Testability Design of the PHM System for Aero-Engines

2012 ◽  
Vol 544 ◽  
pp. 94-98
Author(s):  
Zhen Hua Wen ◽  
Yuan Peng Liu ◽  
Xin Yin
Keyword(s):  
Life Cycle ◽  
Health Management ◽  
Management Strategies ◽  
Prognostics And Health Management ◽  
Technical Approach ◽  
Test Program ◽  
Fusion Methods ◽  
Aero Engines ◽  
Total Life ◽  
Monitoring Information

PHM (Prognostics and Health Management) for Aero-engines is an effective technical approach to balance the economy and safety of the flight in the total life cycle. In this paper, we mainly analyze the popular issues in the process of designing PHM system for aero-engines including the testability design concept, the scheme of condition monitoring and the utilization extent of condition information. Then presents some useful solutions and advices for the testability design respectively; and analyzes the influence of testability on health management strategies and the main source of uncertainty; then propose a roadmap for making test program based on the PHM requirements and evaluating test program, for improve the utilizing degree of monitoring information, we lastly presented common data fusion methods and some typical examples is illustrated.

The Uncertainty Analysis of Health Management Strategies for Aero-Engines

2012 ◽  
Vol 201-202 ◽  
pp. 34-38
Author(s):  
Zhen Hua Wen ◽  
Xin Yin ◽  
Yuan Peng Liu
Keyword(s):  
Health Management ◽  
Management Strategies ◽  
Flight Safety ◽  
Diagnosis And Prognosis ◽  
Uncertainty Sources ◽  
Information Monitoring ◽  
Aero Engine ◽  
Aero Engines ◽  
Fault Diagnosis And Prognosis ◽  
Total Life

The Aero-Engine Is the Heart of an Aircraft and Critical to the Flight Safety and Economy, Health Management in Total Life Cycle for Aero-Engines Is an Effective Way and Is Popularly Employed to Balance the Economy and Safety. however, it Is Difficult to Make Appropriate Health Management Strategies for a Complex System, such as Aero-Engines. Firstly the Uncertainty Source of Fault Diagnosis and Prognosis System Is Explored. then the Primary Uncertainty Sources Including Condition Information, Monitoring and Detection Approaches, Signal Detection, Remaining Lifetime Prediction and Maintenance Strategy-Making, Are Analyzed in this Context. the Corresponding Tactics for Reducing Uncertainty Are Proposed in the Paper, which Provides Essential References for Assisting the Design for Health Management System.

A Survey of Sensor Technologies for Prognostics and Health Management of Electronic Systems

2014 ◽  
Vol 602-605 ◽  
pp. 2229-2232 ◽  
Author(s):  
Wen Xue Yang ◽  
Zhe Chen ◽  
Feng Yang
Keyword(s):  
Life Cycle ◽  
Early Warning ◽  
Health Management ◽  
Life Cycle Costs ◽  
Electronic Systems ◽  
Sensor Systems ◽  
Prognostics And Health Management ◽  
Electronic Products ◽  
System Failures

Recently, the field of Prognostics and Health Management (PHM) for electronic products and systems has received increasing attention due to the potentialities to provide early warning of system failures, reduce life cycle costs, and forecast maintenance as needed. This paper introduces the sensors and their sensor technologies. The required attributes of sensors for the development for PHM of electronics are discussed. Finally, their trends in sensor systems are presented.

scholarly journals PHM Survey : Implementation of Diagnostic Methods for Monitoring Industrial Systems

2019 ◽  
Vol 10 (2) ◽  
Author(s):  
Abdenour Soualhi ◽  
Bilal Elyousfi ◽  
Yasmine Hawwari ◽  
Kamal Medjaher ◽  
Guy Clerc ◽  
...  
Keyword(s):  
Life Cycle ◽  
Health Management ◽  
Fault Isolation ◽  
Diagnostic Methods ◽  
Condition Based Maintenance ◽  
Prognostics And Health Management ◽  
Isolation And Identification ◽  
Industrial Systems ◽  
Industrial Sectors ◽  
Processing Step

The modernization of industrial sectors involves the use of complex industrial systems and therefore requires condition based maintenance. This one aims at increasing the operational availability and reducing the life-cycle while increasing the reliability and life expectancy of industrial systems. This maintenance also called predictive maintenance is a part of an emerging philosophy called PHM ‘Prognostics and Health Management’. In this paper, the PHM will be emphasized on the existing diagnostic methods used for fault isolation and identification. This depicts an important part of the PHM as it exploits the data given by the signal-processing step and its output is treated by the prognostic part. The diagnostic is mainly classified in three categories that will be highlighted in this paper.

scholarly journals Prognostics and Health Management System for Electric Vehicles with a Hierarchy Fusion Framework: Concepts, Architectures, and Methods

