Explainable fault diagnosis for smart cities
Research problem statement
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 to develop 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. The main concept is shown in Figure 1. 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.