摘要
提出了自适应增强支持向量机集成算法,并结合风机噪声信号的人耳听觉谱特征,对风机故障进行分类识别。现场实测数据的识别实验证明,该算法可正确识别99%的正常机器,并且对故障类型诊断的正确识别率比单个支持向量机分类器高1.88%~2.50%。
A self-adaptive enhanced support vector machine ensemble method is proposed.The proposed novel method combined with auditory spectrum feature is applied to identify of fan fault.The identification experiments of field measurement data proved that the proposed method is effective for fan fault diagnosis.The classification accuracy of normal fans is about 99%,and the classification accuracy of the type of faulty fans of proposed SVM ensemble method is 1.88%~2.50% higher than single SVM classifier.
出处
《测控技术》
CSCD
北大核心
2010年第7期72-74,共3页
Measurement & Control Technology
基金
西北工业大学教育教学改革研究基金项目("声环境监测"课程建设的创新研究与实践)
关键词
自适应增强支持向量机集成
人耳听觉谱特征
风机故障诊断
self-adaptive support vector machine ensemble
auditory spectrum feature
fault diagnosis of fan
