l_p范数约束的模拟电路3层多核故障诊断模型

Three-layer Multiple Kernel Fault Diagnosis Model with l_p-norm Constraint for Analog Circuit

为提升模拟电路故障隔离精度,结合基于故障特征间一维模糊度的特征选择算法,提出一种改进的l_p范数约束多核超限学习机诊断模型。该模型通过将带权分类误差融入超限学习机优化目标函数中,基于自适应Boosting策略构建了一种3层多核学习框架。在新框架下通过自适应调整训练样本的权重分布,使得每层框架能够聚焦于不同故障样本,进而提升诊断模型的辨识力。通过对2个电路实例的诊断,结果表明:所提模型在不同范数约束下具有近似一致的诊断性能;当故障属性单一时,在平衡漏警、虚警的同时,能够显著提升诊断正确率;当多种属性的故障并存时,能够将难以辨识的故障更加准确地隔离到少数模糊组中。

In order to improve the fault diagnostic accuracy of analog circuit,an improved lp-norm multiple kernel extreme learning machine（ ELM） diagnostic model is proposed based on the feature selection algorithm with one-dimensional ambiguity among fault features. In this model,the weighted classification error is incorporated into kernel ELM optimized objective function,and then a three-layer multiple kernel learning framework is constructed based on adaptive boosting（ Ada Boost） algorithm,in which the training sample weights are adaptively adjusted so that the every layer in the model can focus on the different training samples. The proposed model provides an excellent strategy to improve the identifiability of classifier. Experimental results of two analog circuits show that the proposed model can achieve approximately consistent diagnostic performance under the constraints of different norms. For a fault with a single attribute,the diagnostic accuracy can be significantly improved,meanwhile the missing alarm and false alarm can be effectively balanced. For a fault with multiple attributes,the faults which are difficult to be identified can be accurately isolated into relevant ambiguity groups.