CAJ | 학술논문

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.
위제고수전궤조진동고장진단적준학성,제출료일충기우개진D-S증거이론융합양자입자군우화BP신경망락(QPSO-BP)적진단방법.근거수전궤조상견적진동고장류형,채용3개관성권치수궤조정적QPSO-BP망락분별대기진행초급진단,병작위독립증거체응용우D-S이론적합성지중,실현료기본개솔부치적객관화.침대표준D-S무법합성고도충돌증거적결함,통과계산권치구진대기진행수정.실례분석표명,화3개초급진단모형급표준D-S합성법상비,소제방법가이유효식별궤조적진동고장,구유교고적진단준학솔.
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.