Data Selection and Feature Engineering for the Application of Machine Learning to the Prediction of Gas Turbine Trip

2021 ◽  
Author(s):  
Enzo Losi ◽  
Mauro Venturini ◽  
Lucrezia Manservigi ◽  
Giuseppe Fabio Ceschini ◽  
Giovanni Bechini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gas Turbine ◽  
Peak Load ◽  
Learning Model ◽  
Remaining Useful Life ◽  
Data Selection ◽  
Feature Engineering ◽  
Homogeneous Groups ◽  
Machine Learning Model ◽  
Definition Of

Abstract A gas turbine trip is an unplanned shutdown, of which the consequences are business interruption and a reduction of equipment remaining useful life. Therefore, detection and identification of symptoms of trips would allow predicting its occurrence, thus avoiding damages and costs. The development of machine learning models able to predict gas turbine trip requires the definition of a set of target data and a procedure of feature engineering that improves machine learning generalization and effectiveness. This paper presents a methodology that focuses on the steps that precede the development of a machine learning model, i.e., data selection and feature engineering, which are the key for a successful predictive model. Data selection is performed by partitioning units into homogeneous groups according to different criteria, e.g., type, region of installation, and operation. A subsequent matching algorithm is applied to rotational speed data of multiple gas turbine units to identify start-ups and shutdowns so that the considered units can be partitioned according to their operation, i.e., base load or peak load. Feature engineering aims at creating features that improve machine learning model accuracy and reliability. First, the Discrete Fourier Transform is used to identify and remove from the time series the seasonal components, i.e., patterns that repeat with a given periodicity. Then, new features are created based on gas turbine domain knowledge in order to capture the complex interactions among system variables and trip occurrence. The outcomes of this paper are the definition of a set of target examples and the identification of a meaningful set of features suitable to develop a machine learning model aimed at predicting gas turbine trip.

Fuzzy based feature engineering architecture for sentiment analysis of medical discussion over online social networks

2021 ◽  
pp. 1-13
Author(s):  
C S Pavan Kumar ◽  
L D Dhinesh Babu
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Membership Function ◽  
Online Social Networks ◽  
Learning Model ◽  
Feature Engineering ◽  
Machine Learning Model ◽  
Social Media Platforms

Sentiment analysis is widely used to retrieve the hidden sentiments in medical discussions over Online Social Networking platforms such as Twitter, Facebook, Instagram. People often tend to convey their feelings concerning their medical problems over social media platforms. Practitioners and health care workers have started to observe these discussions to assess the impact of health-related issues among the people. This helps in providing better care to improve the quality of life. Dementia is a serious disease in western countries like the United States of America and the United Kingdom, and the respective governments are providing facilities to the affected people. There is much chatter over social media platforms concerning the patients’ care, healthy measures to be followed to avoid disease, check early indications. These chatters have to be carefully monitored to help the officials take necessary precautions for the betterment of the affected. A novel Feature engineering architecture that involves feature-split for sentiment analysis of medical chatter over online social networks with the pipeline is proposed that can be used on any Machine Learning model. The proposed model used the fuzzy membership function in refining the outputs. The machine learning model has obtained sentiment score is subjected to fuzzification and defuzzification by using the trapezoid membership function and center of sums method, respectively. Three datasets are considered for comparison of the proposed and the regular model. The proposed approach delivered better results than the normal approach and is proved to be an effective approach for sentiment analysis of medical discussions over online social networks.

