Application of Machine Learning for Predictive Maintenance Cooling System in Nam Ngum-1 Hydropower Plant

2018 ◽  
Author(s):  
Sisavath Xayyasith ◽  
Anucha Promwungkwa ◽  
Kanchit Ngamsanroaj
Keyword(s):  
Machine Learning ◽  
Cooling System ◽  
Predictive Maintenance ◽  
Hydropower Plant

scholarly journals Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1044
Author(s):  
Yassine Bouabdallaoui ◽  
Zoubeir Lafhaj ◽  
Pascal Yim ◽  
Laure Ducoulombier ◽  
Belkacem Bennadji
Keyword(s):  
Machine Learning ◽  
Model Development ◽  
Predictive Maintenance ◽  
Data Availability ◽  
Machine Learning Techniques ◽  
Automation System ◽  
Building Maintenance ◽  
Collection Data ◽  
Iot Devices

The operation and maintenance of buildings has seen several advances in recent years. Multiple information and communication technology (ICT) solutions have been introduced to better manage building maintenance. However, maintenance practices in buildings remain less efficient and lead to significant energy waste. In this paper, a predictive maintenance framework based on machine learning techniques is proposed. This framework aims to provide guidelines to implement predictive maintenance for building installations. The framework is organised into five steps: data collection, data processing, model development, fault notification and model improvement. A sport facility was selected as a case study in this work to demonstrate the framework. Data were collected from different heating ventilation and air conditioning (HVAC) installations using Internet of Things (IoT) devices and a building automation system (BAS). Then, a deep learning model was used to predict failures. The case study showed the potential of this framework to predict failures. However, multiple obstacles and barriers were observed related to data availability and feedback collection. The overall results of this paper can help to provide guidelines for scientists and practitioners to implement predictive maintenance approaches in buildings.

A Machine Learning Based Approach of Performance Estimation for High-Pressure Compressor Airfoils

2018 ◽  
Author(s):  
Jonas Marx ◽  
Stefan Gantner ◽  
Jörn Städing ◽  
Jens Friedrichs
Keyword(s):  
Machine Learning ◽  
High Pressure ◽  
Machine Learning Algorithms ◽  
Performance Estimation ◽  
Predictive Maintenance ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Operating Characteristics ◽  
High Pressure Compressor ◽  
Pressure Compressor

In recent years, the demands of Maintenance, Repair and Overhaul (MRO) customers to provide resource-efficient after market services have grown increasingly. One way to meet these requirements is by making use of predictive maintenance methods. These are ideas that involve the derivation of workscoping guidance by assessing and processing previously unused or undocumented service data. In this context a novel approach on predictive maintenance is presented in form of a performance-based classification method for high pressure compressor (HPC) airfoils. The procedure features machine learning algorithms that establish a relation between the airfoil geometry and the associated aerodynamic behavior and is hereby able to divide individual operating characteristics into a finite number of distinct aero-classes. By this means the introduced method not only provides a fast and simple way to assess piece part performance through geometrical data, but also facilitates the consideration of stage matching (axial as well as circumferential) in a simplified manner. It thus serves as prerequisite for an improved customary HPC performance workscope as well as for an automated optimization process for compressor buildup with used or repaired material that would be applicable in an MRO environment. The methods of machine learning that are used in the present work enable the formation of distinct groups of similar aero-performance by unsupervised (step 1) and supervised learning (step 2). The application of the overall classification procedure is shown exemplary on an artificially generated dataset based on real characteristics of a front and a rear rotor of a 10-stage axial compressor that contains both geometry as well as aerodynamic information. In step 1 of the investigation only the aerodynamic quantities in terms of multivariate functional data are used in order to benchmark different clustering algorithms and generate a foundation for a geometry-based aero-classification. Corresponding classifiers are created in step 2 by means of both, the k Nearest Neighbor and the linear Support Vector Machine algorithms. The methods’ fidelities are brought to the test with the attempt to recover the aero-based similarity classes solely by using normalized and reduced geometry data. This results in high classification probabilities of up to 96 % which is proven by using stratified k-fold cross-validation.

scholarly journals DISAIN SISTEM OTOMASI SUHU RUANGAN PERTEMUAN DENGAN PENERAPAN TEKNIK MACHINE LEARNING

2019 ◽  
pp. 117-120
Author(s):  
Stephanie Imelda Pella ◽  
Hendro FJ L
Keyword(s):  
Machine Learning ◽  
Object Detection ◽  
Room Temperature ◽  
Cooling System ◽  
Raspberry Pi ◽  
Air Cooling ◽  
Minimum Value ◽  
Air Cooling System ◽  
Four Levels ◽  
And Control

This research presents an automation process of controlling room temperature based on the number of people detected in a room. The system consists of a single board raspberry pi computer, esp8266 micro controller, pi camera, and an infrared module. This research is divided into two parts, namely object detection using Raspbery Pi and Tensorflow and Open CV libraries and controlling air cooling system (ACS) using esp8266 and infrared modules by transmitting hexadecimal AC control codes. The ACS temperature is divided into four levels with a minimum value at 18o C and a maximum at 24o C. System testings were carried out in an empty room and a room with a capacity of 50 people that is fully occupied. The results show that the system is able to detect the number of people in the room and control the ACS, but under certain conditions some objects are not detected because the position and camera tilt is not optimal.

