Towards Data-Driven Reliability Modeling for Cyber-Physical Production Systems

Friederich, Jonas; Lazarova-Molnar, Sanja

doi:10.1016/j.procs.2021.03.073

Cited by 24 publications

(6 citation statements)

References 23 publications

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“…In the latter, system data are obtained in real time and used to continuously update and optimize the system representation so that it better reflects the system current state. They have been employed in the analysis of a production plant to provide decision support for maintenance optimization [30], [31] and for the analysis of an integrated energy system [32]. Static data-driven methods use qualitative or quantitative historical data of a system to create reliability models and indexes to support decision making.…”

Section: Literature Reviewmentioning

confidence: 99%

Flow-Network-Based Method for the Reliability Analysis of Decentralized Power System Topologies With a Sequential Monte-Carlo Simulation

Breve,

Bohnet,

Michalke

et al. 2024

IEEE Trans. Rel.

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This article introduces a sequential Monte-Carlo simulation method (MCS) based on flow networks to represent decentralized power systems and evaluate the reliability and availability of their topologies. It can be used to provide a comparison basis during the system design phase. The method can be employed to simulate a variety of types of decentralized local power systems. It is able to simulate bidirectional, radial, and meshed systems with both repairable and nonrepairable elements. Examples of elements that can be simulated include power sources, loads, and energy-storage units. The determination of system success is performed using a minimum-cost-maximum-flow graph algorithm that calculates the power delivered to the system loads given the state of the system components. If the loads are being properly supplied, the system is deemed functional. The simulation can accommodate additional sets of rules for system success if needed. The method can generate a variety of indices for system availability and reliability based on the system topology and the parameters of its individual components. Three example systems are presented and simulated to showcase the method's capabilities.

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Section: Literature Reviewmentioning

confidence: 99%

Flow-Network-Based Method for the Reliability Analysis of Decentralized Power System Topologies With a Sequential Monte-Carlo Simulation

Breve,

Bohnet,

Michalke

et al. 2024

IEEE Trans. Rel.

View full text Add to dashboard Cite

show abstract

“…Primarily, it happens because it is difficult to model and predict human behavior. Humans may be stressed, get tired, make false estimations, and as a result may cause a decline in the reliability of manufacturing systems [8,9,27]. The topic of the impact of humans on the reliability of manufacturing systems should be studied extensively, especially for Industry 5.0 systems, where the core of this era will be the symbiosis and coexistence of humans and machines.…”

Section: Reliability In Manufacturing Systemsmentioning

confidence: 99%

“…Additionally, these methods are conducted offline, which does not give the ability to assess the reliability of robots in real time. For this reason, engineers are moving toward data-driven approaches and artificial intelligence (AI) techniques for reliability assessment, leveraging the huge amount of condition monitoring data produced every day [9].…”

Section: Introductionmentioning

confidence: 99%

Robotic Cell Reliability Optimization Based on Digital Twin and Predictive Maintenance

2023

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Robotic systems have become a standard tool in modern manufacturing due to their unique characteristics, such as repeatability, precision, and speed, among others. One of the main challenges of robotic manipulators is the low degree of reliability. Low reliability increases the probability of disruption in manufacturing processes, minimizing in this way the productivity and by extension the profit of the company. To address the abovementioned challenges, this research work proposes a robotic cell reliability optimization method based on digital twin and predictive maintenance. Concretely, the simulation of the robot is provided, and emphasis is given to the reliability optimization of the robotic cell’s critical component. A supervised machine learning model is trained, aiming to detect and classify the faulty behavior of the critical component. Furthermore, a framework is proposed for the remaining useful life prediction with the aim to improve the reliability of the robotic cell. Thus, following the results of the current research work, appropriate maintenance tasks can be applied, preventing the robotic cell from serious failures and ensuring high reliability.

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“…As the beneficial effects of sharing and collaborating in data processing and analytics are apparent, 8,9 data-driven reliability analysis 10,11 via fault trees is not an exception. Actually, meters and sensors facilitate collection of large amounts of data that is utilized for different purposes, such as fault discovery and diagnosis.…”

Section: Background and Related Workmentioning

confidence: 99%

Collaborative data-driven reliability analysis of multi-state fault trees

Niloofar

Lazarova-Molnar

2022

Proceedings of the Institution of Mechanical Engineers, Part O:

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Fault tree modeling and failure analysis of systems that are equipped with sensors and meters are becoming more automated and less human-dependent. For a single system to benefit from its own collected data, it will need to wait for a long time to collect sufficient data to build representative models to increase its reliability. Therefore, if multiple systems with similar functionalities cooperate, the resolution of the collected data will increase. This leads to extracting fault trees with higher accuracy in failure detection and prediction. In this paper, we present an extended approach for collaborative Data-Driven Fault Tree Analysis (DDFTA) of a system which extracts repairable fault trees from time series data streaming from multiple systems/machines sharing similar functionalities. Results are analyzed to estimate the system’s reliability measures and investigate the effect of number of machines cooperating in data collection. Our method is not limited to binary (two states) components, nor to exponential distributions. Results show that applying collaborative data analytics significantly increases the accuracy of data-driven fault tree analysis, specifically for systems following nonexponential distributions.

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Towards Data-Driven Reliability Modeling for Cyber-Physical Production Systems

Cited by 24 publications

References 23 publications

Flow-Network-Based Method for the Reliability Analysis of Decentralized Power System Topologies With a Sequential Monte-Carlo Simulation

Flow-Network-Based Method for the Reliability Analysis of Decentralized Power System Topologies With a Sequential Monte-Carlo Simulation

Robotic Cell Reliability Optimization Based on Digital Twin and Predictive Maintenance

Collaborative data-driven reliability analysis of multi-state fault trees

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