Hierarchical HMMs for Autonomous Diagnostics and Prognostics

Camci, Fatih; Chinnam, Ratna Babu

doi:10.1109/ijcnn.2006.247092

Cited by 9 publications

(2 citation statements)

References 18 publications

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“…To different systems, many methods can be applied. Regression Prediction model [1] is based on mass statistic data, Artificial Neural Network model [2] simulates brain activity of human beings, condition changing is thought in Markov Model [3] and so on. Although condition of equipment is a success process, it can be divided different stages based on some particular rules.…”

Section: Introductionmentioning

confidence: 99%

Multi-Stage Combination Prediction Model of Technical Condition Parameters of Equipment

Zhang

Zan

Zheng

et al. 2014

AMM

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Technical condition parameters prediction is one of key steps in CBM (Condition based Maintenance). As disadvantage of traditional methods, Base on theory of combination prediction, combining with Delay Time and two-stage prediction model, multi-stage combination prediction model is proposed. The model can solve the problem that complicated rule of variation in equipment condition and uncertainty characteristic. Through lubricating oil analysis and correlated prescribing, operation phase of equipment is parted. At the same time, every method adapt to every stage is found. The result shows the model works well and effectively.

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Section: Introductionmentioning

confidence: 99%

Multi-Stage Combination Prediction Model of Technical Condition Parameters of Equipment

Zhang

Zan

Zheng

et al. 2014

AMM

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“…A large number of technologies are becoming available within the PHM community that enables improved fault detection, advanced diagnostics, and prognostics in aerospace systems [3][4][5][6][7][8][9]. Advances in sensor, health assessment, diagnostics, prognostics, and decision support technologies have produced a wide variety of potential maintenance solutions.…”

Section: Introductionmentioning

confidence: 99%

Methodologies for Integration of PHM Systems with Maintenance Data

Camci

Valentine

Navarra³

2007

2007 IEEE Aerospace Conference

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The Automatic logistics program in the Air Force seeks to reduce development, production, and ownership costs for the next generation fighter aircraft by increasing system reliability while reducing maintenance requirements. A large number of technologies are becoming available within the Prognostics and Health Management (PHM) community that will lead to reduced cost and increased availability. 1 2The challenge is to develop advanced technology to integrate available PHM information from a variety of different sources into the maintenance and logistics infrastructure. PHM and maintenance/logistics systems must be thoroughly examined and tightly integrated in order to perform maintenance actions in the most efficient way to reduce ownership cost and increase availability. This paper presents multi-agent technology that integrates maintenance and PHM data to provide more effective maintenance identification and scheduling.The proposed methodologies will enable the maintenance and logistics infrastructure to fully benefit from newly developed PHM systems. Additionally, the PHM systems update themselves based on feedback obtained from the maintenance systems. The integration will utilize intelligent software agent technology in order to develop such solutions within open, highly dynamic, uncertain and complex environments with data distributed over a network. This provides benefits such as reusability, scalability, and continuous improvement with dynamically evolving ability.

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