Abstract
Cyber-physical systems (CPS) are paving new ground with increasing levels of automation and usage in applications with complex environments, posing greater challenges in terms of safety and reliability. The increasing complexity of CPS environments, tasks, and systems leads to more uncertainties. Unless properly managed, these uncertainties may lead to false detection of real fault condition of a system, which in turn may affect decision making and potentially cause fatal consequences. In order to implement safety-critical missions, such as autonomous driving, it is essential to develop a reliable monitoring and assessment service dealing with the complexity and uncertainty issues. In this article, we propose a fault detection function based on Bayesian inference, which combines empirical knowledge with information of the specific system. By considering uncertainties as possible causes for false detection, various uncertainties during the detection process are analyzed, and the ways to quantify and propagate them are explored. As a result, probabilistic inference is achieved for distinguishing system faults from uncertainties, which contributes to more reliable detection results regarding system faults under dynamically changing environments. A case study on an microelectro mechanical system (MEMS) accelerometer is conducted and the result shows that the fault detection function effectively distinguishes system faults and uncertainties arising from the environment.
Highlights
C YBER-PHYSICAL systems (CPS) involve computation, communication, sensing, and actuation, as well as physical processes [1]
Based on the proposed uncertainty analysis framework, we develop a fault detection function in monitoring and assessment service (MAS) for CPS, which provides probabilistic inference regarding system fault condition based on Bayesian inference (BI)
We have addressed the challenges of fault detection in CPS in the presence of uncertainties
Summary
C YBER-PHYSICAL systems (CPS) involve computation, communication, sensing, and actuation, as well as physical processes [1]. When MAS fails to detect the real fault conditions, a failure of this service occurs, which potentially results into other system failure and influences system safety and reliability. There are various uncertainties that influence fault detection results, for example, environment noise, sensing system failure, lacking of model knowledge, etc These uncertainties may lead to false detection of the real fault conditions, potentially posing risks to personal safety and finances. An implication is that erroneous decision could be made to perform offline maintenance based on false fault detection, with potentially large financial loss for a large product line In these situations, the reliability of the detection result is of great importance in the CPS development. Based on the proposed uncertainty analysis framework, we develop a fault detection function in MAS for CPS, which provides probabilistic inference regarding system fault condition based on Bayesian inference (BI).
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