Research on Prognostics and Health Management System for Self-Propelled Gun

2012 ◽  
Vol 591-593 ◽  
pp. 1938-1941
Author(s):  
Xin Jie Shao ◽  
Li Jun Cao ◽  
Jin Hua Liu ◽  
Qiao Ma ◽  
Guang Tian ◽  
...  
Keyword(s):  
Health Management ◽  
Fault Prediction ◽  
Maintenance Cost ◽  
Prognostics And Health Management ◽  
Working Stress ◽  
System Establishment ◽  
Prediction Mechanism ◽  
Fault Mechanism ◽  
Key Techniques ◽  
Fault Inference

Considering the encapsulation, tremendous working stress, complicated fault mechanism and process, an object-oriented architecture Prognostics and Health Management (PHM) system of Self-Propelled Gun is put forward to solve the difficulties in modern equipments’ maintenance decision and safeguard. The relative key techniques, such as fault characteristics extraction, system identification, data fusion among sensors, adaptive fault thresholds setting, intelligent fault inference mechanism and fault prediction mechanism are discussed in detail. Above functional modules compose the PHM system. Their mutual relationships and data flow are shown as architecture graph, which is the basis of PHM system establishment. Simulation verification experiments indicate that the proposed architecture can satisfy the requirements of standardization, hierarchy and evolvement, as well as reducing the maintenance cost and difficulties.

scholarly journals Prognostics and health management technology for radar system

2020 ◽  
Vol 309 ◽  
pp. 04009
Author(s):  
Yongle Lyu ◽  
Zhuo Pang ◽  
Chuang Zhou ◽  
Peng Zhao
Keyword(s):  
Model Building ◽  
Health Management ◽  
Radar System ◽  
Prognostics And Health Management ◽  
Failure Model ◽  
Long Distance ◽  
Maintenance System ◽  
Management Technology ◽  
Key Techniques ◽  
Selection Of

Information-based war in the future has a higher requirement to the maintenance and support ability of radar system. Prognostics and Health Management(PHM) technology represents the research hotspot of maintenance system, and following key techniques need to be resolved to research on the radar PHM technology such as the acquirement and selection of health information and fault signs of a radar’s electronical components, mass data warehousing and mining, fusion of multi-source test data and multi-field characteristic information, failure model building and forecasting, automatic decision-making on maintenance, and at the same time improving the self built-in test abilities of radar’s components based on the optimization of Design For Testability(DFT). The radar PHM technology has the trend of “built-in to integrate”, “together with DFT” and “long-distance and distributed”. However, subjected to radar’s complexity and current PHM technique level, radar PHM engineering still meets many challenges, but has bright future.

scholarly journals Prognostics and Health Management for Maintenance Practitioners - Review, Implementation and Tools Evaluation

2020 ◽  
Vol 8 (3) ◽  
Author(s):  
Vepa Atamuradov ◽  
Kamal Medjaher ◽  
Pierre Dersin ◽  
Benjamin Lamoureux ◽  
Noureddine Zerhouni
Keyword(s):  
High Speed ◽  
Health Management ◽  
Maintenance Cost ◽  
Health State ◽  
Prognostics And Health Management ◽  
Feature Evaluation ◽  
Tool Evaluation ◽  
Implementation Aspects ◽  
Selection For ◽  
Support Decision Making

In literature, prognostics and health management (PHM) systems have been studied by many researchers from many different engineering fields to increase system reliability, availability, safety and to reduce the maintenance cost of engineering assets. Many works conducted in PHM research concentrate on designing robust and accurate models to assess the health state of components for particular applications to support decision making. Models which involve mathematical interpretations, assumptions and approximations make PHM hard to understand and implement in real world applications, especially by maintenance practitioners in industry. Prior knowledge to implement PHM in complex systems is crucial to building highly reliable systems. To fill this gap and motivate industry practitioners, this paper attempts to provide a comprehensive review on PHM domain and discusses important issues on uncertainty quantification, implementation aspects next to prognostics feature and tool evaluation. In this paper, PHM implementation steps consists of; (1) critical component analysis, (2) appropriate sensor selection for condition monitoring (CM), (3) prognostics feature evaluation under data analysis and (4) prognostics methodology and tool evaluation matrices derived from PHM literature. Besides PHM implementation aspects, this paper also reviews previous and on-going research in high-speed train bogies to highlight problems faced in train industry and emphasize the significance of PHM for further investigations.

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.

A Copula-Based Sampling Method for Data-Driven Prognostics and Health Management

2013 ◽  
Author(s):  
Zhimin Xi ◽  
Rong Jing ◽  
Pingfeng Wang ◽  
Chao Hu
Keyword(s):  
Sampling Method ◽  
Failure Time ◽  
Health Management ◽  
Remaining Useful Life ◽  
Training Data ◽  
Data Driven ◽  
Prognostics And Health Management ◽  
Statistical Relationship ◽  
Cooling Fans ◽  
On Line

This paper develops a Copula-based sampling method for data-driven prognostics and health management (PHM). The principal idea is to first build statistical relationship between failure time and the time realizations at specified degradation levels on the basis of off-line training data sets, then identify possible failure times for on-line testing units based on the constructed statistical model and available on-line testing data. Specifically, three technical components are proposed to implement the methodology. First of all, a generic health index system is proposed to represent the health degradation of engineering systems. Next, a Copula-based modeling is proposed to build statistical relationship between failure time and the time realizations at specified degradation levels. Finally, a sampling approach is proposed to estimate the failure time and remaining useful life (RUL) of on-line testing units. Two case studies, including a bearing system in electric cooling fans and a 2008 IEEE PHM challenge problem, are employed to demonstrate the effectiveness of the proposed methodology.

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