Fault diagnosis of sensor by chaos particle swarm optimization algorithm and support vector machine
Fault diagnosis of sensor by chaos particle swarm optimization algorithm and support vector machine
复制标题
混沌粒子群优化算法和支持向量机传感器故障诊断
DOI：
10.1016/j.eswa.2011.02.043
复制
发表时间：
2011-08-01
影响因子：
8.5
通讯作者：
Jiang Ting
中科院分区：
文献类型：
--
作者：
Zhao Chenglin;Sun Xuebin;Jiang Ting
及时、准确地诊断传感器故障对于提高系统的可靠运行至关重要。本文提出了利用混沌粒子群优化算法和支持向量机对传感器进行故障诊断，其中选择混沌粒子群优化算法来确定支持向量机的参数。混沌粒子群优化是粒子群优化的一种改进，不仅可以避免搜索陷入局部最优，而且利用混沌队列有利于快速搜索最优解。以无线传感器为研究对象，应用其冲击、偏置、短路、移位四种故障类型，与其他诊断方法相比，检验CPSO-SVM的诊断能力。诊断结果表明，CPSO-SVM比PSO-SVM和BP神经网络具有更高的无线传感器诊断精度。 (C) 2011 Elsevier Ltd. 保留所有权利。
Fault diagnosis of sensor timely and accurately is very important to improve the reliable operation of systems. In the study, fault diagnosis of sensor by chaos particle swarm optimization algorithm and support vector machine is presented in the paper, where chaos particle swarm optimization is chosen to determine the parameters of SVM. Chaos particle swarm optimization is a kind of improved particle swarm optimization, which can not only avoid the search being trapped in local optimum and but also help to search the optimum quickly by using chaos queues. The wireless sensor is employed as research object, and its four fault types including shock, biasing, short circuit and shifting are applied to test the diagnostic ability of CPSO-SVM compared with other diagnostic methods. The diagnostic results show that CPSO-SVM has higher diagnostic accuracy of wireless sensor than PSO-SVM and BP neural network. (C) 2011 Elsevier Ltd. All rights reserved.