A Digital Twin as a framework for a machine learning based predictive maintenance system

2021 ◽  
pp. 313-319
Author(s):  
C.-D. Thiele ◽  
J. Brötzmann ◽  
T.-J. Huyeng ◽  
U. Rüppel ◽  
S. R. Lorenzen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Maintenance ◽  
Digital Twin ◽  
Maintenance System

Machine Learning Based Predictive Maintenance System for Industrial Chain Conveyor System

2021 ◽  
pp. 251-263
Author(s):  
Kai Woon Goh ◽  
Kam Heng Chaw ◽  
Jonathan C. E. Yong ◽  
Ye Sheng Koh ◽  
Marvin Dares ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Maintenance ◽  
Conveyor System ◽  
Maintenance System ◽  
Industrial Chain ◽  
Chain Conveyor

scholarly journals Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2099 ◽  
Author(s):  
Martin W. Hoffmann ◽  
Stephan Wildermuth ◽  
Ralf Gitzel ◽  
Aydin Boyaci ◽  
Jörg Gebhardt ◽  
...  
Keyword(s):  
Machine Learning ◽  
Condition Monitoring ◽  
Machine Learning Algorithms ◽  
Predictive Maintenance ◽  
Distribution Grid ◽  
Medium Voltage ◽  
Maintenance System ◽  
Current State ◽  
Smart Mobility ◽  
Medium Voltage Switchgear

The development of renewable energies and smart mobility has profoundly impacted the future of the distribution grid. An increasing bidirectional energy flow stresses the assets of the distribution grid, especially medium voltage switchgear. This calls for improved maintenance strategies to prevent critical failures. Predictive maintenance, a maintenance strategy relying on current condition data of assets, serves as a guideline. Novel sensors covering thermal, mechanical, and partial discharge aspects of switchgear, enable continuous condition monitoring of some of the most critical assets of the distribution grid. Combined with machine learning algorithms, the demands put on the distribution grid by the energy and mobility revolutions can be handled. In this paper, we review the current state-of-the-art of all aspects of condition monitoring for medium voltage switchgear. Furthermore, we present an approach to develop a predictive maintenance system based on novel sensors and machine learning. We show how the existing medium voltage grid infrastructure can adapt these new needs on an economic scale.

A Lesson on Operationalizing Machine Learning for Predictive Maintenance of Gas Turbines

2021 ◽  
Author(s):  
Paolo Pileggi ◽  
Elena Lazovik ◽  
Ron Snijders ◽  
Lars-Uno Axelsson ◽  
Sietse Drost ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gas Turbines ◽  
Service Providers ◽  
Black Box ◽  
Careful Consideration ◽  
Knowledge Systems ◽  
Predictive Maintenance ◽  
Data Driven ◽  
Use Case ◽  
Digital Twin

Abstract OEMs, service providers and end-users are moving from preventative to predictive maintenance to minimize the risk of unwanted power plant shut-downs and to maximize profitability. Digital Twin and Machine Learning (ML) are important techniques in this transformation as it complements and improves the traditional expert-based knowledge systems. There is a continued trend to use data-driven, so-called black-box, ML techniques as an improvement over traditional statistical approaches. However, these ML approaches suffer from low interpretability or explainability, making it hard to trust how or why a certain anomaly in the system is detected, limiting the trust in the prediction and making it much less likely to identify the real original cause of the problem. In this paper, we present our lesson learnt from operationalizing ML in a real-world use case that studied data from the 1.85 MWe OPRA OP16 all radial single-shaft gas turbine. We comment on the unforeseen obstacles we uncovered during our ML anomaly detection application and juxtapose them with the high potential value that our novel ML applications and explanation method can provide. ML may be enticing for the predictive maintenance of gas turbines but our lesson makes it clear that operationalizing ML goes beyond merely algorithm specifics. In line with the nature of the Digital Twin, it requires careful consideration of the specialized IT system supporting the algorithm, and the specific process it supports and in which it is deployed.

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.

scholarly journals Bearing Anomaly Recognition Using an Intelligent Digital Twin Integrated with Machine Learning

2021 ◽  
Vol 11 (10) ◽  
pp. 4602
Author(s):  
Farzin Piltan ◽  
Jong-Myon Kim
Keyword(s):  
Machine Learning ◽  
Variable Structure ◽  
Vibration Signal ◽  
Crack Size ◽  
Approximation Technique ◽  
Digital Twin ◽  
Average Accuracy ◽  
Machine Learning Approach ◽  
The Impact ◽  
Signal Approximation

In this study, the application of an intelligent digital twin integrated with machine learning for bearing anomaly detection and crack size identification will be observed. The intelligent digital twin has two main sections: signal approximation and intelligent signal estimation. The mathematical vibration bearing signal approximation is integrated with machine learning-based signal approximation to approximate the bearing vibration signal in normal conditions. After that, the combination of the Kalman filter, high-order variable structure technique, and adaptive neural-fuzzy technique is integrated with the proposed signal approximation technique to design an intelligent digital twin. Next, the residual signals will be generated using the proposed intelligent digital twin and the original RAW signals. The machine learning approach will be integrated with the proposed intelligent digital twin for the classification of the bearing anomaly and crack sizes. The Case Western Reserve University bearing dataset is used to test the impact of the proposed scheme. Regarding the experimental results, the average accuracy for the bearing fault pattern recognition and crack size identification will be, respectively, 99.5% and 99.6%.

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