Using Support Vector Machines and Rough Sets Theory for Classifying Faulty Types of Diesel Engine

2007 ◽  
pp. 550-561
Author(s):  
Ping-Feng Pai ◽  
Yu-Ying Huang
Keyword(s):  
Support Vector Machines ◽  
Diesel Engine ◽  
Rough Sets ◽  
Support Vector ◽  
Rough Sets Theory ◽  
Vector Machines ◽  
Sets Theory

Twin Support Vector Machines Based on Rough Sets

2012 ◽  
Vol 6 (20) ◽  
pp. 493-500 ◽  
Author(s):  
Junzhao Yu ◽  
Shifei Ding ◽  
Fengxiang Jin ◽  
Huajuan Huang ◽  
Youzhen Han
Keyword(s):  
Support Vector Machines ◽  
Rough Sets ◽  
Support Vector ◽  
Twin Support Vector Machines ◽  
Vector Machines

SOFT SENSOR MODELING FOR VACUUM OIL PURIFICATION MACHINE BASED ON LSSVM

2008 ◽  
Vol 07 (01) ◽  
pp. 141-144 ◽  
Author(s):  
CHUAN LI ◽  
SHILONG WANG ◽  
XIANMING ZHANG ◽  
LING KANG ◽  
JIANJUN MIN
Keyword(s):  
Rough Sets ◽  
Soft Sensor ◽  
Industrial Processes ◽  
Support Vector ◽  
Rough Sets Theory ◽  
Sensor Model ◽  
Oil Purification ◽  
Sets Theory ◽  
Primary Variable ◽  
Sensor Modeling

According to the secondary variables acquired from industrial processes, a Least Squares Support Vector Machine (LSSVM) based model is proposed for the primary variable soft sensing. The Rough Sets Theory is firstly employed to compress values and attributes of the secondary variables. Then the LSSVM is delivered for the primary variable nonlinear estimating. The method is applied for the vacuum oil purification machine. The moisture content in oil, a hard-to-be-measured primary variable, is computed from the soft sensor model. The result shows that the proposed method features a faster and more precise approximation ability.

Research on Extracting Vehicle Fault Symptoms Based on Rough Sets Theory in Mechanical Engineering

2013 ◽  
Vol 644 ◽  
pp. 110-114
Author(s):  
Chang Shun Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Diesel Engine ◽  
Network Theory ◽  
Rough Sets ◽  
Fuzzy Theory ◽  
Rough Sets Theory ◽  
Combined Application ◽  
Neural Network Theory ◽  
Sets Theory

This paper gives a brief introduction of rough sets theory. Taking the fault diagnosis of the diesel engine valve clearance for example, it discusses about the basic algorithms and methods to extract fault symptoms based on rough sets theory. The conclusion shows the possibility to develop intelligent fault diagnosis with the combined application of fuzzy theory and neural network theory.

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