scholarly journals A physics-informed machine learning framework for predictive maintenance applied to turbomachinery assets

2021 ◽  
pp. 1-15
Author(s):  
Marzia Sepe ◽  
Antonino Graziano ◽  
Maciej Badora ◽  
Alessandro Di Stazio ◽  
Luca Bellani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Domain Knowledge ◽  
Risk Model ◽  
Hybrid Approach ◽  
Predictive Maintenance ◽  
Learning Framework ◽  
Maintenance Service ◽  
Aging Phenomena ◽  
Long Term Prediction

The paper presents an overview of Baker Hughes digital framework for a predictive maintenance service boosted by Machine Learning and asset knowledge, applied to turbomachinery assets. Optimization of the maintenance scenario is performed through a risk model that assesses online health status and probability of failure, by detecting functional anomalies and aging phenomena and evaluating their impact on asset serviceability. Turbomachinery domain knowledge is used to create physics-based models, to configure a severity assessment layer and to properly map maintenance actions to anomaly types. The implemented analytics framework is able also to forecast engine behaviour over the future in order to optimize asset operation and maintenance, minimizing downtime and residual risk. Predictive capabilities are optimized thanks to the hybrid approach, where physics-based knowledge empowers long term prediction accuracy while data-driven analytics ensure fast-events prognostics. Accuracy of the hybrid approach is a differentiator for maintenance optimization, allowing activities to be planned properly and in early advance with respect to outage execution.

scholarly journals LONG-TERM PREDICTION OF CARDIAC EVENTS IN ASYMPTOMATIC SUBJECTS BY INTEGRATING NOVEL CIRCULATING AND IMAGING BIOMARKERS USING MACHINE LEARNING

2020 ◽  
Vol 75 (11) ◽  
pp. 1580
Author(s):  
Andrew Lin ◽  
Balaji Tamarappoo ◽  
Frederic Commandeur ◽  
Priscilla McElhinney ◽  
Sebastien Cadet ◽  
...  
Keyword(s):  
Machine Learning ◽  
Imaging Biomarkers ◽  
Cardiac Events ◽  
Term Prediction ◽  
Asymptomatic Subjects ◽  
Long Term Prediction

scholarly journals Evaluating Human versus Machine Learning Performance in a LegalTech Problem

2021 ◽  
Vol 12 (1) ◽  
pp. 297
Author(s):  
Tamás Orosz ◽  
Renátó Vági ◽  
Gergely Márk Csányi ◽  
Dániel Nagy ◽  
István Üveges ◽  
...  
Keyword(s):  
Machine Learning ◽  
Domain Knowledge ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Added Value ◽  
Learning Performance ◽  
Production Environment ◽  
Legal Domain ◽  
The Cost

Many machine learning-based document processing applications have been published in recent years. Applying these methodologies can reduce the cost of labor-intensive tasks and induce changes in the company’s structure. The artificial intelligence-based application can replace the application of trainees and free up the time of experts, which can increase innovation inside the company by letting them be involved in tasks with greater added value. However, the development cost of these methodologies can be high, and usually, it is not a straightforward task. This paper presents a survey result, where a machine learning-based legal text labeler competed with multiple people with different legal domain knowledge. The machine learning-based application used binary SVM-based classifiers to resolve the multi-label classification problem. The used methods were encapsulated and deployed as a digital twin into a production environment. The results show that machine learning algorithms can be effectively utilized for monotonous but domain knowledge- and attention-demanding tasks. The results also suggest that embracing the machine learning-based solution can increase discoverability and enrich the value of data. The test confirmed that the accuracy of a machine learning-based system matches up with the long-term accuracy of legal experts, which makes it applicable to automatize the working process.

scholarly journals A spatiotemporal ensemble machine learning framework for generating land use / land cover time-series maps for Europe (2000 – 2019) based on LUCAS, CORINE and GLAD Landsat

2021 ◽  
Author(s):  
Martijn Witjes ◽  
Leandro Parente ◽  
Chris J. van Diemen ◽  
Tomislav Hengl ◽  
Martin Landa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Land Cover ◽  
Data Science ◽  
Cross Validation ◽  
Series Data ◽  
Cover Time ◽  
Learning Framework ◽  
Tree Classifier

Abstract A seamless spatiotemporal machine learning framework for automated prediction, uncertainty assessment, and analysis of long-term LULC dynamics is presented. The framework includes: (1) harmonization and preprocessing of high-resolution spatial and spatiotemporal input datasets (GLAD Landsat, NPP/VIIRS) including 5~million harmonized LUCAS and CORINE Land Cover-derived training samples, (2) model building based on spatial k-fold cross-validation and hyper-parameter optimization, (3) prediction of the most probable class, class probabilities and uncertainty per pixel, (4) LULC change analysis on time-series of produced maps. The spatiotemporal ensemble model consists of a random forest, gradient boosted tree classifier, and a artificial neural network, with a logistic regressor as meta-learner. The results show that the most important variables for mapping LULC in Europe are: seasonal aggregates of Landsat green and near-infrared bands, multiple Landsat-derived spectral indices, long-term surface water probability, and elevation. Spatial cross-validation of the model indicates consistent performance across multiple years with overall accuracy (weighted F1-score) of 0.49, 0.63, and 0.83 when predicting 44 (level-3), 14 (level-2), and 5 classes (level-1). The spatiotemporal model outperforms spatial models on known-year classification by 2.7% and unknown-year classification by 3.5%. Results of the accuracy assessment using 48,365 independent test samples shows 87% match with the validation points. Results of time-series analysis (time-series of LULC probabilities and NDVI images) suggest forest loss in large parts of Sweden, the Alps, and Scotland. An advantage of using spatiotemporal ML is that the fitted model can be used to predict LULC in years that were not included in its training dataset, allowing generalization to past and future periods, e.g. to predict land cover for years prior to 2000 and beyond 2020. The generated land cover time-series data stack (ODSE-LULC), including the training points, is publicly available via the Open Data Science (ODS)-Europe Viewer. Functions used to prepare data and run modeling are available via the eumap library for python.

