USE OF MACHINE LEARNING IN THE FORECASTING OF ELECTROMECHANICAL ROLLING PRODUCTION SYSTEMS CONDITIONS

2017 ◽  
Author(s):  
A.V. Kozhevnikov ◽  
I.S. Ilatovsky ◽  
O.I. Solovyova
Keyword(s):  
Machine Learning ◽  
Production Systems ◽  
Rolling Production

scholarly journals Machine Learning in Agriculture: A Review

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2674 ◽  
Author(s):  
Konstantinos Liakos ◽  
Patrizia Busato ◽  
Dimitrios Moshou ◽  
Simon Pearson ◽  
Dionysis Bochtis
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Production Systems ◽  
Species Recognition ◽  
Learning Technologies ◽  
Sensor Data ◽  
Weed Detection ◽  
Data Intensive ◽  
Big Data Technologies ◽  
Applications Of Machine Learning

Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies. By applying machine learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action.

Creep Rupture Forecasting

2014 ◽  
Vol 2 (2) ◽  
pp. 1-25 ◽  
Author(s):  
Stylianos Chatzidakis ◽  
Miltiadis Alamaniotis ◽  
Lefteri H. Tsoukalas
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Process Model ◽  
Production Systems ◽  
Learning Algorithms ◽  
Difficult Problem ◽  
Network Size ◽  
Creep Rupture ◽  
Machine Learning Algorithms ◽  
Training Data

Creep rupture is becoming increasingly one of the most important problems affecting behavior and performance of power production systems operating in high temperature environments and potentially under irradiation as is the case of nuclear reactors. Creep rupture forecasting and estimation of the useful life is required to avoid unanticipated component failure and cost ineffective operation. Despite the rigorous investigations of creep mechanisms and their effect on component lifetime, experimental data are sparse rendering the time to rupture prediction a rather difficult problem. An approach for performing creep rupture forecasting that exploits the unique characteristics of machine learning algorithms is proposed herein. The approach seeks to introduce a mechanism that will synergistically exploit recent findings in creep rupture with the state-of-the-art computational paradigm of machine learning. In this study, three machine learning algorithms, namely General Regression Neural Networks, Artificial Neural Networks and Gaussian Processes, were employed to capture the underlying trends and provide creep rupture forecasting. The current implementation is demonstrated and evaluated on actual experimental creep rupture data. Results show that the Gaussian process model based on the Matérn kernel achieved the best overall prediction performance (56.38%). Significant dependencies exist on the number of training data, neural network size, kernel selection and whether interpolation or extrapolation is performed.

scholarly journals Model-Based Diagnosis for Cyber-Physical Production Systems Based on Machine Learning and Residual-Based Diagnosis Models

2019 ◽  
Vol 33 ◽  
pp. 2727-2735 ◽  
Author(s):  
Andreas Bunte ◽  
Benno Stein ◽  
Oliver Niggemann
Keyword(s):  
Machine Learning ◽  
Production Systems ◽  
Quantitative Model ◽  
Machine Learning Algorithms ◽  
Seamless Integration ◽  
Model Based ◽  
Novel Approach ◽  
Qualitative Models ◽  
Easy Integration ◽  
Cyber Physical Production Systems

This paper introduces a novel approach to Model-Based Diagnosis (MBD) for hybrid technical systems. Unlike existing approaches which normally rely on qualitative diagnosis models expressed in logic, our approach applies a learned quantitative model that is used to derive residuals. Based on these residuals a diagnosis model is generated and used for a root cause identification. The new solution has several advantages such as the easy integration of new machine learning algorithms into MBD, a seamless integration of qualitative models, and a significant speed-up of the diagnosis runtime. The paper at hand formally defines the new approach, outlines its advantages and drawbacks, and presents an evaluation with real-world use cases.

From remote sensing to bioeconomy: how big data can improve automatic map generation

2020 ◽  
Author(s):  
Jonathan Rizzi ◽  
Ingvild Nystuen ◽  
Misganu Debella-Gilo ◽  
Nils Egil Søvde
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Big Data ◽  
Climate Adaptation ◽  
Technology Development ◽  
Production Systems ◽  
Learning Algorithm ◽  
Efficient Production ◽  
Research Activity ◽  
Social Mission