scholarly journals PERFORMANCE COMPARISON OF MACHINE LEARNING ALGORITHMS FOR PREDICTIVE MAINTENANCE

2020 ◽  
Vol 10 (3) ◽  
pp. 32-35
Author(s):  
Jakub Gęca
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Performance Comparison ◽  
Machine Learning Algorithms ◽  
Predictive Maintenance ◽  
Model Parameters ◽  
Data Set ◽  
Reduction Techniques ◽  
Machine Reliability ◽  
Dimensionality Reduction Techniques

The consequences of failures and unscheduled maintenance are the reasons why engineers have been trying to increase the reliability of industrial equipment for years. In modern solutions, predictive maintenance is a frequently used method. It allows to forecast failures and alert about their possibility. This paper presents a summary of the machine learning algorithms that can be used in predictive maintenance and comparison of their performance. The analysis was made on the basis of data set from Microsoft Azure AI Gallery. The paper presents a comprehensive approach to the issue including feature engineering, preprocessing, dimensionality reduction techniques, as well as tuning of model parameters in order to obtain the highest possible performance. The conducted research allowed to conclude that in the analysed case , the best algorithm achieved 99.92% accuracy out of over 122 thousand test data records. In conclusion, predictive maintenance based on machine learning represents the future of machine reliability in industry.

Determining Parametric Effects for Droplet Evaporation on Nanoporous Superhydrophillic Surfaces Using Machine Learning Techniques

2021 ◽  
Author(s):  
Emma R. McClure ◽  
Van P. Carey
Keyword(s):  
Machine Learning ◽  
Cooling System ◽  
Spray Cooling ◽  
Droplet Evaporation ◽  
Contact Angles ◽  
Machine Learning Techniques ◽  
Metallic Surfaces ◽  
Droplet Spreading ◽  
Droplet Vaporization ◽  
Speed Accuracy

Abstract Experimental results demonstrate that droplet vaporization on metal surfaces can be significantly enhanced with the application of a nanoporous, superhydrophilic surface coating. A thin layer of ZnO nanopillars can be easily seeded and grown on most metallic surfaces to achieve nanoscale pores between pillars, and ultra-low apparent contact angles. These surface coatings have the potential to improve spray cooling processes, and can be easily scaled up to larger and more complex heat exchangers. In order to characterize the potential improvement to a spray cooling system it is important to understand the dependence on system parameters, and to have a clear model of droplet vaporization on such surfaces. There are a number of surface and impact parameters that will affect the droplet spreading and subsequent vaporization on the surface. The surface contact angle, wicking speed and impact velocity all interact to affect the maximum spread of the droplet and the speed at which the droplet reaches this state. Along with variations in droplet volume and wall superheat, the model for droplet vaporization becomes more complex and nonlinear. Instead of exploring a single parameter at a time, machine learning tools can be utilized to determine the dependence of droplet evaporation time on these parameters simultaneously. In this study a genetic algorithm and a neural network were used to develop a droplet evaporation model for these superhydrophilic surfaces. Each algorithm demonstrated clear advantages depending on whether speed, accuracy, or an explicit mathematical model was prioritized.

Ansatz zur Zustandsbeschreibung technischer Bauteile

2021 ◽  
Vol 2021 (2) ◽  
pp. 35-38
Author(s):  
Lukas Egbert ◽  
◽  
Anton Zitnikov ◽  
Thorsten Tietjen ◽  
Klaus-Dieter Thoben
Keyword(s):  
Machine Learning ◽  
Predictive Maintenance ◽  
Learning Tool ◽  
Machine Learning Tool

Dieser Beitrag beschreibt eine Herangehensweise für eine Predictive Maintenance-Lösung, bei der die Bauteilabnutzung technischer Systeme mithilfe eines Sensorik-Toolkits erfasst und mittels eines Prognosetools überwacht wird. Die Sensorik als auch das Prognosetool müssen flexibel ausgelegt sein, damit sie zielführend an unterschiedlichen technischen Systemen einsetzbar sind. Die Zustandsbestimmung der Bauteile erfolgt dabei nicht kontinuierlich, sondern basierend auf zeitdiskreten Messungen. Anhand der aufgenommenen Daten wird über ein Prognosemodell die wahrscheinliche Restnutzungsdauer der Bauteile prognostiziert. Für die Erstellung der Prognose dient ein Machine Learning Tool, welches mit historischen Abnutzungsverläufen trainiert wird. Die Trainingsdaten werden durch statistische Versuche erfasst, in denen die Einflussgrößen und charakteristische Verläufe verschiedener Abnutzungsarten identifiziert werden. Als Grundlage für diesen Beitrag dienen Untersuchungen an einem Rolltor, die im Rahmen des „LongLife“ Projekts durchgeführt wurden, das vom Bundesministerium für Bildung und Forschung gefördert wird. Das Forschungsvorhaben ist der Fördermaßnahme „Ressourceneffiziente Kreislaufwirtschaft – Innovative Produktkreisläufe“ (ReziProK) zugeordnet und hat eine Laufzeit von 36 Monaten.

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