Real-Time Ageing and Diagnostic Prediction for Various Hybrid Solar-TEG Power Units by Machine Learning

2021 ◽  
Author(s):  
Surawich Kasempong ◽  
Niyom Kanokwareerat ◽  
Boonyalit Tangjitkongpittaya ◽  
Sarunyoo Setakornnukul ◽  
Apinan Laipanich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Spare Parts ◽  
Predictive Maintenance ◽  
Battery Life ◽  
Time Duration ◽  
Thermo Electric ◽  
Electric Generator ◽  
Failure Patterns ◽  
Run Time

Abstract In PTTEP's offshore fields, more than 100 units of hybrid Solar-TEG power with individual VRLA battery banks are installed on wellhead platforms for powering the process. With large number of various equipment in remote locations, traditional maintenance approach highly consuming resources is, thus, not cost effective especially in oil price crisis. In 2019, "Hybrid Power Solar-TEG Predictive Maintenance" project is established to develop a predictive model and transform maintenance process to total predictive maintenance. The project was begun with three platforms as a pilot project. The operating model was built by machine learning using various historical data recorded in PI system, records of maintenance data and other relevant information such as manufacturer manual, international standard and related white papers. The modelled algorithm was embedded in an application which was developed by Python to predict the ageing and performance of battery banks on pilot wellhead platforms. In 2020, the project continues to build the model of Thermo-Electric Generator (TEG) and extend the coverage location for additional thirty-seven (37) platforms. Lower Depth of Discharge (DoD), higher ambient temperature and lower charging performance are signs of battery’s deterioration while lower supplied current from power source is sign of their underperformance. All parameters were ingested to conduct pattern recognition to make algorithm be able to predict the remaining life of the key equipment. The Eyeball method is conducted to train algorithm the various charging patterns by the developers with aim to evaluate the DoD of battery bank. Apart from battery life prediction, DoD is employed to determine the energy left in battery from night operation to indicate the remaining run time duration. By leveraging machine learning, all failure patterns are recognized. The application is operating real?time and provide early alarm to all person-in-charge when failure potential is realized. The results are visualized on PowerBI to provide the latest status of power units of each platforms. From above, the maintenance approach is thus completely converted from Run-to-Failure to Predictive Maintenance. The long lead spare parts e.g. battery cells could be procured in advance. Spare inventory can be optimized per actual demand. In addition, the offshore supervisor could accurately identify the defective battery banks and proactively recover them in time to minimize unplanned shutdown. The modelled algorithm was in-house developed based on technical information and maintenance records. Although the system goes live, the preventive maintenance according to IEEE1188 is still retained for further collecting more field data to improve accuracy of the model. In addition, the model’s analyzed information, such as battery run time and DoD, has revealed the hidden actual design margin of power system. The platform CAPEX can be thus deducted by removing such excess margin.

Creating adaptive predictions for packaging-critical quality parameters using advanced analytics and machine learning

TAPPI Journal ◽  
2019 ◽  
Vol 18 (11) ◽  
pp. 679-689
Author(s):  
CYDNEY RECHTIN ◽  
CHITTA RANJAN ◽  
ANTHONY LEWIS ◽  
BETH ANN ZARKO
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Predictive Analytics ◽  
Paper Industry ◽  
Quality Parameters ◽  
Fast Change ◽  
The Face ◽  
Model Tuning ◽  
Reducing Costs ◽  
Time Critical

Packaging manufacturers are challenged to achieve consistent strength targets and maximize production while reducing costs through smarter fiber utilization, chemical optimization, energy reduction, and more. With innovative instrumentation readily accessible, mills are collecting vast amounts of data that provide them with ever increasing visibility into their processes. Turning this visibility into actionable insight is key to successfully exceeding customer expectations and reducing costs. Predictive analytics supported by machine learning can provide real-time quality measures that remain robust and accurate in the face of changing machine conditions. These adaptive quality “soft sensors” allow for more informed, on-the-fly process changes; fast change detection; and process control optimization without requiring periodic model tuning. The use of predictive modeling in the paper industry has increased in recent years; however, little attention has been given to packaging finished quality. The use of machine learning to maintain prediction relevancy under everchanging machine conditions is novel. In this paper, we demonstrate the process of establishing real-time, adaptive quality predictions in an industry focused on reel-to-reel quality control, and we discuss the value created through the availability and use of real-time critical quality.

Speech Organ Contour Extraction Using Real-Time MRI and Machine Learning Method

2019 ◽  
Author(s):  
Hironori Takemoto ◽  
Tsubasa Goto ◽  
Yuya Hagihara ◽  
Sayaka Hamanaka ◽  
Tatsuya Kitamura ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Machine Learning Method ◽  
Learning Method ◽  
Contour Extraction

scholarly journals Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4736
Author(s):  
Sk. Tanzir Mehedi ◽  
Adnan Anwar ◽  
Ziaur Rahman ◽  
Kawsar Ahmed
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Real Time ◽  
Transfer Learning ◽  
Security Requirements ◽  
Detection Accuracy ◽  
Area Network ◽  
Complex Data ◽  
Network Intrusion

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.

scholarly journals ViCrypt to the Rescue: Real-Time, Machine-Learning-Driven Video-QoE Monitoring for Encrypted Streaming Traffic

2020 ◽  
Vol 17 (4) ◽  
pp. 2007-2023
Author(s):  
Sarah Wassermann ◽  
Michael Seufert ◽  
Pedro Casas ◽  
Li Gang ◽  
Kuang Li
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Time Machine ◽  
Streaming Traffic

scholarly journals Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics in extreme environments – cyber risk in the colonisation of Mars

2021 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Kevin Page ◽  
Max Van Kleek ◽  
Omar Santos ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Cyber Security ◽  
Extreme Environments ◽  
Learning Technologies ◽  
Cyber Attacks ◽  
Edge Computing ◽  
Mathematical Formulas ◽  
Cyber Risk

AbstractMultiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

Sign in / Sign up

Export Citation Format

Share Document