<p>Recent years are experiencing an exponential increase of remote sensing datasets coming from different sources (satellites, airplanes, UAVs) at different resolutions (up to few cm) based on different sensors (single bands sensors, hyperspectral cameras, LIDAR, …). At the same time, IT developments are allowing for the storage of very large datasets (up to Petabytes) and their efficient processing (through HPC, distributed computing, use of GPUs). This allowed for the development and diffusion of many libraries and packages implementing machine learning algorithm in a very efficient way. It has become therefor possible to use machine learning (including deep learning methods such as convolutional neural networks) to spatial datasets with the aim of increase the level of automaticity of the creation of new maps or the update of existing maps. </p><p>Within this context, the Norwegian Institute of Bioeconomy Research (NIBIO), has started a project to test and apply big data methods and tools to support research activity transversally across its divisions.  NIBIO is a research-based knowledge institution that utilizes its expertise and professional breadth for the development of the bioeconomy in Norway. Its social mission entails a national responsibility in the bioeconomy sector, focusing on several societal challenges including: i) Climate (emission reductions, carbon uptake and climate adaptation); ii) Sustainability (environment, resource management and production within nature and society's tolerance limits); iii) Transformation (circular economy, resource efficient production systems, innovation and technology development); iv) food; and v) economy.</p><p>The presentation will show obtained results focus on land cover mapping using different methods and different dataset, include satellite images and airborne hyperspectral images. Further, the presentation will focus related on the criticalities related to automatic mapping from remote sensing dataset and importance of the availability of large training datasets.</p>

scholarly journals A Holistic Quality Assurance Approach for Machine Learning Applications in Cyber-Physical Production Systems

2021 ◽  
Vol 11 (20) ◽  
pp. 9590
Author(s):  
Hajo Wiemer ◽  
Alexander Dementyev ◽  
Steffen Ihlenfeldt
Keyword(s):  
Machine Learning ◽  
Quality Assurance ◽  
Process Model ◽  
Production Systems ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Monitoring Process ◽  
Machine Learning Applications ◽  
Cyber Physical Production Systems ◽  
Automation Technology

With the trend of increasing sensors implementation in production systems and comprehensive networking, essential preconditions are becoming required to be established for the successful application of data-driven methods of equipment monitoring, process optimization, and other relevant automation tasks. As a protocol, these tasks should be performed by engineers. Engineers usually do not have enough experience with data mining or machine learning techniques and are often skeptical about the world of artificial intelligence (AI). Quality assurance of AI results and transparency throughout the IT chain are essential for the acceptance and low-risk dissemination of AI applications in production and automation technology. This article presents a conceptual method of the stepwise and level-wise control and improvement of data quality as one of the most important sources of AI failures. The appropriate process model (V-model for quality assurance) forms the basis for this.

scholarly journals Towards low-cost machine learning solutions for manufacturing SMEs

AI & Society ◽  
2021 ◽  
Author(s):  
Jan Kaiser ◽  
German Terrazas ◽  
Duncan McFarlane ◽  
Lavindra de Silva
Keyword(s):  
Machine Learning ◽  
Production Systems ◽  
Low Cost ◽  
Parameter Tuning ◽  
Simple Solution ◽  
Data Preparation ◽  
Comprehensive Understanding ◽  
Manufacturing Environment ◽  
Extensive Data ◽  
Learning Capabilities

AbstractMachine learning (ML) is increasingly used to enhance production systems and meet the requirements of a rapidly evolving manufacturing environment. Compared to larger companies, however, small- and medium-sized enterprises (SMEs) lack in terms of resources, available data and skills, which impedes the potential adoption of analytics solutions. This paper proposes a preliminary yet general approach to identify low-cost analytics solutions for manufacturing SMEs, with particular emphasis on ML. The initial studies seem to suggest that, contrarily to what is usually thought at first glance, SMEs seldom need digital solutions that use advanced ML algorithms which require extensive data preparation, laborious parameter tuning and a comprehensive understanding of the underlying problem. If an analytics solution does require learning capabilities, a ‘simple solution’, which we will characterise in this paper, should be sufficient.

Enhancing Cloud Availability via Intelligent Monitoring using Time Series Database and Machine Learning

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Production Systems ◽  
Technical Support ◽  
Machine Learning Techniques ◽  
Cloud Provider ◽  
Monitoring Tool ◽  
Intelligent Monitoring ◽  
Learning Techniques ◽  
Proactive Measures

Every cloud provider, wishes to provide 99.9999% availabil- ity for the systems provisioned and operated by them for the customer i.e. may it be SaaS or PaaS or IaaS model, the availability of the system must be greater than 99.9999%.It becomes vital for the provider to mon- itor the systems and take proactive measures to reduce the downtime.In an ideal scenario, the support colleagues (24*7 technical support) must be aware of the on-going issues in the production systems before it is raised as an incident by the customer. But currently, there is no effective alert monitoring solutions for the same. The proposed solution presented in this paper is to have a central alert monitoring tool for all cloud so- lutions offered by the cloud provider. The central alert monitoring tool constantly observes the time series database which contains metric val- ues populated by HA and compares the incoming metric values with the defined thresholds. When a metric value exceeds the defined threshold, using machine learning techniques the monitoring tool decides & takes actions.