scholarly journals Machine Learning Framework for Predictive Maintenance in Milling

2019 ◽  
Vol 52 (13) ◽  
pp. 177-182 ◽  
Author(s):  
Emiliano Traini ◽  
Giulia Bruno ◽  
Gianluca D’Antonio ◽  
Franco Lombardi
Keyword(s):  
Machine Learning ◽  
Predictive Maintenance ◽  
Learning Framework

scholarly journals A hierarchical expert-guided machine learning framework for clinical decision support systems: an application to traumatic brain injury prognostication

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Negar Farzaneh ◽  
Craig A. Williamson ◽  
Jonathan Gryak ◽  
Kayvan Najarian
Keyword(s):  
Machine Learning ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Domain Knowledge ◽  
Characteristic Curve ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Learning Framework ◽  
Diagnosis And Prognosis ◽  
Learning Classifier

AbstractPrognosis of the long-term functional outcome of traumatic brain injury is essential for personalized management of that injury. Nonetheless, accurate prediction remains unavailable. Although machine learning has shown promise in many fields, including medical diagnosis and prognosis, such models are rarely deployed in real-world settings due to a lack of transparency and trustworthiness. To address these drawbacks, we propose a machine learning-based framework that is explainable and aligns with clinical domain knowledge. To build such a framework, additional layers of statistical inference and human expert validation are added to the model, which ensures the predicted risk score’s trustworthiness. Using 831 patients with moderate or severe traumatic brain injury to build a model using the proposed framework, an area under the receiver operating characteristic curve (AUC) and accuracy of 0.8085 and 0.7488 were achieved, respectively, in determining which patients will experience poor functional outcomes. The performance of the machine learning classifier is not adversely affected by the imposition of statistical and domain knowledge “checks and balances”. Finally, through a case study, we demonstrate how the decision made by a model might be biased if it is not audited carefully.

scholarly journals Machine Learning Framework for the Estimation of Average Speed in Rural Road Networks with OpenStreetMap Data

2020 ◽  
Vol 9 (11) ◽  
pp. 638
Author(s):  
Sina Keller ◽  
Raoul Gabriel ◽  
Johanna Guth
Keyword(s):  
Machine Learning ◽  
Domain Knowledge ◽  
Road Network ◽  
Reference Data ◽  
Coefficient Of Determination ◽  
Rural Roads ◽  
Average Speed ◽  
Learning Framework ◽  
Input Feature ◽  
Better Than

Average speed information, which is essential for routing applications, is often missing in the freely available OpenStreetMap (OSM) road network. In this contribution, we propose an estimation framework, including different machine learning (ML) models that estimate rural roads’ average speed based on current road information in OSM. We rely on three datasets covering two regions in Chile and Australia. Google Directions API data serves as reference data. An appropriate estimation framework is presented, which involves supervised ML models, unsupervised clustering, and dimensionality reduction to generate new input features. The regression performance of each model with different input feature modes is evaluated on each dataset. The best performing model results in a coefficient of determination R2=80.43%, which is significantly better than previous approaches relying on domain-knowledge. Overall, the potential of the ML-based estimation framework to estimate the average speed with OSM road network data is demonstrated. This ML-based approach is data-driven and does not require any domain knowledge. In the future, we intend to focus on the generalization ability of the estimation framework concerning its application in different regions worldwide. The implementation of our estimation framework for an exemplary dataset is provided on GitHub.

scholarly journals A hierarchical machine learning framework for the identification of automated construction

2021 ◽  
Vol 26 ◽  
pp. 591-623
Author(s):  
Aparna Harichandran ◽  
Benny Raphael ◽  
Abhijit Mukherjee
Keyword(s):  
Machine Learning ◽  
Monitoring System ◽  
Domain Knowledge ◽  
Progress Monitoring ◽  
Problem Formulation ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Hierarchical Framework ◽  
Automated Monitoring ◽  
Learning Framework

A robust monitoring system is essential for ensuring safety and reliability in automated construction. Activity recognition is one of the critical tasks in automated monitoring. Existing studies in this area have not fully exploited the potential for enhancing the performance of machine learning algorithms using domain knowledge, especially in problem formulation. This paper presents a hierarchical machine learning framework for improving the accuracy of identification of Automated Construction System (ACS) operations. The proposed identification framework arranges the operations to be identified in the form of a hierarchy and uses multiple classifiers that are organized hierarchically for separating the operation classes. It is tested on a laboratory prototype of an ACS, which follows a top-down construction method. The ACS consists of a set of lightweight and portable machinery designed to automate the construction of the structural frame of low-rise buildings . Accelerometers were deployed at critical locations on the structure. The acceleration data collected while operating the equipment were used to identify the operations through machine learning techniques. The performance of the proposed framework is compared with that of the conventional approach for equipment operation identification which involves a flat list of classes to be separated. The performance was comparable at the top level. However, the hierarchical framework outperformed the conventional one when fine levels of operations were identified. The versatility and noise tolerance of the hierarchical framework are also reported. Results demonstrate that the framework is robust, and it is feasible to identify the ACS operations precisely. Although the proposed framework is validated on a full-scale prototype of the ACS, the effects of strong ambient disturbances on actual construction sites have not been evaluated. This study will support the development of an automated monitoring system and assist the main operator to ensure safe operations. The high-level operation details collected for this purpose can also be utilised for project performance assessment and progress monitoring. The potential application of the proposed hierarchical framework in the operation recognition of conventional construction equipment is also outlined.

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