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Cheng Wang ◽  
Tongtong Ji ◽  
Feng Mao ◽  
Zhenpo Wang ◽  
Zhiheng Li
Keyword(s):  
Life Cycle ◽  
Fault Diagnosis ◽  
Real Time ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Health Management ◽  
Research Progress ◽  
Prognostics And Health Management ◽  
Time Data ◽  
Real Time Data

The prognostics and health management (PHM) of electric vehicles is an important guarantee for their safety and long-term development. At present, there are few studies researching about life cycle PHM system of electric vehicles. In this paper, we first summarize the research progress and key methods of PHM. Then, we propose a three-level PHM system with a hierarchy fusion architecture for electric vehicles based on the structure, data source of them. In the PHM system, we introduce a database consisting of the factory data, real-time data, and detection data. The electric vehicle's factory parameters are used for determining the life curve of the electric vehicle and its components, the real-time data are used for predicting the remaining useful lifetime (RUL) of the electric vehicle and its components, and the detection data are used for fault diagnosis. This health management database is established to help make condition-based maintenance decisions for electric vehicles. In this way, a complete electric vehicle PHM system is formed, which can realize the whole-life-cycle life prediction and fault diagnosis of electric vehicles.

Challenges in Reliability Assessment for Electronics

2010 ◽  
Vol 118-120 ◽  
pp. 419-423 ◽  
Author(s):  
Qing Chuan He ◽  
Wen Hua Chen ◽  
Jun Pan ◽  
Shi Jiao Wang
Keyword(s):  
Life Cycle ◽  
Failure Mechanism ◽  
Health Management ◽  
Reliability Assessment ◽  
Model Identification ◽  
Catastrophic Failure ◽  
Prognostics And Health Management ◽  
Enabling Technology ◽  
Actual Application ◽  
Reliability Of Electronics

There has been a growing interest in assessing the ongoing reliability of electronics and systems in order to predict failures and provide warning to avoid catastrophic failure. Methods based on prognostics and health management shows an enabling technology to assess the reliability of electronics and systems under its actual application conditions. However, many challenges in implementation of methods based on PHM still remain including: environmental and usage profiles for life-cycle loads, identification of failure mechanism, identification of failure PoF model, identification of parameters to be monitored, approaches to anomaly detection. These challenges were presented and discussed, and would be carried out by developing methodologies and techniques.

A Logistics Model of Total Life Cycle of Mechanical Products

2012 ◽  
Vol 248 ◽  
pp. 402-407 ◽  
Author(s):  
Liang He ◽  
Wan Lin Guo
Keyword(s):  
Life Cycle ◽  
Life Cycle Analysis ◽  
Production Capacity ◽  
Stable Production ◽  
Equivalent Quantity ◽  
Aero Engine ◽  
Mechanical Products ◽  
Aero Engines ◽  
Total Life ◽  
Production Resources

A kind of processing strategy of total life cycle of mechanical products was designed. A logistics model of total life cycle of mechanical products was established based on eight typical states of life cycle of mechanical products. The logistics analysis of total life cycle of a sort of aero-engine was carried out by using the model. The dynamic equivalent quantity of the aero-engines in different states of life cycle was obtained when times changed from the products were first put into production to the time when stable production capacity was reached. The model can also be used to predict logistics of other products rapidly. The results give references for production departments or enterprises which use life cycle methods to configure their production resources effectively and optimize production processes, and also provide a basis for further analysis of total life cycle analysis such as economic and environmental assessment.

A Review on Prognostics and Health Management : 2013~2018

2019 ◽  
Vol 19 (1) ◽  
pp. 68-84 ◽  
Author(s):  
Hyun Su Sim ◽  
Jun-Gyu Kang ◽  
Yong Soo Kim
Keyword(s):  
Health Management ◽  
Prognostics And Health Management

Remaining useful life estimation: a review on stochastic process-based approaches

2020 ◽  
Vol 14 ◽  
Author(s):  
Dangbo Du ◽  
Jianxun Zhang ◽  
Xiaosheng Si ◽  
Changhua Hu
Keyword(s):  
Stochastic Process ◽  
Health Management ◽  
Remaining Useful Life ◽  
Future Trends ◽  
Prognostics And Health Management ◽  
Inverse Gaussian ◽  
Life Estimation ◽  
Inverse Gaussian Process ◽  
Pros And Cons ◽  
Useful Life

Background: Remaining useful life (RUL) estimation is the central mission to the complex systems’ prognostics and health management. During last decades, numbers of developments and applications of the RUL estimation have proliferated. Objective: As one of the most popular approaches, stochastic process-based approach has been widely used for characterizing the degradation trajectories and estimating RULs. This paper aimed at reviewing the latest methods and patents on this topic. Methods: The review is concentrated on four common stochastic processes for degradation modelling and RUL estimation, i.e., Gamma process, Wiener process, inverse Gaussian process and Markov chain. Results: After a briefly review of these four models, we pointed out the pros and cons of them, as well as the improvement direction of each method. Conclusion: For better implementation, the applications of these four approaches on maintenance and decision-making are systematically introduced. Finally, the possible future trends are concluded tentatively.

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