Dissertation > Excellent graduate degree dissertation topics show
Research on Gas Sensor Fault Diagnosis Based on Rough Set and Evidence Theory
Author: HuYaZuo
Tutor: FuHua
School: Liaoning Technical University
Course: Control Theory and Control Engineering
Keywords: Rough Set D-S evidence theory Troubleshooting Information Fusion Gas sensor
CLC: TP212
Type: Master's thesis
Year: 2009
Downloads: 124
Quote: 0
Read: Download Dissertation
Abstract
|
Gas sensor as a coal mine safety monitoring system, a key component of its performance is good or bad is essential, if false , it will cause huge economic losses and casualties , so study the fault diagnosis technology has important practical significance. Thesis for the purpose of solving the above problems , the use of evidence based on rough set theory and DS combined gas sensor fault diagnosis method to study the rough set theory and DS evidence theory advantages and disadvantages , and the feasibility of the two theories were used in combination analysis, development of this technology in the gas sensor fault diagnosis among embodiment . First, the gas sensor output value and its impact on the type of fault analysis , fault diagnosis model established ; Secondly, the model for discrete data sample processing , the establishment of decision-making table and apply the matrix can be distinguished attribute decision table reduction ; again , about reduced decision table data into DS evidence theory among the basic probability assignment based decision rules for evidence synthesis ; Finally, in the experimental stage , the application of the theory to make a gas sensor troubleshooting. Thesis focused to achieve a rough set theory and DS evidence combining information fusion process , the experimental results show that the method can improve the thesis coal mine identification of environmental information and decision-making effect can be effective for gas sensor fault diagnosis , and effectively improve a fault diagnosis algorithm speed and accuracy, the fault diagnosis field has good application prospects.
|
Related Dissertations
- Tongue Feature Extraction and Research of Fusion Classification,TP391.41
- Research on Joint Target Detection for Dual-Sensor Image and System Implementation,TP391.41
- Fault Diagnosis Method Based on Support Vector Machine,TP18
- Research on Clustering Algorithm Based on Genetic Algorithm and Rough Set Theory,TP18
- Based on Rough Set of Urban Areas When Traffic Green Control System Research,TP18
- Incremental rough set attribute reduction,TP18
- Calculation of Knowledge Granulation and Study of Its Application in Attribute Reduction,TP18
- Research of License Plate Recognition Based on Rough Sets and Fuzzy SVM,TP391.41
- Synthesis of SnO2 by Flame Spray Pyrolysis: Structures Design and the Gas Sensing Properties,TB383.1
- Research of Absorbed Fiber-optic CO Gas Sensor Signal Processing,TP212
- Application of Rough Set and Flex in Mid-long Term Runoff Forecasting,P338
- Importance of the study of the physical and chemical indicators based on rough set theory Daqu,TS262.3
- Detection of Pollutant Gas by the Ionic Liquids Film for Cultural Heritage Preservation,TP212.9
- The software design and implementation of the the clothing quality prediction system,TP311.52
- HMM-based Connection between the Social Network Analysis,F49
- Fiber Methane Gas Sensor System and the Light Source Driver,TP212.14
- Optical fiber methane gas concentration detection system design and implementation,TP212
- Band Entropy Method and Its Application to Fault Diagnosis of Rolling Bearings,TH165.3
- Evidence-based cell model theory and semantic multi-tag music emotion recognition,TP391.41
- Water quality time series data processing and Early Warning System Construction Research Database,TP274
- Based on information fusion safety assessment of genetically modified foods,TS201.6
CLC: > Industrial Technology > Automation technology,computer technology > Automation technology and equipment > Automation components,parts > Transmitter ( converter),the sensor
© 2012 www.DissertationTopic.Net Mobile
|