the results suggest that the measurement of changes in axial deflection could show higher sensitivity to cracks and TBC defects, and therefore, constitutes a potential alternative for continuous monitoring with respect to unforeseeable rapidly growing blade damage. Moreover, TBC spallation seems more difficult to immediately detect as the ensuing changes in blade tip position are small. However, they cause changes in deflection that can switch from negative to positive as they are located closer to the blade root, which may allow to assess their location during monitoring. In contrast, critical cracks located close to the blade root can cause measurable changes in blade deflexion, potentially making their timely detection and continuous monitoring possible.

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“…It's already too late to stop the Artificial Intelligence arms race, we must manage it instead" [12], [13]. Even the F-35 uses AI to distribute battlefield awareness [8], [14], [15], and the office or Naval Research's (ONR) Smart Ship Program also uses AI [16], [17], [18] which is essentially replace sailors [2] on the their 27 CG 47 Ticonderoga-class cruisers [10], [13]. To sum up the realization the US is in danger of losing its lead on Artificial Intelligence to China is evidenced by, William Roper, when heading the US Pentagon's Strategic Capabilities Office, said that his superiors at the Pentagon finally realized that America's world-leading efforts in artificial intelligence were about to be overtaken by China [8], [13], when in March 2016, Elsa Kania, a specialist on Chinese military innovation showed Roper's superiors that it was when Google's DeepMind used Artificial Intelligence to defeat a world champion in the ancient Chinese game of Go, that the Chinese military made a goal to have Chinese Artificial Intelligence surpass the human mind and the US militaries' Artificial Intelligence by 2030 [8].…”

Section: Introductionmentioning

confidence: 99%

Chinese & Iranian Artificial Intelligence in Low Earth Orbit to THAAD Space Wars

Lewis

2019

5th International Conference on Computer Science and Information Technology (CSTY 2019)

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This paper addresses what the role of artificial intelligence will be in low earth orbit (LEO) and space war is and specifically, what China and Iran's latest research in artificial intelligence on unmanned aerial vehicles (UAVs) for LEO space war domains has been, is, and strives to become. The author first presents testimony from scholars and space research scientists from many countries who all categorically state, without a trace of doubt, that all future space warfare will be in satellites and unmanned UAVs and how they in turn will rely heavily on artificial intelligence. This paper includes an analysis on China's strengths in space artificial intelligence and, Iran's systematic approach or arming its UAVs. The second portion of this research drills down into what are the specific mathematical theoretical research areas of artificial research for LEO space wars in various countries, including China. The author concludes with research strategies that will combat China's dominance of space wars.

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Jet engine life prediction systems integrated with prognostics health management

Cited by 10 publications

References 3 publications

A Survey of Data-Driven Prognostics

A Survey of Data-Driven Prognostics

Numerical assessment of blade deflection and elongation for improved monitoring of blade and TBC damage

Chinese & Iranian Artificial Intelligence in Low Earth Orbit to THAAD Space Wars

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