Real-Time Fault Detection and Diagnosis of CPS Faults in DEVS

Boi-Ukeme, Joseph; Ruiz-Martín, Cristina; Wainer, Gabriel

doi:10.1109/dependsys51298.2020.00017

Cited by 3 publications

(5 citation statements)

References 17 publications

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“…CPSs produce a huge amount of data as they include many sensors and devices for processing and communication tasks, which connect physical elements like machines with cyber-physical data-processing units [ 6 , 11 ]. In addition, they consist of several subsystems, which leads to a higher complexity of the production line itself and its data relations [ 12 , 14 ]. Moreover, the higher complexity makes CPSs more prone to faults [ 13 ].…”

Section: Requirements For Effective Fault Handlingmentioning

confidence: 99%

“…Here, we focus on cyber-physical systems (CPS) in Industry 4.0, which is also known as cyber-physical production systems (CPPS) [ 9 ]. A CPS consists of both cyber-elements, e.g., software-modules and physical-components, e.g., sensors and actuators [ 6 , 10 ] and their interaction [ 11 , 12 ]. Regarding the distribution of the term CPPS in research, we also use the broader term of a CPS focusing on production systems.…”

Section: Introductionmentioning

confidence: 99%

“…Complexity in fault detection rises in such an environment and, in addition to that, the probability of fault. In such complex systems, the probability of faults is higher [ 6 , 13 ], which leads to a more complicated and computationally intensive fault detection [ 12 ]. Therefore, the need for automated and scaleable fault handling methods gets even higher.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the need for automated and scaleable fault handling methods gets even higher. In this paper, a fault will be defined as a machine state in which a process does not perform as required or needed (e.g., [ 12 , 14 , 15 ]).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fault Handling in Industry 4.0: Definition, Process and Applications

Webert

Döß

Kaupp

et al. 2022

Sensors

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The increase of productivity and decrease of production loss is an important goal for modern industry to stay economically competitive. For that, efficient fault management and quick amendment of faults in production lines are needed. The prioritization of faults accelerates the fault amendment process but depends on preceding fault detection and classification. Data-driven methods can support fault management. The increasing usage of sensors to monitor machine health status in production lines leads to large amounts of data and high complexity. Machine Learning methods exploit this data to support fault management. This paper reviews literature that presents methods for several steps of fault management and provides an overview of requirements for fault handling and methods for fault detection, fault classification, and fault prioritization, as well as their prerequisites. The paper shows that fault prioritization lacks research about available learning methods and underlines that expert opinions are needed.

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Section: Requirements For Effective Fault Handlingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fault Handling in Industry 4.0: Definition, Process and Applications

Webert

Döß

Kaupp

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Many technologies, such as an interactive human-machine interface [8], a human digital twin [9] and a virtual-physical collaboration controller [10] have been designed for the interaction between CPSs and humans in the robotics domain. However, in comparison, despite the widespread study of the fault detection and diagnosis (FDD) problem in CPSs, most works focus solely on the development of FDD algorithms [11]- [13]. To date, limited works have proposed a comprehensive system-level CPS design that considers both physical components, simulation systems, and an interaction design specifically applied to FDD scenarios.…”

Section: Introductionmentioning

confidence: 99%

Causal Signal Temporal Logic for the Environmental Control and Life Support System’s Fault Analysis and Explanation

et al. 2023

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Modern cyber-physical systems would often fall victim to unanticipated anomalies. Humans are still required in many operations to troubleshoot and respond to such anomalies, such those in future deep space habitats. To maximize the effectiveness and efficiency of the anomaly response process, the information provided by anomaly response technologies to their human operators must be epistemically accessible or explainable. This paper offers a first step towards developing explainable anomaly response systems. It proposes a logic, Causal Signal Temporal Logic (CaSTL), which can formally describe causeeffect relationships pertaining to fault explanation. Moreover, it develops an algorithm to infer a CaSTL formula that explains why a fault has happened in a system, given the model of the system and an observation about the fault. The effectiveness of the proposed algorithm is demonstrated with a simulated Environmental Control and Life Support System (ECLSS).

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A Workflow For Data-Driven Fault Detection and Diagnosis In Buildings

Boi-Ukeme¹,

Wainer²

2021

2021 Winter Simulation Conference (WSC)

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Real-Time Fault Detection and Diagnosis of CPS Faults in DEVS

Cited by 3 publications

References 17 publications

Fault Handling in Industry 4.0: Definition, Process and Applications

Fault Handling in Industry 4.0: Definition, Process and Applications

Causal Signal Temporal Logic for the Environmental Control and Life Support System’s Fault Analysis and Explanation

A Workflow For Data-Driven Fault Detection and Diagnosis In Buildings

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