scholarly journals The development of data mining methods criteria to identify failures of aircraft control surface actuator

2017 ◽  
Vol 112 ◽  
pp. 1007-1014 ◽  
Author(s):  
G.S. Veresnikov ◽  
A.V. Skryabin
Keyword(s):  
Data Mining ◽  
Control Surface ◽  
Aircraft Control ◽  
Mining Methods

THE MODEL FOR THE ANALYSIS OF PHARMACOEPIDEMIOLOGICAL DATA BASED ON DATA MINING METHODS

2017 ◽  
Vol 19 (10) ◽  
pp. 289-298
Author(s):  
I.M. Burykin ◽  
◽  
G.N. Aleeva ◽  
R.Kh. Khafizianova ◽  
◽  
...  
Keyword(s):  
Data Mining ◽  
Mining Methods

Research on diagnostic strategy for faults in VRF air conditioning system using hybrid data mining methods

2021 ◽  
pp. 111144
Author(s):  
Yuzhou Wang ◽  
Zhengfei Li ◽  
Huanxin Chen ◽  
Jianxin Zhang ◽  
Qian Liu ◽  
...  
Keyword(s):  
Data Mining ◽  
Air Conditioning ◽  
Diagnostic Strategy ◽  
Air Conditioning System ◽  
Hybrid Data ◽  
Mining Methods

scholarly journals IEEE Access Special Section Editorial: Advanced Data Mining Methods for Social Computing

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 228598-228604
Author(s):  
Yongqiang Zhao ◽  
Shirui Pan ◽  
Jia Wu ◽  
Huaiyu Wan ◽  
Huizhi Liang ◽  
...  
Keyword(s):  
Data Mining ◽  
Social Computing ◽  
Special Section ◽  
Mining Methods

scholarly journals A Holistic Approach for Quality Oriented Maintenance Planning Supported by Data Mining Methods

Procedia CIRP ◽  
2016 ◽  
Vol 57 ◽  
pp. 259-264 ◽  
Author(s):  
Robert Glawar ◽  
Zsolt Kemeny ◽  
Tanja Nemeth ◽  
Kurt Matyas ◽  
Laszlo Monostori ◽  
...  
Keyword(s):  
Data Mining ◽  
Holistic Approach ◽  
Maintenance Planning ◽  
Mining Methods

A data mining approach to the diagnosis of failure modes for two serial fastened sandwich composite plates

2016 ◽  
Vol 51 (20) ◽  
pp. 2853-2862 ◽  
Author(s):  
Serkan Ballı
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Failure Modes ◽  
Composite Plates ◽  
Study Data ◽  
Sandwich Composite ◽  
Support Vector ◽  
Geometrical Parameters ◽  
Mining Methods

The aim of this study is to diagnose and classify the failure modes for two serial fastened sandwich composite plates using data mining techniques. The composite material used in the study was manufactured using glass fiber reinforced layer and aluminum sheets. Obtained results of previous experimental study for sandwich composite plates, which were mechanically fastened with two serial pins or bolts were used for classification of failure modes. Furthermore, experimental data from previous study consists of different geometrical parameters for various applied preload moments as 0 (pinned), 2, 3, 4, and 5 Nm (bolted). In this study, data mining methods were applied by using these geometrical parameters and pinned/bolted joint configurations. Therefore, three geometrical parameters and 100 test data were used for classification by utilizing support vector machine, Naive Bayes, K-Nearest Neighbors, Logistic Regression, and Random Forest methods. According to experiments, Random Forest method achieved better results than others and it was appropriate for diagnosing and classification of the failure modes. Performances of all data mining methods used were discussed in terms of accuracy and error ratios.

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