Using Machine Learning to Increase Model Performance for a Gas Turbine System

2020 ◽  
Author(s):  
Samuel M. Hipple ◽  
Zachary T. Reinhart ◽  
Harry Bonilla-Alvarado ◽  
Paolo Pezzini ◽  
Kenneth Mark Bryden
Keyword(s):  
Machine Learning ◽  
Power Plant ◽  
Gas Turbine ◽  
Clean Energy ◽  
Learning Model ◽  
Operating Conditions ◽  
Plant Performance ◽  
Outlet Temperature ◽  
Machine Learning Model ◽  
Critical Components

Abstract With increasing regulation and the push for clean energy, the operation of power plants is becoming increasingly complex. This complexity combined with the need to optimize performance at base load and off-design condition means that predicting power plant performance with computational modeling is more important than ever. However, traditional modeling approaches such as physics-based models do not capture the true performance of power plant critical components. The complexity of factors such as coupling, noise, and off-design operating conditions makes the performance prediction of critical components such as turbomachinery difficult to model. In a complex system, such as a gas turbine power plant, this creates significant disparities between models and actual system performance that limits the detection of abnormal operations. This study compares machine learning tools to predict gas turbine performance over traditional physics-based models. A long short-term memory (LSTM) model, a form of a recurrent neural network, was trained using operational datasets from a 100 kW recuperated gas turbine power system designed for hybrid configuration. The LSTM turbine model was trained to predict shaft speed, outlet pressure, and outlet temperature. The performance of both the machine learning model and a physics-based model were compared against experimental data of the gas turbine system. Results show that the machine learning model has significant advantages in prediction accuracy and precision compared to a traditional physics-based model when fed facility data as an input. This advantage of predicting performance by machine learning models can be used to detect abnormal operations.

Analysis of Gas Turbine Compressor Degradation Using Random Forest-based Machine Learning Model

2021 ◽  
Vol 45 (11) ◽  
pp. 605-612
Author(s):  
Myeonghwan Bang ◽  
Haesu Kang ◽  
Kyuheon Lee ◽  
Chansu Oh ◽  
Woosung Choi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Gas Turbine ◽  
Learning Model ◽  
Machine Learning Model ◽  
Gas Turbine Compressor

scholarly journals Feature engineering with clinical expert knowledge: A case study assessment of machine learning model complexity and performance

PLoS ONE ◽  
2020 ◽  
Vol 15 (4) ◽  
pp. e0231300
Author(s):  
Kenneth D. Roe ◽  
Vibhu Jawa ◽  
Xiaohan Zhang ◽  
Christopher G. Chute ◽  
Jeremy A. Epstein ◽  
...  
Keyword(s):  
Machine Learning ◽  
Expert Knowledge ◽  
Learning Model ◽  
Model Complexity ◽  
Feature Engineering ◽  
Machine Learning Model ◽  
And Performance ◽  
Clinical Expert

scholarly journals Feature Engineering and Machine Learning Model Comparison for Malicious Activity Detection in the DNS-Over-HTTPS Protocol

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Matthew Behnke ◽  
Nathan Briner ◽  
Drake Cullen ◽  
Katelynn Schwerdtfeger ◽  
Jackson Warren ◽  
...  
Keyword(s):  
Machine Learning ◽  
Model Comparison ◽  
Learning Model ◽  
Feature Engineering ◽  
Activity Detection ◽  
Malicious Activity ◽  
Machine Learning Model

scholarly journals High Availability of Datacenter in Karachi, Pakistan

2021 ◽  
Vol 10 (2) ◽  
pp. 1134-1135
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Data Center ◽  
Learning Model ◽  
High Availability ◽  
Central Issue ◽  
Feature Engineering ◽  
The Core ◽  
Machine Learning Model ◽  
Microsoft Azure

The datacenter is the core infrastructure of today's world. Every data center should have many resources and applications that are running for several decades or even more. Many failures happen in the physical datacenter in on-premises environments. In this research paper evaluating the datacenter by having the dataset provided by Premier Systems (Pvt.) Ltd. This dataset is having all the failures and datacenter related issues from Jan-2016 to Dec-2019 in Karachi, Pakistan. This research performed the Linear Regression via the Microsoft Azure Machine Learning Studio for the machine learning model. It would allow us to know which fault will more and which is not for the concern requirement. This experiment would have the Feature Engineering feature in Microsoft Azure Machine Learning Studio, which will automatically apply the filters required. After knowing which the central issue are related to any physical data center in Karachi. This research allows to handle the required precaution in datacenters of Karachi, Pakistan.

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

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