Support Vector Machine for Misalignment Fault Classification Under Different Loading Conditions Using Vibro-Acoustic Sensor Data Fusion

2022 ◽  
Author(s):  
S. Patil ◽  
A.K. Jalan ◽  
A.M. Marathe
Keyword(s):  
Support Vector Machine ◽  
Data Fusion ◽  
Sensor Data ◽  
Fault Classification ◽  
Support Vector ◽  
Acoustic Sensor ◽  
Loading Conditions ◽  
Sensor Data Fusion ◽  
Misalignment Fault

scholarly journals Intelligent Control of Bulk Tobacco Curing Schedule Using LS-SVM- and ANFIS-Based Multi-Sensor Data Fusion Approaches

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1778 ◽  
Author(s):  
Juan Wu ◽  
Simon X. Yang
Keyword(s):  
Data Fusion ◽  
Sensor Data ◽  
Support Vector ◽  
Curing Process ◽  
Inference System ◽  
Tobacco Leaves ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Fusion Scheme ◽  
Hybrid Fusion

The bulk tobacco flue-curing process is followed by a bulk tobacco curing schedule, which is typically pre-set at the beginning and might be adjusted by the curer to accommodate the need for tobacco leaves during curing. In this study, the controlled parameters of a bulk tobacco curing schedule were presented, which is significant for the systematic modelling of an intelligent tobacco flue-curing process. To fully imitate the curer’s control of the bulk tobacco curing schedule, three types of sensors were applied, namely, a gas sensor, image sensor, and moisture sensor. Feature extraction methods were given forward to extract the odor, image, and moisture features of the tobacco leaves individually. Three multi-sensor data fusion schemes were applied, where a least squares support vector machines (LS-SVM) regression model and adaptive neuro-fuzzy inference system (ANFIS) decision model were used. Four experiments were conducted from July to September 2014, with a total of 603 measurement points, ensuring the results’ robustness and validness. The results demonstrate that a hybrid fusion scheme achieves a superior prediction performance with the coefficients of determination of the controlled parameters, reaching 0.9991, 0.9589, and 0.9479, respectively. The high prediction accuracy made the proposed hybrid fusion scheme a feasible, reliable, and effective method to intelligently control over the tobacco curing schedule.

scholarly journals A Multi-Sensor Data Fusion Approach for Atrial Hypertrophy Disease Diagnosis Based on Characterized Support Vector Hyperspheres

Sensors ◽  
2017 ◽  
Vol 17 (9) ◽  
pp. 2049 ◽  
Author(s):  
Yungang Zhu ◽  
Dayou Liu ◽  
Radu Grosu ◽  
Xinhua Wang ◽  
Hongying Duan ◽  
...  
Keyword(s):  
Data Fusion ◽  
Disease Diagnosis ◽  
Sensor Data ◽  
Support Vector ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Fusion Approach

Sensor Data Fusion by Support Vector Regression Methodology—A Comparative Study

2015 ◽  
Vol 15 (2) ◽  
pp. 850-854 ◽  
Author(s):  
Shahaboddin Shamshirband ◽  
Dalibor Petkovic ◽  
Hossein Javidnia ◽  
Abdullah Gani
Keyword(s):  
Data Fusion ◽  
Comparative Study ◽  
Support Vector Regression ◽  
Sensor Data ◽  
Support Vector ◽  
Sensor Data Fusion
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