A Data Mining Approach for Generation of Control Signatures

2002 ◽  
Vol 124 (4) ◽  
pp. 923-926 ◽  
Author(s):  
Andrew Kusiak
Keyword(s):  
Data Mining ◽  
Metal Forming ◽  
Rough Set Theory ◽  
Decision Rules ◽  
Forming Process ◽  
Data Mining Algorithms ◽  
Data Mining Approach ◽  
Metal Forming Process ◽  
Material Forming ◽  
Mining Algorithms

Data mining offers methodologies and tools for data analysis, discovery of new knowledge, and autonomous process control. This paper introduces basic data mining algorithms. An approach based on rough set theory is used to derive associations among control parameters and the product quality in the form of decision rules. The model presented in the paper produces control signatures leading to good quality products of a metal forming process. The computational results reported in the paper indicate that data mining opens a new avenue for decision-making in material forming industry.

Fault Monitoring of Wind Turbine Generator Brushes: A Data-Mining Approach

2012 ◽  
Vol 134 (2) ◽  
Author(s):  
Anoop Verma ◽  
Andrew Kusiak
Keyword(s):  
Data Mining ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Turbine Generator ◽  
Wind Turbine Generator ◽  
Data Mining Algorithms ◽  
Data Mining Approach ◽  
Carbon Brushes ◽  
Critical Components ◽  
Mining Algorithms

Components of wind turbines are subjected to asymmetric loads caused by variable wind conditions. Carbon brushes are critical components of the wind turbine generator. Adequately maintaining and detecting abnormalities in the carbon brushes early is essential for proper turbine performance. In this paper, data-mining algorithms are applied for early prediction of carbon brush faults. Predicting generator brush faults early enables timely maintenance or replacement of brushes. The results discussed in this paper are based on analyzing generator brush faults that occurred on 27 wind turbines. The datasets used to analyze faults were collected from the supervisory control and data acquisition (SCADA) systems installed at the wind turbines. Twenty-four data-mining models are constructed to predict faults up to 12 h before the actual fault occurs. To increase the prediction accuracy of the models discussed, a data balancing approach is used. Four data-mining algorithms were studied to evaluate the quality of the models for predicting generator brush faults. Among the selected data-mining algorithms, the boosting tree algorithm provided the best prediction results. Research limitations attributed to the available datasets are discussed.

scholarly journals Prediction for rice yield using data mining approach in Ranga Reddy district of Telangana, India

2021 ◽  
Vol 23 (2) ◽  
pp. 242-248
Author(s):  
BABY AKULA ◽  
R.S.PARMAR ◽  
M. P. RAJ ◽  
K. INDUDHAR REDDY
Keyword(s):  
Data Mining ◽  
Logistic Model ◽  
Yield Data ◽  
Data Mining Algorithms ◽  
Data Mining Approach ◽  
Rice Yields ◽  
Using Data ◽  
Mlp Classifier ◽  
Mining Algorithms ◽  
Best Fit

In order to explore the possibility of crop estimation, data mining approach being multidisciplinary was followed. The district of Ranga Reddy, Telangana State, India has been chosen for the study and its year wise average yield data of rice and daily weather over a period of 31 years i.e. from 1988-2019 (30th to 47th Standard Meteorological Weeks). Data mining tool WEKA (V3.8.1). Min- Max Normalization technique followed by Feature Selection algorithm, ‘cfsSubsetEval’ was also adopted to improve quality and accuracy of data mining algorithms. Thus, after cleaning and sorting of data, five classifiers viz., Logistic, MLP (Multi Layer Perceptron), J48 Classifier, LMT (Logistic Model Trees) and PART Classifier were employed over the trained data. The results indicated that the function based and tree based models have better performance over rule based model. In case of function based two models examined, viz., Logistic and MLP, the later performed better over Logistic model. Between tree based two models, LMT performed better over J48. Thus, MLP classifier model found to be the best fit model in predicting rice yields as it recorded an accuracy of 74.19 %, sensitivity of 0.742 and precision of 0.743 as compared with other models. The MLP has also achieved the highest F1 score of (0.742) and MCC (0.581).

scholarly journals An In-Depth Analysis of The Data Mining Algorithms and Their Effective Applicability in Health and Biomedical Informatics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Deeya Tangri
Keyword(s):  
Data Mining ◽  
Health Care ◽  
Medical Science ◽  
Massive Data ◽  
Medical Field ◽  
Data Mining Algorithms ◽  
Data Mining Approach ◽  
Depth Analysis ◽  
Data Extract ◽  
Mining Algorithms

Nowadays, the Health care industry is one of the fastest-growing industries. As we already know, health care has researched very widely, introducing many medical data that is not easy to mine. Data mining is an approach that helps to discover essential data from massive data or collection of data. So, in medical Science, there is a need for tools that help analyses the data, extract the significant result from massive data, and discover efficient use of information. Generally, three things are mandatory in medical for every patient. First is patient details, diagnosis and medications. Converting these data into a basic pattern for predicting the patient disease helps in early diagnosis. This research mainly focuses on the data mining approach, which is widely considered in the medical field.

