| 引用本文: | 程加堂,段志梅,艾莉,熊燕.基于QPSO-BP和改进D-S的水电机组振动故障诊断[J].电力系统保护与控制,2015,43(19):66-71.[点击复制] |
| CHENG Jiatang,DUAN Zhimei,AI Li,XIONG Yan.Vibration fault diagnosis for hydroelectric generating unit based on QPSO-BP and modified D-S theory[J].Power System Protection and Control,2015,43(19):66-71[点击复制] |
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| 摘要: |
| 为提高水电机组振动故障诊断的准确性，提出了一种基于改进D-S证据理论融合量子粒子群优化BP神经网络(QPSO-BP)的诊断方法。根据水电机组常见的振动故障类型，采用3个惯性权值随机调整的QPSO-BP网络分别对其进行初级诊断，并作为独立证据体应用于D-S理论的合成之中，实现了基本概率赋值的客观化。针对标准D-S无法合成高度冲突证据的缺陷，通过计算权值矩阵对其进行修正。实例分析表明，和3个初级诊断模型及标准D-S合成法相比，所提方法可以有效识别机组的振动故障，具有较高的诊断准确率。 |
| 关键词: 水电机组 振动 故障诊断 量子粒子群优化BP神经网络 改进D-S证据理论 |
| DOI：10.7667/j.issn.1674-3415.2015.19.011 |
| 投稿时间：2014-12-17修订日期：2015-05-07 |
| 基金项目:云南省教育厅科学基金项目(2012Y450) |
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| Vibration fault diagnosis for hydroelectric generating unit based on QPSO-BP and modified D-S theory |
| CHENG Jiatang,DUAN Zhimei,AI Li,XIONG Yan |
| (College of Engineering, Honghe University, Mengzi 661199, China) |
| Abstract: |
| In order to improve the accuracy of vibration fault diagnosis for hydroelectric generating unit, a method is proposed based on quantum particle swarm optimized BP neural network (QPSO-BP) which is fused by modified D-S evidence theory. According to the common vibration fault types, three QPSO-BP networks with inertia weight being adjusted randomly are used as its primary diagnosis models, then the independent bodies of evidence are applied to the synthesis of D-S theory, and the basic probability assignment is realized objectively. In view of the defects that standard D-S can not synthesize high conflict evidence, the weight matrix is calculated to improve it. Example analysis shows that the proposed method can effectively identify vibration fault of the unit, and has a high diagnostic accuracy compared with three primary diagnostic model and standard D-S theory. |
| Key words: hydroelectric generating unit vibration fault diagnosis quantum particle swarm optimized BP neural network (QPSO-BP) modified D-S evidence theory |
