HVAC Fault Diagnosis System Using Rough Set Theory and Support Vector Machine
ABSTRACT Preventive maintenance plays a very important role in the modern Heating, Ventilation and Air Conditioning (HVAC) systems for guaranteeing the thermal comfort, energy saving and reliability. The fault diagnosis on HVAC system is a difficult problem due to the complex structure of the HVAC and the presence of multi-excite sources. As the HVAC system fault information has inaccurate and uncertainty characteristic, A new kind of fault diagnosis system based on Rough Set Theory (RST) and Support Vector Machine (SVM) is presented in this paper. The hybrid model is integrated the advantages of RST effectively dealing with the uncertainty information and SVMpsilas greater generalization performance. The HVAC diagnosis experiment demonstrated that the solution can reduce the cost and raise the efficiency of diagnosis, and verified the feasibility of engineering application. As a result, the presented hybrid fault diagnosis method can help to maintain the health of the HVAC systems, reduce energy consumption and maintenance cost.
Conference Proceeding: Power Transformer Fault Diagnosis Based on Rough Set Theory and Support Vector Machine[show abstract] [hide abstract]
ABSTRACT: Power transformers are one of the most expensive components of electrical power plants and the failures of such transformers can result in serious power system issues, so fault diagnosis for power transformer is very important to insure the whole power system run normally. Based on fault attributes of transformers, there are a few works have been done on transformer fault diagnosis using such methods as neural network,bayesian,and so on. As the fault information of power transformers has uncertainty characteristic, in this paper, a novel approach based on rough set theory and SVM is proposed. Moreover, by comparing with the traditional methods like the neural network, there is less fault data discriminated by the rough set theory and SVM model and the accuracy for power transformer fault diagnosis is improved using our proposed model.Fuzzy Systems and Knowledge Discovery, 2007. FSKD 2007. Fourth International Conference on; 09/2007
Conference Proceeding: A New Model of Mine Hoist Fault Diagnosis Based on the Rough Set Theory[show abstract] [hide abstract]
ABSTRACT: Extraction of simple and effective rules for fault diagnosis is one of the most important issues needed to be addressed in fault diagnosis, because available information is often inconsistent and redundant. This paper presents a fault diagnosis model based on rough set theory. Firstly, this model can discretize fault continued attributes using a modified genetic algorithm. Then, reduce diagnosis rule by using heuristic algorithm of rough set theory, a set of diagnosis rules are generated and a rule database for fault diagnosis is established. Simulation results for fault diagnosis of mine hoist show that this method improves the accuracy rate of fault diagnosis, predigest the number of feature parameters and diagnostic rules, and reduces the cost of diagnosis, with more applicable than the classical RS-method in practical applications.Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2008. SNPD '08. Ninth ACIS International Conference on; 09/2008
Conference Proceeding: Fault Diagnosis System Based on Rough Set Theory and Support Vector Machine.[show abstract] [hide abstract]
ABSTRACT: The fault diagnosis on diesel engine is a difficult problem due to the complex structure of the engine and the presence of multi-excite sources. A new kind of fault diagnosis system based on Rough Set Theory and Support Vector Machine is proposed in the paper. Integrating the advantages of Rough Set Theory in effectively dealing with the uncertainty information and Support Vector Machine’s greater generalization performance. The diagnosis of a diesel demonstrated that the solution can reduce the cost and raise the efficiency of diagnosis, and verified the feasibility of engineering application.Fuzzy Systems and Knowledge Discovery, Second International Conference, FSKD 2005, Changsha, China, August 27-29, 2005, Proceedings, Part II; 01/2005