scholarly journals Data Mining Methodology for Engineering Applications (DMME)—A Holistic Extension to the CRISP-DM Model

2019 ◽  
Vol 9 (12) ◽  
pp. 2407 ◽  
Author(s):  
Hajo Wiemer ◽  
Lucas Drowatzky ◽  
Steffen Ihlenfeldt
Keyword(s):  
Experimental Data ◽  
Data Mining ◽  
Data Acquisition ◽  
Data Analytics ◽  
Production Systems ◽  
Industrial Applications ◽  
Engineering Applications ◽  
Collaborative Project ◽  
Cyber Physical Production Systems

The value of data analytics is fundamental in cyber-physical production systems for tasks like optimization and predictive maintenance. The de facto standard for conducting data analytics in industrial applications is the CRISP-DM methodology. However, CRISP-DM does not specify a data acquisition phase within production scenarios. With this chapter, we present DMME as an extension to the CRISP-DM methodology specifically tailored for engineering applications. It provides a communication and planning foundation for data analytics within the manufacturing domain, including the design and evaluation of the infrastructure for process-integrated data acquisition. In addition, the methodology includes functions of design of experiments capabilities to systematically and efficiently identify relevant interactions. The procedure of DMME methodology is presented in detail and an example project illustrates the workflow. This case study was part of a collaborative project with an industrial partner who wanted an application to detect marginal lubrication in linear guideways of a servo-driven axle based only on data from the drive controller. Decision trees detect the lubrication state, which are trained with experimental data. Several experiments, taking the lubrication state, velocity, and load on the slide into account, provide the training and test datasets.

scholarly journals QHAR: Q-Holonic-Based ARchitecture for Self-Configuration of Cyber–Physical Production Systems

2021 ◽  
Vol 11 (19) ◽  
pp. 9013
Author(s):  
Douha Macherki ◽  
Thierno M. L. Diallo ◽  
Jean-Yves Choley ◽  
Amir Guizani ◽  
Maher Barkallah ◽  
...  
Keyword(s):  
Decentralized Control ◽  
System Performance ◽  
Production Systems ◽  
Control Level ◽  
Centralized Control ◽  
Four Dimensions ◽  
Potential Capacity ◽  
Heterarchical Control ◽  
Cyber Physical Production Systems

Production systems must be able to adapt to increasingly frequent internal and external changes. Cyber-Physical Production Systems (CPPS), thanks to their potential capacity for self-reconfiguration, can cope with this need for adaptation. To implement the self-reconfiguration functionality in economical and safe conditions, CPPS must have appropriate tools and contextualized information. This information can be organized in the form of an architecture. In this paper, after the analysis of several holonic and nonholonic architectures, we propose a holonic architecture that allows for reliable and efficient reconfiguration. We call this architecture QHAR (Q-Holonic-based ARchitecture). QHAR is constructed based on the idea of a Q-holon, which has four dimensions (physical, cyber, human, and energy) and can exchange three flows (energy, data, and materials). It is a generic Holon that can represent any entity or actor of the supply chain. The QHAR is structured in three levels: centralized control level, decentralized control level, and execution level. QHAR implements the principle of an oligarchical control architecture by deploying both hierarchical and heterarchical control approaches. This ensures the overall system performance and reactivity to hazards. The proposed architecture is tested and validated on a case study.

scholarly journals How environment dynamics affects production scheduling: Requirements for development of CPPS models

2021 ◽  
Vol 49 (4) ◽  
pp. 827-834
Author(s):  
Cátia Alves ◽  
Goran Putnik ◽  
Leonilde Varela
Keyword(s):  
Production Scheduling ◽  
Manufacturing Systems ◽  
New Technologies ◽  
Production Systems ◽  
Strong Positive Correlation ◽  
Systems Architecture ◽  
Study Results ◽  
Cyber Physical Production Systems ◽  
Scheduling Models

Production scheduling can be affected by many disturbances in the manufacturing system, and consequently, the feasible schedules previously defined became obsolete. Emerging of new technologies associated with Industry 4.0, such as Cyber-Physical Production Systems, as a paradigm of implementation of control and support in decision making, should embed the capacity to simulate different environment scenarios based on the data collected by the manufacturing systems. This paper presents the evaluation of environment dynamics effect on production scheduling, considering three scheduling models and three environment scenarios, through a case study. Results show that environment dynamics affect production schedules, and a very strong or strong positive correlation between environment dynamics scenarios and total completion time with delay, over three scheduling paradigms. Based on these results, the requirement for mandatory inclusion of a module for different environment dynamics scenarios generation and the corresponded simulations, of a Cyber-Physical Production Systems architecture, is confirmed.

Application of data mining for spare parts information in maintenance schedule: a case study

2019 ◽  
Vol 30 (7) ◽  
pp. 1055-1072
Author(s):  
U.C. Moharana ◽  
S.P. Sarmah ◽  
Pradeep Kumar Rathore
Keyword(s):  
Data Mining ◽  
Industrial Applications ◽  
Spare Parts ◽  
Sequential Patterns ◽  
Historical Records ◽  
Threshold Values ◽  
Content Type ◽  
Maintenance Activity ◽  
Maintenance Activities

Purpose The purpose of this paper is to suggest a framework for extracting the sequential patterns of maintenance activities and related spare parts information from historical records of maintenance data with pre-defined support or threshold values. Design/methodology/approach A data mining approach has been adopted for predicting the maintenance activity along with spare parts. It starts with a collection of spare parts and maintenance data, and then the development of sequential patterns followed by formation of frequent spare part groups, and finally, integration of sequential maintenance activities with the associated spare parts. Findings This study suggests a framework for extracting the sequential patterns of maintenance activities from historical records of maintenance data with pre-defined support or threshold values. A rule-based approach is proposed in this paper to predict the occurrence of next maintenance activity along with the information of spare parts consumption for that maintenance activity. Research limitations/implications Presented model can be extended for analyzing the failure maintenance activities and performing root cause analysis that can give more valuable suggestion to maintenance managers to take corrective actions prior to next occurrence of failures. In addition, the timestamp information can be utilized to prioritize the maintenance activity that is ignored in this study. Practical implications The proposed model has a high potential for industrial applications and is validated through a case study. The study suggests that the model gives a better approach for selecting spare parts based on their similarity or correlation, considering their actual occurrence during maintenance activities. Apart from this, the clustering of spare parts also trains maintenance manager to learn about the dependency among the spares for group stocking and maintaining the parts availability during maintenance activities. Originality/value This study has used the technique of data mining to find dependent spare parts itemset from the database of the company and developed the model for associated spare parts requirement for subsequent maintenance activity.

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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