A Graph-Based Fault Identification and Propagation Framework for Functional Design of Complex Systems

2008 ◽  
Vol 130 (5) ◽  
Author(s):  
Tolga Kurtoglu ◽  
Irem Y. Tumer
Keyword(s):  
Qualitative Reasoning ◽  
Fault Identification ◽  
Critical Event ◽  
Functional Design ◽  
Fault Propagation ◽  
Functional Failure ◽  
Physical Systems ◽  
Failure Risk ◽  
Behavioral Simulation ◽  
Novel Approach

In this paper, the functional-failure identification and propagation (FFIP) framework is introduced as a novel approach for evaluating and assessing functional-failure risk of physical systems during conceptual design. The task of FFIP is to estimate potential faults and their propagation paths under critical event scenarios. The framework is based on combining hierarchical system models of functionality and configuration, with behavioral simulation and qualitative reasoning. The main advantage of the method is that it allows the analysis of functional failures and fault propagation at a highly abstract system concept level before any potentially high-cost design commitments are made. As a result, it provides the designers and system engineers with a means of designing out functional failures where possible and designing in the capability to detect and mitigate failures early on in the design process. Application of the presented method to a fluidic system example demonstrates these capabilities.

A Graph-Based Framework for Early Assessment of Functional Failures in Complex Systems

2007 ◽  
Author(s):  
Tolga Kurtoglu ◽  
Irem Y. Tumer
Keyword(s):  
Design Process ◽  
Qualitative Reasoning ◽  
Critical Event ◽  
Fault Propagation ◽  
Early Assessment ◽  
Functional Failure ◽  
Physical Systems ◽  
Failure Risk ◽  
Behavioral Simulation ◽  
Novel Approach

In this paper, the Functional Failure Identification and Propagation (FFIP) framework is introduced as a novel approach for evaluating and assessing functional failure risk of physical systems during conceptual design. The task of FFIP is to estimate potential faults and their propagation paths under critical event scenarios. The framework is based on combining hierarchical system models of functionality and configuration, with behavioral simulation and qualitative reasoning. The main advantage of the method is that it allows the analysis of functional failures and fault propagation at a highly abstract system concept level before any potentially high-cost design commitments are made. As a result, it provides the designers and system engineers with a means of designing out functional failures where possible and designing in the capability to detect and mitigate failures early on in the design process. Application of the presented method to a fluidic system example demonstrates these capabilities.

A Framework for Building Behavioral Models for Design-Stage Failure Identification Using Dimensional Analysis

2010 ◽  
Author(s):  
Eric Coatane´a ◽  
Tuomas Ritola ◽  
Irem Y. Tumer ◽  
David Jensen
Keyword(s):  
Dimensional Analysis ◽  
Behavioral Model ◽  
Qualitative Reasoning ◽  
Design Stage ◽  
Critical Event ◽  
Behavioral Models ◽  
New Approach ◽  
Design Variables ◽  
Qualitative Physics ◽  
Failure Identification

In this paper, a design-stage failure identification framework is proposed using a modeling and simulation approach based on Dimensional Analysis and qualitative physics. The proposed framework is intended to provide a new approach to model the behavior in the Functional-Failure Identification and Propagation (FFIP) framework, which estimates potential faults and their propagation paths under critical event scenarios. The initial FFIP framework is based on combining hierarchical system models of functionality and configuration, with behavioral simulation and qualitative reasoning. This paper proposes to develop a behavioral model derived from information available at the configuration level. Specifically, the new behavioral model uses design variables, which are associated with units and quantities (i.e., Mass, Length, Time, etc…). The proposed framework continues the work to allow the analysis of functional failures and fault propagation at a highly abstract system concept level before any potentially high-cost design commitments are made. The main contribution in this paper consists of developing component behavioral models based on the combination of fundamental design variables used to describe components and their units or quantities, more precisely describing components’ behavior.