Hybrid Data Mining for Medical Applications

2009 ◽  
pp. 523-543
Author(s):  
Syed Zahid Hassan ◽  
Brijesh Verma
Keyword(s):  
Data Mining ◽  
Medical Applications ◽  
Medical Database ◽  
Data Repositories ◽  
Data Mining Algorithms ◽  
Hybrid Approaches ◽  
Data Mining Approach ◽  
Hybrid Data ◽  
Mining Algorithms ◽  
Existing Data

This chapter focuses on hybrid data mining algorithms and their use in medical applications. It reviews existing data mining algorithms and presents a novel hybrid data mining approach, which takes advantage of intelligent and statistical modeling of data mining algorithms to extract meaningful patterns from medical data repositories. Various hybrid combinations of data mining algorithms are formulated and tested on a benchmark medical database. The chapter includes the experimental results with existing and new hybrid approaches to demonstrate the superiority of hybrid data mining algorithms over standard algorithms.

scholarly journals An Entropy-Based Car Failure Detection Method Based on Data Acquisition Pipeline

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 426 ◽  
Author(s):  
Bartosz Kowalik ◽  
Marcin Szpyrka
Keyword(s):  
Data Mining ◽  
Detection Method ◽  
Electronic Devices ◽  
Failure Detection ◽  
Point Of View ◽  
Electronic Control ◽  
Data Mining Algorithms ◽  
Data Mining Approach ◽  
Control Units ◽  
Mining Algorithms

Modern cars are equipped with plenty of electronic devices called Electronic Control Units (ECU). ECUs collect diagnostic data from a car’s components such as the engine, brakes etc. These data are then processed, and the appropriate information is communicated to the driver. From the point of view of safety of the driver and the passengers, the information about the car faults is vital. Regardless of the development of on-board computers, only a small amount of information is passed on to the driver. With the data mining approach, it is possible to obtain much more information from the data than it is provided by standard car equipment. This paper describes the environment built by the authors for data collection from ECUs. The collected data have been processed using parameterized entropies and data mining algorithms. Finally, we built a classifier able to detect a malfunctioning thermostat even if the car equipment does not indicate it.

Prediction of Status Patterns of Wind Turbines: A Data-Mining Approach

2011 ◽  
Vol 133 (1) ◽  
Author(s):  
Andrew Kusiak ◽  
Anoop Verma
Keyword(s):  
Data Mining ◽  
Wind Turbines ◽  
Association Rule ◽  
Performance Monitoring ◽  
Prediction Models ◽  
Rule Mining ◽  
Data Mining Algorithms ◽  
Data Mining Approach ◽  
The Status ◽  
Mining Algorithms

This paper presents the application of data-mining techniques for identification and prediction of status patterns in wind turbines. Early prediction of status patterns benefits turbine maintenance by indicating the deterioration of components. An association rule mining algorithm is used to identify frequent status patterns of turbine components and systems that are in turn predicted using historical wind turbine data. The status patterns are predicted at six time periods spaced at 10 min intervals. The prediction models are generated by five data-mining algorithms. The random forest algorithm has produced the best prediction results. The prediction results are used to develop a component performance monitoring scheme.

Novel Adverse Events of Iloperidone: A Disproportionality Analysis in US Food and Drug Administration Adverse Event Reporting System (FAERS) Database

2019 ◽  
Vol 14 (1) ◽  
pp. 21-26 ◽  
Author(s):  
Viswam Subeesh ◽  
Eswaran Maheswari ◽  
Hemendra Singh ◽  
Thomas Elsa Beulah ◽  
Ann Mary Swaroop
Keyword(s):  
Data Mining ◽  
Adverse Event ◽  
Adverse Events ◽  
Reporting System ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Disproportionality Analysis ◽  
Positive Signal ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Background: The signal is defined as “reported information on a possible causal relationship between an adverse event and a drug, of which the relationship is unknown or incompletely documented previously”. Objective: To detect novel adverse events of iloperidone by disproportionality analysis in FDA database of Adverse Event Reporting System (FAERS) using Data Mining Algorithms (DMAs). Methodology: The US FAERS database consists of 1028 iloperidone associated Drug Event Combinations (DECs) which were reported from 2010 Q1 to 2016 Q3. We consider DECs for disproportionality analysis only if a minimum of ten reports are present in database for the given adverse event and which were not detected earlier (in clinical trials). Two data mining algorithms, namely, Reporting Odds Ratio (ROR) and Information Component (IC) were applied retrospectively in the aforementioned time period. A value of ROR-1.96SE>1 and IC- 2SD>0 were considered as the threshold for positive signal. Results: The mean age of the patients of iloperidone associated events was found to be 44years [95% CI: 36-51], nevertheless age was not mentioned in twenty-one reports. The data mining algorithms exhibited positive signal for akathisia (ROR-1.96SE=43.15, IC-2SD=2.99), dyskinesia (21.24, 3.06), peripheral oedema (6.67,1.08), priapism (425.7,9.09) and sexual dysfunction (26.6-1.5) upon analysis as those were well above the pre-set threshold. Conclusion: Iloperidone associated five potential signals were generated by data mining in the FDA AERS database. The result requires an integration of further clinical surveillance for the quantification and validation of possible risks for the adverse events reported of iloperidone.

scholarly journals Simple Correlation Between Weather and COVID-19 Pandemic Using Data Mining Algorithms

2020 ◽  
Vol 982 ◽  
pp. 012015
Author(s):  
Ari Fadli ◽  
Azis Wisnu Widhi Nugraha ◽  
Muhammad Syaiful Aliim ◽  
Acep Taryana ◽  
Yogiek Indra Kurniawan ◽  
...  
Keyword(s):  
Data Mining ◽  
Simple Correlation ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Mining Algorithms
Sign in / Sign up

Export Citation Format

Share Document