Statistical & Computational Intelligence Methods for Prognostics in Condition-Based-Maintenance Systems

统计

• 批准号: 0300132
• 负责人: Ratna Babu Chinnam
• 金额: $ 10万
• 依托单位: Wayne State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-07-15 至 2006-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0300132&HistoricalAwards=false
• 关键词: Statistical; & Computational; Intelligence; Methods

This grant provides funding for the development of theory and algorithms necessary to establish effective prognostics methods and technologies for Condition-Based Maintenance (CBM) of critical equipment and systems through statistical and computational intelligence methods and sensor-fusion technology. The objective is to develop prognostic algorithms that facilitate accurate estimation of Remaining-Useful-Life (RUL) values and confidence levels for components and equipment. The prognostic methods for signal modeling and forecasting will exploit existing theory in three promising areas: Statistical Nonlinear Mixed-Effect Models, Dynamic Bayesian Network Models, and Structural-Learning Neural Networks. In order to accurately establish a definition of failure in the degradation signal domain, several probabilistic and fuzzy-inference methods will also be developed to accommodate physics based failure mechanisms and experiential and/or empirical knowledge. The resulting methods and technology will be further refined and validated using a Computer Numerical Control (CNC) machining test-rig.If successful, the results of this research will lead to a better understanding for the science of equipment prognostics (often regarded as the Achilles' heel of CBM) and a methodology to work with it in a generic framework. The generic nature of these algorithms could lead to a more widespread permeation of CBM technology, thus bringing down the cost of maintenance operations while avoiding unnecessary maintenance. Another benefit is improved availability of equipment (resulting from reduced or eliminated equipment breakdowns). The resulting methods and technology are relevant for almost all industries.

这笔赠款提供资金，用于开发必要的理论和算法，以便通过统计和计算智能方法以及传感器融合技术，为关键设备和系统的基于状态的维修建立有效的预测方法和技术。其目标是开发预测算法，以便于准确估计部件和设备的剩余使用寿命(RUL)值和置信度。信号建模和预测的预测方法将利用三个有前途的领域的现有理论：统计非线性混合效应模型、动态贝叶斯网络模型和结构学习神经网络。为了在退化信号域中准确地建立故障的定义，还将开发几种概率和模糊推理方法，以适应基于物理的故障机制以及经验和/或经验知识。如果成功，这项研究的结果将有助于更好地理解设备预测科学(通常被视为煤层气的阿喀琉斯之踵)，并建立一种在通用框架下使用设备预测的方法。这些算法的一般性可能导致更广泛地渗透煤层气技术，从而降低维护操作的成本，同时避免不必要的维护。另一个好处是提高了设备的可用性(减少或消除了设备故障)。由此产生的方法和技术几乎适用于所有行业。