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Knowledge-based architecture for integrated condition based maintenance of engineering systems.

机译：基于知识的体系结构，用于基于条件的工程系统维护。

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摘要

A paradigm shift is emerging in system reliability and maintainability. The military and industrial sectors are moving away from the traditional breakdown and scheduled maintenance to adopt concepts referred to as Condition Based Maintenance (CBM) and Prognostic Health Management (PHM). In addition to signal processing and subsequent diagnostic and prognostic algorithms these new technologies involve storage of large volumes of both quantitative and qualitative information to carry out maintenance tasks effectively. This not only requires research and development in advanced technologies but also the means to store, organize and access this knowledge in a timely and efficient fashion. Knowledge-based expert systems have been shown to possess capabilities to manage vast amounts of knowledge, but an intelligent systems approach calls for attributes like learning and adaptation in building autonomous decision support systems.; This research presents an integrated knowledge-based approach to diagnostic reasoning for CBM of engineering systems. A two level diagnosis scheme has been conceptualized in which first a fault is hypothesized using the observational symptoms from the system and then a more specific diagnostic test is carried out using only the relevant sensor measurements to confirm the hypothesis. Utilizing the qualitative (textual) information obtained from these systems in combination with quantitative (sensory) information reduces the computational burden by carrying out a more informed testing. An Industrial Language Processing (ILP) technique has been developed for processing textual information from industrial systems. Compared to other automated methods that are computationally expensive, this technique manipulates standardized language messages by taking advantage of their semi-structured nature and domain limited vocabulary in a tractable manner.; A Dynamic Case-based reasoning (DCBR) framework provides a hybrid platform for diagnostic reasoning and an integration mechanism for the operational infrastructure of an autonomous Decision Support System (DSS) for CBM. This integration involves data gathering, information extraction procedures, and real-time reasoning frameworks to facilitate the strategies and maintenance of critical systems. As a step further towards autonomy, DCBR builds on a self-evolving knowledgebase that learns from its performance feedback and reorganizes itself to deal with non-stationary environments. A unique Human-in-the-Loop Learning (HITLL) approach has been adopted to incorporate human feedback in the traditional Reinforcement Learning (RL) algorithm.
机译：系统可靠性和可维护性正在出现范式转变。军事和工业部门正在从传统的故障和定期维护转移到采用基于状态维护（CBM）和预后健康管理（PHM）的概念。除了信号处理以及后续的诊断和预后算法外，这些新技术还涉及存储大量的定量和定性信息，以有效地执行维护任务。这不仅需要研究和开发先进技术，还需要及时有效地存储，组织和访问这些知识的手段。以知识为基础的专家系统已被证明具有管理大量知识的能力，但是智能系统方法要求在建立自主决策支持系统中需要学习和适应等属性。这项研究提出了一种基于知识的集成方法来对工程系统的煤层气进行诊断推理。已经提出了两级诊断方案，其中首先使用来自系统的观察症状来假设故障，然后仅使用相关的传感器测量值来进行更具体的诊断测试以确认假设。通过将从这些系统中获得的定性（文本）信息与定量（感官）信息结合使用，可以通过进行更明智的测试来减轻计算负担。已经开发了一种工业语言处理（ILP）技术来处理来自工业系统的文本信息。与其他在计算上昂贵的自动化方法相比，该技术通过利用标准语言消息的半结构化性质和领域受限的词汇以易于处理的方式来对其进行操作。基于动态案例的推理（DCBR）框架提供了用于诊断推理的混合平台和用于CBM的自主决策支持系统（DSS）的操作基础架构的集成机制。这种集成涉及数据收集，信息提取过程和实时推理框架，以促进关键系统的策略和维护。作为朝着自治迈进的一步，DCBR建立在一个不断发展的知识库上，该知识库从其性能反馈中学习，并重新组织以应对非平稳环境。已经采用了独特的“在环学习”（HITLL）方法，将人的反馈纳入传统的“强化学习”（RL）算法中。

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  • 作者

    Saxena, Abhinav.;

  • 作者单位

    Georgia Institute of Technology.;

  • 授予单位 Georgia Institute of Technology.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2007
  • 页码 201 p.
  • 总页数 201
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;
  • 关键词

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