scholarly journals AS-Solar, a Tool for Predictive Maintenance of Solar Groundwater Pumping Systems

Agronomy ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2356
Author(s):  
Jorge Cervera-Gascó ◽  
Jesús Montero ◽  
Miguel A. Moreno
Keyword(s):  
Renewable Energy ◽  
Predictive Maintenance ◽  
Complex Case ◽  
Time Data ◽  
Variable Frequency Drive ◽  
Water Abstraction ◽  
Acceptable Accuracy ◽  
Pumping System ◽  
Electrical Components

Energy for water abstraction limits the viability of some irrigable areas. Increasing efficiency and introducing renewable energy can reduce energy cost. Solar pumping is a widely recognized renewable energy solution. These pumping systems suffer special wear out due to sudden changes and for having working conditions far from the nominal points. Thus, monitoring systems are becoming more frequent for maintenance issues. A new decision support system, named AS-Solar, was developed to perform predictive maintenance. This model permits detecting if the source of the anomaly in the pump performance is the pump, the electrical components (motor, variable frequency drive (VFD) or cables) or the pumping pipe. It demands real-time data from the monitoring system and an accurate simulation model, together with an optimization process that helps in the decision making in predictive maintenance. To validate the developed model, it was applied to a complex case study of a solar pumping system of 40 kWp that abstracts groundwater from nearly 200 m deep. This pumping system has a VFD, two lines of cables up to the pump and aggressive water with slimes, which causes different problems in the pumping system. In this case study, the AS-Solar model shows an acceptable accuracy, with a relative error (RE) of the 2.9% in simulated power and 7.9% in simulated discharge.

scholarly journals An intelligent approach for data pre-processing and analysis in predictive maintenance with an industrial case study

2020 ◽  
Vol 12 (5) ◽  
pp. 168781402091920 ◽  
Author(s):  
Ebru Turanoglu Bekar ◽  
Per Nyqvist ◽  
Anders Skoogh
Keyword(s):  
Machine Learning ◽  
Manufacturing Industry ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Predictive Maintenance ◽  
Machine Learning Techniques ◽  
Time Data ◽  
Industrial Case Study ◽  
Intelligent Approach

Recent development in the predictive maintenance field has focused on incorporating artificial intelligence techniques in the monitoring and prognostics of machine health. The current predictive maintenance applications in manufacturing are now more dependent on data-driven Machine Learning algorithms requiring an intelligent and effective analysis of a large amount of historical and real-time data coming from multiple streams (sensors and computer systems) across multiple machines. Therefore, this article addresses issues of data pre-processing that have a significant impact on generalization performance of a Machine Learning algorithm. We present an intelligent approach using unsupervised Machine Learning techniques for data pre-processing and analysis in predictive maintenance to achieve qualified and structured data. We also demonstrate the applicability of the formulated approach by using an industrial case study in manufacturing. Data sets from the manufacturing industry are analyzed to identify data quality problems and detect interesting subsets for hidden information. With the approach formulated, it is possible to get the useful and diagnostic information in a systematic way about component/machine behavior as the basis for decision support and prognostic model development in predictive maintenance.

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

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.

scholarly journals Holistic simulation of wind turbines with fully aero-elastic and electrical model

2021 ◽  
Author(s):  
Marcus Wiens ◽  
Sebastian Frahm ◽  
Philipp Thomas ◽  
Shoaib Kahn
Keyword(s):  
Renewable Energy ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Low Voltage ◽  
Renewable Energy Sources ◽  
Elastic Model ◽  
Electrical Model ◽  
Holistic Model ◽  
Electric System ◽  
Electrical Components

AbstractRequirements for the design of wind turbines advance facing the challenges of a high content of renewable energy sources in the public grid. A high percentage of renewable energy weaken the grid and grid faults become more likely, which add additional loads on the wind turbine. Load calculations with aero-elastic models are standard for the design of wind turbines. Components of the electric system are usually roughly modeled in aero-elastic models and therefore the effect of detailed electrical models on the load calculations is unclear. A holistic wind turbine model is obtained, by combining an aero-elastic model and detailed electrical model into one co-simulation. The holistic model, representing a DFIG turbine is compared to a standard aero-elastic model for load calculations. It is shown that a detailed modelling of the electrical components e.g., generator, converter, and grid, have an influence on the results of load calculations. An analysis of low-voltage-ride-trough events during turbulent wind shows massive increase of loads on the drive train and effects the tower loads. Furthermore, the presented holistic model could be used to investigate different control approaches on the wind turbine dynamics and loads. This approach is applicable to the modelling of a holistic wind park to investigate interaction on the electrical level and simultaneously evaluate the loads on the wind turbine.

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