Fault Diagnosis Analysis with Support Vector Regression and Particle Swarm Optimization Algorithm
The fault diagnosis model with support vector regression (SVR) and particle swarm optimization algorithm (POSA) for is proposed. The novel structure model has higher accuracy and faster convergence speed. We construct the network structure, and give the algorithm flow. The impact factor of fault behaviors is discussed. With the ability of strong self-learning and faster convergence, this fault detection method can detect various fault behaviors rapidly and effectively by learning the typical fault characteristic information. Utilizing the character that principal components analysis algorithm can keep the discern ability of original dataset after reduction, the reduces of the original dataset are calculated and used to train individual SVR for ensemble, and consequently, increase the detection accuracy. To validate the effectiveness of the proposed method, simulation experiments are performed based on the electronic circuit dataset. The results show that the proposed method is a promised method owning to its high diversity, high detection accuracy and faster speed in fault diagnosis.
Support Vector Regression Principal Components Analysis Fault Diagnosis Reduction Particle Swarm Optimization Algorithm
WenJie Tian JiCheng Liu
Beijing Automation Institute of Beijing Union University, Beijing, China, 100101
国际会议
The 22nd China Control and Decision Conference(2010年中国控制与决策会议)
徐州
英文
3370-3374
2010-05-26（万方平台首次上网日期，不代表论文的发表时间）