Toward Digital Twin for Cyber Physical Production Systems Maintenance: Observation Framework Based on Artificial Intelligence Techniques

2021 ◽  
pp. 123-134
Author(s):  
Farah Abdoune ◽  
Maroua Nouiri ◽  
Pierre Castagna ◽  
Olivier Cardin
Keyword(s):  
Artificial Intelligence ◽  
Production Systems ◽  
Artificial Intelligence Techniques ◽  
Digital Twin ◽  
Systems Maintenance ◽  
Cyber Physical Production Systems

scholarly journals Sustainable Industry 4.0 Wireless Networks, Machine Learning Algorithms, and Internet of Things-based Real-Time Production Logistics in Digital Twin-driven Smart Manufacturing

2021 ◽  
Vol 129 ◽  
pp. 04003
Author(s):  
Elvira Nica ◽  
Gheorghe H. Popescu ◽  
George Lăzăroiu
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Internet Of Things ◽  
Real Time ◽  
Industry 4.0 ◽  
Production Systems ◽  
Smart Manufacturing ◽  
Digital Twin ◽  
Cyber Physical Production Systems

Research background: The aim of this paper is to synthesize and analyze existing evidence on artificial intelligence-based decision-making algorithms, industrial big data, and Internet of Things sensing networks in digital twin-driven smart manufacturing. Purpose of the article: Using and replicating data from Altair, Catapult, Deloitte, DHL, GAVS, PwC, and ZDNet we performed analyses and made estimates regarding cyber-physical system-based real-time monitoring, product decision-making information systems, and artificial intelligence data-driven Internet of Things systems in digital twin-based cyber-physical production systems. Methods: From the completed surveys, we calculated descriptive statistics of compiled data when appropriate. The data was weighted in a multistep process that accounts for multiple stages of sampling and nonresponse that occur at different points in the survey process. The precision of the online polls was measured using a Bayesian credibility interval. To ensure high-quality data, data quality checks were performed to identify any respondents showing clear patterns of satisficing. Test data was populated and analyzed in SPSS to ensure the logic and randomizations were working as intended before launching the survey. An Internet-based survey software program was utilized for the delivery and collection of responses. The sample weighting was accomplished using an iterative proportional fitting process that simultaneously balanced the distributions of all variables. The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau’s American Community Survey to reflect reliably and accurately the demographic composition of the United States. Confirmatory factor analysis was employed to test for the reliability and validity of measurement instruments. Findings & Value added: The way Internet of Things-based decision support systems, artificial intelligence-driven big data analytics, and robotic wireless sensor networks configure digital twin-driven smart manufacturing and cyber-physical production systems in sustainable Industry 4.0.

scholarly journals CAAI—a cognitive architecture to introduce artificial intelligence in cyber-physical production systems

2020 ◽  
Vol 111 (1-2) ◽  
pp. 609-626 ◽  
Author(s):  
Andreas Fischbach ◽  
Jan Strohschein ◽  
Andreas Bunte ◽  
Jörg Stork ◽  
Heide Faeskorn-Woyke ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Modular Design ◽  
Production Systems ◽  
Cognitive Architecture ◽  
Performance Criteria ◽  
Data Platform ◽  
Constant Observation ◽  
Cyber Physical Production Systems ◽  
The Individual

Abstract This paper introduces CAAI, a novel cognitive architecture for artificial intelligence in cyber-physical production systems. The goal of the architecture is to reduce the implementation effort for the usage of artificial intelligence algorithms. The core of the CAAI is a cognitive module that processes the user’s declarative goals, selects suitable models and algorithms, and creates a configuration for the execution of a processing pipeline on a big data platform. Constant observation and evaluation against performance criteria assess the performance of pipelines for many and different use cases. Based on these evaluations, the pipelines are automatically adapted if necessary. The modular design with well-defined interfaces enables the reusability and extensibility of pipeline components. A big data platform implements this modular design supported by technologies such as Docker, Kubernetes, and Kafka for virtualization and orchestration of the individual components and their communication. The implementation of the architecture is evaluated using a real-world use case. The prototypic implementation is accessible on GitHub and contains a demonstration.

scholarly journals Cognitive capabilities for the CAAI in cyber-physical production systems

2021 ◽  
Author(s):  
Jan Strohschein ◽  
Andreas Fischbach ◽  
Andreas Bunte ◽  
Heide Faeskorn-Woyke ◽  
Natalia Moriz ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Production Systems ◽  
Cognitive Architecture ◽  
Use Case ◽  
Test Functions ◽  
Model Quality ◽  
Implementation Effort ◽  
Data Platform ◽  
Cyber Physical Production Systems ◽  
The Individual

AbstractThis paper presents the cognitive module of the Cognitive Architecture for Artificial Intelligence (CAAI) in cyber-physical production systems (CPPS). The goal of this architecture is to reduce the implementation effort of artificial intelligence (AI) algorithms in CPPS. Declarative user goals and the provided algorithm-knowledge base allow the dynamic pipeline orchestration and configuration. A big data platform (BDP) instantiates the pipelines and monitors the CPPS performance for further evaluation through the cognitive module. Thus, the cognitive module is able to select feasible and robust configurations for process pipelines in varying use cases. Furthermore, it automatically adapts the models and algorithms based on model quality and resource consumption. The cognitive module also instantiates additional pipelines to evaluate algorithms from different classes on test functions. CAAI relies on well-defined interfaces to enable the integration of additional modules and reduce implementation effort. Finally, an implementation based on Docker, Kubernetes, and Kafka for the virtualization and orchestration of the individual modules and as messaging technology for module communication is used to evaluate a real-world use case.

scholarly journals An architecture of an Intelligent Digital Twin in a Cyber-Physical Production System

2019 ◽  
Vol 67 (9) ◽  
pp. 762-782 ◽  
Author(s):  
Behrang Ashtari Talkhestani ◽  
Tobias Jung ◽  
Benjamin Lindemann ◽  
Nada Sahlab ◽  
Nasser Jazdi ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Production Systems ◽  
Heterogeneous Data ◽  
Use Cases ◽  
Added Value ◽  
Automation System ◽  
The Real ◽  
Digital Twin ◽  
Digital Twins ◽  
Cyber Physical Production Systems

Abstract The role of a Digital Twin is increasingly discussed within the context of Cyber-Physical Production Systems. Accordingly, various architectures for the realization of Digital Twin use cases are conceptualized. There lacks, however, a clear, encompassing architecture covering necessary components of a Digital Twin to realize various use cases in an intelligent automation system. In this contribution, the added value of a Digital Twin in an intelligent automation system is highlighted and various existing definitions and architectures of the Digital Twin are discussed. Flowingly, an architecture for a Digital Twin and an architecture for an Intelligent Digital Twin and their required components are proposed, with which use cases such as plug and produce, self-x and predictive maintenance are enabled. In the opinion of the authors, a Digital Twin requires three main characteristics: synchronization with the real asset, active data acquisition from the real environment and the ability of simulation. In addition to all the characteristics of a Digital Twin, an Intelligent Digital Twin must also include the characteristics of Artificial Intelligence. The Intelligent Digital Twin can be used for the realization of the autonomous Cyber-Physical Production Systems. In order to realize the proposed architecture for a Digital Twin, several methods, namely the Anchor-Point-Method, a method for heterogeneous data acquisition and data integration as well as an agent-based method for the development of a co-simulation between Digital Twins were implemented and evaluated.

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