CAJ | 학술논문

　　针对有效采样点法提取故障特征时冗余信息多、易造成维数灾等问题，提出利用改进的二进制粒子群算法提取故障特征。研究粒子群优化算法和二进制粒子群优化算法的差异以及在故障特征提取方面存在的不足，通过改进群体极值的更新方式避免搜索结果陷入局部最优。以Sallen-Key带通滤波器为诊断实例，完成9类模拟电路故障模式的特征提取。结果表明：通过该方法进行特征提取可有效降低故障诊断模型的复杂性，与二进制粒子群优化算法相比，该方法在特征维度和诊断准确率上具有明显的优势。
　　침대유효채양점법제취고장특정시용여신식다、역조성유수재등문제，제출이용개진적이진제입자군산법제취고장특정。연구입자군우화산법화이진제입자군우화산법적차이이급재고장특정제취방면존재적불족，통과개진군체겁치적경신방식피면수색결과함입국부최우。이Sallen-Key대통려파기위진단실례，완성9류모의전로고장모식적특정제취。결과표명：통과해방법진행특정제취가유효강저고장진단모형적복잡성，여이진제입자군우화산법상비，해방법재특정유도화진단준학솔상구유명현적우세。
According to the problems such as redundancy information and dimension disaster caused by fault feature extraction of effective sampling point method, put forwards improved binary particle swarm method to extract fault feature. The differences and deficiencies between particle swarm optimization (PSO) algorithm and binary particle swarm optimization (BPSO) algorithm are analyzed. Change swarm extremum update mode to avoid research result to get into local best. Taking Sallen-Key filter as example, realize feature extraction of 9 simulation circuit fault mode. The result shows that the proposed method can effectively reduce the complexity of the fault diagnosis model. Compared with the binary particle swarm optimization algorithm, it has obvious advantages in the feature dimension and diagnostic accuracy.