scholarly journals The quality management ecosystem for predictive maintenance in the Industry 4.0 era

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Sang M. Lee ◽  
DonHee Lee ◽  
Youn Sung Kim
Keyword(s):  
Quality Management ◽  
Industry 4.0 ◽  
Predictive Maintenance

scholarly journals Cloud-Based Analytics Module for Predictive Maintenance of the Textile Manufacturing Process

2021 ◽  
Vol 11 (21) ◽  
pp. 9945
Author(s):  
Ray-I Chang ◽  
Chia-Yun Lee ◽  
Yu-Hsin Hung
Keyword(s):  
Quality Management ◽  
Industry 4.0 ◽  
Mean Squared Error ◽  
Production Quality ◽  
Machine Learning Algorithms ◽  
Predictive Maintenance ◽  
Third Party ◽  
Manufacturing Processes ◽  
Textile Manufacturing ◽  
Iot Applications

Industry 4.0 has remarkably transformed many industries. Supervisory control and data acquisition (SCADA) architecture is important to enable an intelligent and connected manufacturing factory. SCADA is extensively used in many Internet of Things (IoT) applications, including data analytics and data visualization. Product quality management is important across most manufacturing industries. In this study, we extensively used SCADA to develop a cloud-based analytics module for production quality predictive maintenance (PdM) in Industry 4.0, thus targeting textile manufacturing processes. The proposed module incorporates a complete knowledge discovery in database process. Machine learning algorithms were employed to analyze preprocessed data and provide predictive suggestions for production quality management. Equipment data were analyzed using the proposed system with an average mean-squared error of ~0.0005. The trained module was implemented as an application programming interface for use in IoT applications and third-party systems. This study provides a basis for improving production quality by predicting optimized equipment settings in manufacturing processes in the textile industry.

scholarly journals The Role of Industry 4.0 and BPMN in the Arise of Condition-Based and Predictive Maintenance: A Case Study in the Automotive Industry

2021 ◽  
Vol 11 (8) ◽  
pp. 3438
Author(s):  
Jorge Fernandes ◽  
João Reis ◽  
Nuno Melão ◽  
Leonor Teixeira ◽  
Marlene Amorim
Keyword(s):  
Business Process ◽  
Automotive Industry ◽  
Industry 4.0 ◽  
Process Model ◽  
Business Process Management ◽  
Process Management ◽  
Predictive Maintenance ◽  
The Evolution Of Industry

This article addresses the evolution of Industry 4.0 (I4.0) in the automotive industry, exploring its contribution to a shift in the maintenance paradigm. To this end, we firstly present the concepts of predictive maintenance (PdM), condition-based maintenance (CBM), and their applications to increase awareness of why and how these concepts are revolutionizing the automotive industry. Then, we introduce the business process management (BPM) and business process model and notation (BPMN) methodologies, as well as their relationship with maintenance. Finally, we present the case study of the Renault Cacia, which is developing and implementing the concepts mentioned above.

Part 6 Summary: Predictive Maintenance in Industry 4.0 - Methodologies, Tools and Interoperable Applications

2018 ◽  
pp. 233-237
Author(s):  
Gregoris Mentzas ◽  
Karl Hribernik ◽  
Klaus-Dieter Thoben ◽  
Dimitris Kiritsis ◽  
Ali Mousavi
Keyword(s):  
Industry 4.0 ◽  
Predictive Maintenance

scholarly journals Smart Mechanical Systems for Manufacturing in the Era of Industry 4.0: Condition-Based Predictive Maintenance and Dynamic System Modification for Small and Medium-Sized Enterprises

2021 ◽  
Vol 20 (2) ◽  
Author(s):  
Giovanni Carabin ◽  
Erich Wehrle ◽  
Renato Vidoni
Keyword(s):  
Dynamic System ◽  
Industry 4.0 ◽  
Industrial Revolution ◽  
Mechanical Systems ◽  
Predictive Maintenance ◽  
Condition Based Maintenance ◽  
Specific Target ◽  
New Class ◽  
Fourth Industrial Revolution ◽  
Potential Benefits

We are in the era of the fourth industrial revolution. Which highlights adaptability, monitoring, digitisation and efficiency in manufacturing as a result of the design of new smart mechanical systems. A central role in Industry 4.0 is played by maintenance and, within this framework, we define and review condition-based predictive maintenance. Thereafter, we propose a new class of smart mechanical systems that self-optimise utilising both condition-based maintenance and dynamic system modification. Akin to smart structures, smart mechanical systems will recognise and predict faults or malfunctions and, subsequently, self-optimise to restore desirable system behaviour. Potential benefits include increased reliability and efficiency while reducing cost without the requirement of highly skilled technicians. Thus, small and medium-sized enterprises are a specific target of such technology due to their increasing level of automatisation within Industry 4.0.

Influence of Quality on Manufacturing Processes in Enterprises from the Perspective of Generating Value Added For Customers

2021 ◽  
Vol 12 (1) ◽  
pp. 128-128
Author(s):  
Kostiuk Yaroslava
Keyword(s):  
Quality Management ◽  
Corporate Culture ◽  
Industry 4.0 ◽  
Value Added ◽  
Business Environment ◽  
Added Value ◽  
Professional Literature ◽  
Value Stream ◽  
Global Dynamic ◽  
Value Stream Map

In the current global dynamic and competitive business environment of Industry 4.0, small and medium-sized enterprises face a major challenge of expanding their market activities and adapt to new conditions in order to survive in times of economic or pandemic crisis. The implementation of comprehensive quality management in business environment within EU organizations is a response to this challenge for global competition (Abdul, Sumantoro, & Maria, 2019). Current problem is the fact that the implementation and monitoring of quality management as a process of business management in the majority of small and medium-sized enterprises (SMEs) is not sufficiently used, underestimated, or even considered to be obsolete (Rigby, Bilodeau, 2018). In an enterprise with good financial health and healthy corporate culture, all transactions and processes are carried out properly and the relationships among all stakeholders (employees, suppliers, and customers) are successful. For other companies, it is necessary to take steps to ensure quality and follow them (Fernandes et al., 2017) in order to move towards the concept of Industry 4.0. According to published professional literature, each research worker has developed their own framework for mapping value production operations based on specific needs and interests in the fields under review. However, the relationship between the quality processes and value stream maps has not been adequately addressed in professional literature, especially in the case of small and medium-sized enterprises. Therefore, for achieving the objective of the contribution, the following research questions have been formulated: To which extent quality participates in generating value added within production process? In which production operations does the quality factor contributes most to generating value added? Keywords: Quality value stream map, Value stream map, quality management, added value for the customer.

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