A Novel Approach for Deploying Minimum Sensors in Smart Buildings

2022 ◽  
Vol 6 (1) ◽  
pp. 1-29
Author(s):  
Anshul Agarwal ◽  
Krithi Ramamritham
Keyword(s):  
Real World ◽  
Cyber Physical Systems ◽  
Management Systems ◽  
Physical Systems ◽  
Optimization Framework ◽  
Sensing System ◽  
Novel Approach ◽  
Building Management ◽  
Minimum Number ◽  
Building Management Systems

Buildings, viewed as cyber-physical systems, become smart by deploying Building Management Systems (BMS). They should be aware about the state and environment of the building. This is achieved by developing a sensing system that senses different interesting factors of the building, called as “facets of sensing.” Depending on the application, different facets need to be sensed at various locations. Existing approaches for sensing these facets consist of deploying sensors at all the places so they can be sensed directly. But installing numerous sensors often aggravate the issues of user inconvenience, cost of installation and maintenance, and generation of e-waste. This article proposes how intelligently using the existing information can help to estimate the facets in cyber-physical systems like buildings, thereby reducing the sensors to be deployed. In this article, an optimization framework has been developed, which optimally deploys sensors in a building such that it satisfies BMS requirements with minimum number of sensors. The proposed solution is applied to real-world scenarios with cyber-physical systems. The results indicate that the proposed optimization framework is able to reduce the number of sensors by 59% and 49% when compared to the baseline and heuristic approach, respectively.

scholarly journals Predictive Monitoring with Logic-Calibrated Uncertainty for Cyber-Physical Systems

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-25
Author(s):  
Meiyi Ma ◽  
John Stankovic ◽  
Ezio Bartocci ◽  
Lu Feng
Keyword(s):  
Neural Networks ◽  
Real World ◽  
Recurrent Neural Networks ◽  
Cyber Physical Systems ◽  
Uncertainty Estimation ◽  
Physical Systems ◽  
Confidence Levels ◽  
Novel Approach ◽  
Infinite Set

Predictive monitoring—making predictions about future states and monitoring if the predicted states satisfy requirements—offers a promising paradigm in supporting the decision making of Cyber-Physical Systems (CPS). Existing works of predictive monitoring mostly focus on monitoring individual predictions rather than sequential predictions. We develop a novel approach for monitoring sequential predictions generated from Bayesian Recurrent Neural Networks (RNNs) that can capture the inherent uncertainty in CPS, drawing on insights from our study of real-world CPS datasets. We propose a new logic named Signal Temporal Logic with Uncertainty (STL-U) to monitor a flowpipe containing an infinite set of uncertain sequences predicted by Bayesian RNNs. We define STL-U strong and weak satisfaction semantics based on whether all or some sequences contained in a flowpipe satisfy the requirement. We also develop methods to compute the range of confidence levels under which a flowpipe is guaranteed to strongly (weakly) satisfy an STL-U formula. Furthermore, we develop novel criteria that leverage STL-U monitoring results to calibrate the uncertainty estimation in Bayesian RNNs. Finally, we evaluate the proposed approach via experiments with real-world CPS datasets and a simulated smart city case study, which show very encouraging results of STL-U based predictive monitoring approach outperforming baselines.

scholarly journals Hardware-in-the-loop test based non-intrusive diagnostics of cyber-physical systems using microcontroller debug ports

ACTA IMEKO ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 27
Author(s):  
Balázs Scherer
Keyword(s):  
Measurement Method ◽  
Internal State ◽  
Physical World ◽  
Cyber Physical Systems ◽  
Hardware In The Loop ◽  
Access Port ◽  
Physical Systems ◽  
Testing Phase ◽  
Software Interface ◽  
Novel Approach

<p class="Abstract"><span lang="EN-US">Cyber-physical systems have extensive contact with the physical world. Usually during the development of these systems, the testing phase cannot be done efficiently or safely in the complete real environment, and therefore HIL (Hardware In the Loop) simulators are used. During HIL testing, diagnostic protocols are used very often to gather detailed information about the DUT’s (Device Under Test) internal state. Diagnostic protocols are very useful during testing, but they cause a significant load to the DUT. This paper introduces a novel approach to replace traditional diagnostic protocols with a non-intrusive solution. The presented method is based on the debug capabilities of modern ARM Cortex M core microcontroller, and uses a CMSIS-DAP (Cortex Microcontroller Software Interface Standard Debug - Access Port) based interface. This paper also introduces a solution to integrate this non-intrusive measurement method to NI LabVIEW based test environments and NI VeriStand based HIL simulations. </span></p>

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