A Machine Learning Workflow to Predict Anomalous Sanding Events in Deepwater Wells

2021 ◽  
Author(s):  
Mustafa Can Kara ◽  
Malina Majeran ◽  
Bret Peterson ◽  
Tom Wimberly ◽  
Greg Sinclair
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Real Time ◽  
Short Term Memory ◽  
Production Systems ◽  
Model Performance ◽  
Process Data ◽  
Business Units ◽  
Significant Damage ◽  
Anomaly Score

Abstract Deepwater wells possess a high risk of sand escaping the reservoir into the production systems. Sand production is a common operational issue which results in potential equipment damage and hence product contamination. Excessive sand erosion causes blockage in tubulars and cavities in downhole equipment (subsea valves, chokes, bends etc.), resulting in maintenance costs for subsea equipment that adds up to millions of dollars yearly to operators. In this work, a scalable Machine Learning (ML) model readily accessing historical and real-time feed of sensor and simulation data is built to develop a predictive solution. Deployed workflow can inform Control Room Operators before significant damage occurs. An anomaly detection architecture, a common unsupervised learning framework for maintenance analytics, is deployed. Anomaly detection models include methods within the scope of dimensionality reduction. Principle Component Analysis (PCA) and Long Short-Term Memory (LSTM) Autoencoders are deployed to tackle the problem through reconstruction of the original input. During the workflow, a threshold is calculated after batch training and passed along with anomaly error scores in real-time. An alarm is triggered once the real-time anomaly score passes the threshold calculated during batch training. ML outputs are streamlined in near real-time to the database. In this study, deployed ML model performance is benchmarked against a GOM Deepwater well where sanding is known to occur often. The ML Model architecture can process data that is captured by OSI PI historian, predict anomalous sanding events in advance, and is shown to be scalable to other wells in GOM. It is noted from this study that streamlined ML architecture and outputs simplify exploratory data analysis and model deployment across Onshore and Offshore Business Units. In addition, sanding stakeholders are notified in advance and can take early mitigative action before significant damage to wellhead or downhole equipment occurs instead of reacting to a possible sanding event offshore. The novelty of the utilized ML algorithm and process is in the ability to predict sanding anomalies in advance through ML batch training, infer prediction values near real-time, and scale to other assets.

Multi-agent Environment for Decision-Support in Production Systems Using Machine Learning Methods

2019 ◽  
pp. 517-529
Author(s):  
Jarosław Koźlak ◽  
Bartlomiej Sniezynski ◽  
Dorota Wilk-Kołodziejczyk ◽  
Albert Leśniak ◽  
Krzysztof Jaśkowiec
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Production Systems ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Multi Agent

scholarly journals Application of Meta-Analysis and Machine Learning Methods to the Prediction of Methane Production from In Vitro Mixed Ruminal Micro-Organism Fermentation

Animals ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 720
Author(s):  
Jennifer L. Ellis ◽  
Héctor Alaiz-Moretón ◽  
Alberto Navarro-Villa ◽  
Emma J. McGeough ◽  
Peter Purcell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Production Systems ◽  
Meta Analysis ◽  
Gas Production ◽  
Production Model ◽  
Support Vector ◽  
Ch4 Production ◽  
Ruminant Diets ◽  
Data Points

In vitro gas production systems are utilized to screen feed ingredients for inclusion in ruminant diets. However, not all in vitro systems are set up to measure methane (CH4) production, nor do all publications report in vitro CH4. Therefore, the objective of this study was to develop models to predict in vitro CH4 production from total gas and volatile fatty acid (VFA) production data and to identify the major drivers of CH4 production in these systems. Meta-analysis and machine learning (ML) methodologies were applied to a database of 354 data points from 11 studies to predict CH4 production from total gas production, apparent DM digestibility (DMD), final pH, feed type (forage or concentrate), and acetate, propionate, butyrate and valerate production. Model evaluation was performed on an internal dataset of 107 data points. Meta-analysis results indicate that equations containing DMD, total VFA production, propionate, feed type and valerate resulted in best predictability of CH4 on the internal evaluation dataset. The ML models far exceeded the predictability achieved using meta-analysis, but further evaluation on an external database would be required to assess generalization ability on unrelated data. Between the ML methodologies assessed, artificial neural networks and support vector regression resulted in very similar predictability, but differed in fitting, as assessed by behaviour analysis. The models developed can be utilized to estimate CH4 emissions in vitro.

Sign in / Sign up

Export Citation Format

Share Document