Explainable fault diagnosis for smart cities

智慧城市的可解释故障诊断

• 批准号: 443128409
• 负责人: Professor Dr.-Ing. Kay Smarsly
• 金额: --
• 依托单位: Lehrstuhl Mathematik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/443128409?language=en
• 关键词: Explainable; fault; diagnosis; smart; cities

Smart cities are based on wireless sensor networks. Faults and miscalibrations in wireless sensor networks, if undetected, may degrade the quality of "big data" collected for autonomous decision making, which is imperative in smart city applications. The need for reliable fault diagnosis is particularly prominent in smart infrastructure, which is an essential component of smart cities. Smart infrastructure is equipped with wireless sensor networks that autonomously collect, analyze, and communicate structural data, referred to as "smart monitoring". Although fault diagnosis concepts are not new in related research areas, these concepts have not kept pace with the ongoing "smartification" and cannot be adapted to smart infrastructure. This project aims at developing a fault diagnosis framework for wireless sensor networks deployed in smart infrastructure. Unlike analytical redundancy approaches that are usually used to achieve fault tolerance in distributed systems, this project proposes a new methodology based on artificial intelligence (AI). The novelty of the AI-based framework is a strong mathematical formulation of a deep learning concept proposed for distributedly embedding convolutional neural networks in wireless sensor networks. In addition to the decentralization itself, the limited energy and computing resources of wireless sensor nodes are also considered. Moreover, a generally valid classification-based mathematical formulation of the fault diagnosis problem is introduced. One of the key advantages of the classification-based fault diagnosis problem formulation is the absence of analytical redundancy requirements by shifting the fault diagnosis problem to the mathematical features inherent in sensor data. Propelled by the lack of trust in AI algorithms that are black-box by nature, the AI-based fault diagnosis framework is complemented by an explanation interface based on the classification-based mathematical formulation, thus adding transparency to the AI-based fault diagnosis framework. Finally, the fault diagnosis framework is verified and validated by means of a dual verification and validation strategy that builds upon the results of a DFG research training group at Bauhaus University Weimar, using experimental laboratory tests as well as structural data recorded from a real-world railway bridge in operation. Through the AI algorithms of the fault diagnosis framework, being explainable and transparent to engineers, it is expected that smart infrastructure will be enabled to reliably self-detect sensor faults and sensor miscalibrations – without the need for multiple redundant sensors, first-principle models (such as finite element models), or a priori knowledge on the physical principles of smart infrastructure. As a result, the dependability and the accuracy of autonomous decision making in smart infrastructure will be enhanced, thus facilitating reduced maintenance and operation costs in smart cities.

智慧城市是基于无线传感器网络的。无线传感器网络中的故障和误校准如果未被检测到，可能会降低为自主决策收集的“大数据”的质量，这在智慧城市应用中是必不可少的。可靠的故障诊断需求在智能基础设施中尤为突出，这是智能城市的重要组成部分。智能基础设施配备了无线传感器网络，可以自主收集，分析和传输结构数据，称为“智能监控”。虽然故障诊断概念在相关研究领域并不新鲜，但这些概念没有跟上正在进行的“智能化”的步伐，无法适应智能基础设施。本计画旨在发展一个可应用于智慧型基础设施的无线感测网路故障诊断架构。与通常用于在分布式系统中实现容错的分析冗余方法不同，该项目提出了一种基于人工智能（AI）的新方法。基于AI的框架的新奇是深度学习概念的强大数学公式，该概念被提出用于在无线传感器网络中分布式嵌入卷积神经网络。除了分散性本身，无线传感器节点的能量和计算资源的有限性也被考虑。此外，一般有效的分类为基础的故障诊断问题的数学公式。基于分类的故障诊断问题公式化的关键优点之一是通过将故障诊断问题转移到传感器数据中固有的数学特征来消除分析冗余要求。由于对本质上是黑箱的AI算法缺乏信任，基于AI的故障诊断框架由基于分类的数学公式的解释接口补充，从而增加了基于AI的故障诊断框架的透明度。最后，故障诊断框架进行了验证和确认的双重验证和确认策略，建立在一个DFG的研究培训小组在包豪斯大学魏玛的结果，使用实验室测试以及结构数据记录从现实世界中的铁路桥梁运行。通过故障诊断框架的人工智能算法，对工程师来说是可解释和透明的，预计智能基础设施将能够可靠地自我检测传感器故障和传感器失调-而不需要多个冗余传感器，第一原理模型（如有限元模型）或关于智能基础设施物理原理的先验知识。因此，智能基础设施中自主决策的可靠性和准确性将得到提高，从而有助于降低智能城市的维护和运营成本。