The Convergence between Predictive Maintenance and Augmented Reality to Aid Renewable Energy Equipment Availability

2014 ◽  
Vol 2 (5) ◽  
pp. 152
Author(s):  
José Manuel Torres Farinha ◽  
Inácio Adelino Fonseca ◽  
Rúben Silva Oliveira ◽  
Fernando Maciel Barbosa
Keyword(s):  
Renewable Energy ◽  
Augmented Reality ◽  
Predictive Maintenance ◽  
Equipment Availability ◽  
Energy Equipment

Nonlinear Trend Analysis of Mill Fan System Vibrations for Predictive Maintenance and Diagnostics

2012 ◽  
Vol 58 (4) ◽  
pp. 351-356
Author(s):  
Mincho B. Hadjiski ◽  
Lyubka A. Doukovska ◽  
Stefan L. Kojnov
Keyword(s):  
Power Plant ◽  
Trend Analysis ◽  
Thermal Power Plant ◽  
Power Plants ◽  
Thermal Power ◽  
Balkan Peninsula ◽  
Predictive Maintenance ◽  
Nonlinear Trend ◽  
The Subject ◽  
The Given

Abstract Present paper considers nonlinear trend analysis for diagnostics and predictive maintenance. The subject is a device from Maritsa East 2 thermal power plant a mill fan. The choice of the given power plant is not occasional. This is the largest thermal power plant on the Balkan Peninsula. Mill fans are main part of the fuel preparation in the coal fired power plants. The possibility to predict eventual damages or wear out without switching off the device is significant for providing faultless and reliable work avoiding the losses caused by planned maintenance. This paper addresses the needs of the Maritsa East 2 Complex aiming to improve the ecological parameters of the electro energy production process.

Predictive Maintenance based on mechanical unbalance severity analysis of rotating machines

2017 ◽  
Author(s):  
Dionisio Martins ◽  
Thiago de Moura Prego ◽  
Amaro Lima ◽  
Douglas Hemerly ◽  
Fabrício Lopes e Silva
Keyword(s):  
Predictive Maintenance ◽  
Rotating Machines

IoT based Predictive Maintenance in Manufacturing Sector

2020 ◽  
Author(s):  
Shikhil Nangia ◽  
Sandhya Makkar ◽  
Rohail Hassan
Keyword(s):  
Manufacturing Sector ◽  
Predictive Maintenance

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.

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