Model-free condition monitoring with confidence

Götzinger, Maximilian; TaheriNejad, Nima; Kholerdi, Hedyeh A.; Jantsch, Axel; Willegger, Edwin; Glatzl, Thomas; Rahmani, Amir M.; Sauter, Thilo; Liljeberg, Pasi

doi:10.1080/0951192x.2019.1605201

Cited by 8 publications

(5 citation statements)

References 22 publications

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“…Content may change prior to final publication. (see [29]- [31]). Each new signal sample is compared with historical information to detect if the signal changed state.…”

Section: ) Modeling the Monitoring Systemmentioning

confidence: 98%

“…The monitoring system adapts to any SuO based on contextual information only (see context-awareness). We have performed experiments with two variants of the system: (i) Context-aware Health Monitoring (CAH) [29], [30] applies a threshold-based decision-making process and (ii) Confidence-based Context-Aware condition Monitoring (CCAM) [31] makes decisions based on confidence.…”

Section: ) Background and Problem Statementmentioning

confidence: 99%

“…While a selfawareness property has some fundamental characteristic, a corresponding functionality may be implemented in different ways. For example, while confidence is a measure of how trustworthy the work of a task, part of the system, or the entire system is (Section VI-C), it may be calculated in many different ways [100] (see for example [31], [55], [101]). What interface to use for a self-awareness property depends on the actual usage.…”

Section: A Modeling Self-awarenessmentioning

confidence: 99%

“…This paper provides an overview of the framework, how it works and how it can be used. The applicability and flexibility of RoSA are demonstrated by two case studies from the fields of human health monitoring [26]- [28] and industrial machine monitoring [29]- [31]. The main contributions of the paper are:…”

Section: Introductionmentioning

confidence: 99%

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RoSA: A Framework for Modeling Self-Awareness in Cyber-Physical Systems

et al. 2020

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The role of smart and autonomous systems is becoming vital in many areas of industry and society. Expectations from such systems continuously rise and become more ambitious: long lifetime, high reliability, high performance, energy efficiency, and adaptability, particularly in the presence of changing environments. Computational self-awareness promises a comprehensive assessment of the system state for sensible and well-informed actions and resource management. Computational self-awareness concepts can be used in many applications such as automated manufacturing plants, telecommunication systems, autonomous driving, traffic control, smart grids, and wearable health monitoring systems. Developing self-aware systems from scratch for each application is the most common practice currently, but this is highly redundant, inefficient, and uneconomic. Hence, we propose a framework that supports modeling and evaluation of various self-aware concepts in hierarchical agent systems, where agents are made up of self-aware functionalities. This paper presents the Research on Self-Awareness (RoSA) framework and its design principles. In addition, self-aware functionalities abstraction, data reliability, and confidence, which are currently provided by RoSA, are described. Potential use cases of RoSA are discussed. Capabilities of the proposed framework are showcased by case studies from the fields of healthcare and industrial monitoring. We believe that RoSA is capable of serving as a common framework for self-aware modeling and applications and thus helps researchers and engineers in exploring the vast design space of hierarchical agent-based systems with computational self-awareness.

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“…Content may change prior to final publication. (see [29]- [31]). Each new signal sample is compared with historical information to detect if the signal changed state.…”

Section: ) Modeling the Monitoring Systemmentioning

confidence: 98%

Section: ) Background and Problem Statementmentioning

confidence: 99%

Section: A Modeling Self-awarenessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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RoSA: A Framework for Modeling Self-Awareness in Cyber-Physical Systems

et al. 2020

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“…In 2016, TaheriNejad et al published a paper [29] which highlighted this aspect and elaborated on different elements of observation and their potential effect on self-awareness and the overall performance of the system. Since then, several publications have appeared in the literature which demonstrated this effect in various applications [13,[26][27][28][30][31][32][33].…”

Section: Background and Related Workmentioning

confidence: 99%

Confidence-Enhanced Early Warning Score Based on Fuzzy Logic

et al. 2019

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Cardiovascular diseases are one of the world's major causes of loss of life. The vital signs of a patient can indicate this up to 24 hours before such an incident happens. Healthcare professionals use Early Warning Score (EWS) as a common tool in healthcare facilities to indicate the health status of a patient. However, the chance of survival of an outpatient could be increased if a mobile EWS system would monitor them during their daily activities to be able to alert in case of danger. Because of limited healthcare professional supervision of this health condition assessment, a mobile EWS system needs to have an acceptable level of reliability-even if errors occur in the monitoring setup such as noisy signals and detached sensors. In earlier works, a data reliability validation technique has been presented that gives information about the trustfulness of the calculated EWS. In this paper, we propose an EWS system enhanced with the self-aware property confidence, which is based on fuzzy logic. In our experiments, we demonstrate that-under adverse monitoring circumstances (such as noisy signals, detached sensors, and non-nominal monitoring conditions)-our proposed Self-Aware Early Warning Score (SA-EWS) system provides a more reliable EWS than an EWS system without self-aware properties.

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