摘要
针对齿轮振动信号特征难以提取的现状,依据免疫系统的自适应,自学习特性,提出了自适应免疫选择模型,该模型中引入了自适应变异和3种抑制方式初始化抗体抑制、克隆抑制、检测器优化抑制,并利用该模型生成成熟故障检测器。将生成的检测器应用于齿轮故障诊断试验中,试验结果表明该模型能有效地对3类典型齿轮故障进行分类与辨识。
It is difficult to extract the features from gear vibration signal, based on immune system adaptive, self-learning characteristics, the adaptive immune selection algorithm was proposed. The adaptive mutation and three inhibition of initializing antibody inhibition, clone inhibition, inhibition of formation of mature detector optimization were introduced in the model. The excellence detector to diagnose fault was produced. The model was tested in the gear fault diagnosis and the trial results indicate that the detector can effectively classify and identify three typical kinds of gear faults.
出处
《煤矿机械》
北大核心
2014年第2期232-234,共3页
Coal Mine Machinery
关键词
齿轮故障诊断
免疫选择
自适应变异
免疫疫苗
Key words： gear fault diagnosis
immune selection
adaptive variation
immune vaccine
