Failure Detection and Prevention for Cyber-Physical Systems Using Ontology-Based Knowledge Base

Ali, Nazakat; Hong, Jang-Eui

doi:10.3390/computers7040068

Cited by 44 publications

(30 citation statements)

References 33 publications

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“…Microcracks in polymeric materials are often difficult to detect, yet they can jeopardize the device performance leading to failure. Strategies that enhance detection of damage are necessary to improve functionality, ensure safety, and boost reliability [ 438 ]. Since their inception, chromogenic-based polymeric materials have been creating a fundamental interest in the development of materials that change their color in response to an external stimulus [ 439 ].…”

Section: Sensor Building Blocksmentioning

confidence: 99%

Blood Pressure Sensors: Materials, Fabrication Methods, Performance Evaluations and Future Perspectives

Al-Qatatsheh

Morsi

Zavabeti

et al. 2020

Sensors

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Advancements in materials science and fabrication techniques have contributed to the significant growing attention to a wide variety of sensors for digital healthcare. While the progress in this area is tremendously impressive, few wearable sensors with the capability of real-time blood pressure monitoring are approved for clinical use. One of the key obstacles in the further development of wearable sensors for medical applications is the lack of comprehensive technical evaluation of sensor materials against the expected clinical performance. Here, we present an extensive review and critical analysis of various materials applied in the design and fabrication of wearable sensors. In our unique transdisciplinary approach, we studied the fundamentals of blood pressure and examined its measuring modalities while focusing on their clinical use and sensing principles to identify material functionalities. Then, we carefully reviewed various categories of functional materials utilized in sensor building blocks allowing for comparative analysis of the performance of a wide range of materials throughout the sensor operational-life cycle. Not only this provides essential data to enhance the materials’ properties and optimize their performance, but also, it highlights new perspectives and provides suggestions to develop the next generation pressure sensors for clinical use.

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Section: Sensor Building Blocksmentioning

confidence: 99%

Blood Pressure Sensors: Materials, Fabrication Methods, Performance Evaluations and Future Perspectives

Al-Qatatsheh

Morsi

Zavabeti

et al. 2020

Sensors

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“…As we see, confidence C ≥ min_confidance and support S ≥ min_support where min_confidance and min_support are the corresponding confidence and support thresholds defined by safety engineers. The Confidence C is calculated from equation 1and support S is calculated using equation (2). Support S measures how frequent a fault occurs with another fault or how often a safety guard is used to mitigate a fault in the whole causal chain history R. Confidence C defines the likeliness of occurrence of consequent fault x in hazard analysis that contains ik in the causal chain history.…”

Section: ) Relationship Between Hazard Analysis Techniquesmentioning

confidence: 99%

“…Cyber-Physical Systems (CPSs) are highly connected and massively networked systems composed of cyber (computation and networking) and physical (sensors and actuators) components that interact with each other in order to achieve the goals of, enhance reliability, efficiency, robustness, and sustainability when dealing with a specific task [1], [2]. CPSs cover autonomous and adaptive operations due to their properties of robustness and heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Safety of Collaborative Cyber-Physical Systems Considering Variability

2020

Self Cite

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Cyber-Physical System (CPS) is co-engineered interacting networks of physical and computational components that operate on different spatial and temporal scales. The safety goal of a single CPS is usually achieved by applying hazard analysis techniques and by following the standard processes defined in ISO 26262 and IEC 61508. However, the safety property may not be satisfied when multiple CPSs collaborate due to complexity, uncertainty, and variability. Therefore, a technique that would provide a hazardous-free collaboration for multiple CPSs is required to preserve sustainability. In this paper, we analyze the hazards arising due to variabilities in collaborative CPSs. We extend the hazard analysis techniques (FTA, FMEA, and ETA) to explore hazards with variability and developed a fault traceability graph from our extended techniques to trace the faults considered by multiple hazard analyses in collaborative CPSs with variability. To justify our proposed approach, a case study on the human rescue robot system was conducted to analyze hazards emerging as a result of variabilities. Finally, a tool (CPS Tracer) was developed to model the FTA, ETA, and FMEA with variability (v_FTA, v_FMEA, and v_ETA), and generates the fault traceability graph (v_FTG) that represents fault propagation route.

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“…They believe that ontologies model real-world phenomena, therefore, they are a better approach to understand the dimensions of sustainability. Authors in [12] presented an ontology-based knowledge base system for failure detection and prevention of cyber physical system. Such systems are equipped with the components for the detection and prevention of the system failure and their mitigation.Core component of such physical systems is a sensor whose failure leads to the failure of entire system.…”

Section: Ontology-based Content Management Systemsmentioning

confidence: 99%

Automatic Acquisition of Failure Mode and Effect Analysis Ontology for Sustainable Risk Management

et al. 2020

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In this piece of research, we have presented an approach to populate Failure Mode and Effect Analysis (FMEA) ontology from existing worksheets prepared by experts. FMEA is a commonly used method for risk assessment in any organization. This method is initiated by domain experts who analyze all the associated risks to a product or process, their causes, severity, effects and mitigation actions. Besides domain experts, time and cost are the other two factors involved in successful completion of FMEA. Reusability of the knowledge produced at the end of this method can bring numerous benefits to an organization. Some ontologies are available for semantic content management of FMEA knowledge but in order to avail their full benefits, it is must that they can acquire the existing knowledge automatically. Major objective of this article is to develop an algorithm, which can populate FMEA ontology from existing worksheets. Major contribution of this work is to identify an existing FMEA ontology and its evaluation for schema and relationship richness, then its automatic population using proposed algorithm without human intervention, and finally making it a part of complete knowledge management system. Our proposed algorithm correctly mapped 1357 instances to FMEA ontology from manually prepared FMEA spreadsheets. This FMEA ontology has been queried by domain experts and it was proved to be very helpful in experts like decision-making.

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Failure Detection and Prevention for Cyber-Physical Systems Using Ontology-Based Knowledge Base

Cited by 44 publications

References 33 publications

Blood Pressure Sensors: Materials, Fabrication Methods, Performance Evaluations and Future Perspectives

Blood Pressure Sensors: Materials, Fabrication Methods, Performance Evaluations and Future Perspectives

Analyzing Safety of Collaborative Cyber-Physical Systems Considering Variability

Automatic Acquisition of Failure Mode and Effect Analysis Ontology for Sustainable Risk Management

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