摘要
提出一种基于声音增强技术的高空作业车电气故障监测方法。通过小波包分解降噪和梅尔频率倒谱系数(Mel Frequency Cepstrum Coefficient,MFCC)特征提取,结合支持矢量机(Support Vector Machine,SVM)分类器,实现了对高空作业车电气系统故障的高效识别。实验结果表明,该方法在复杂噪声环境下仍能保持较高的故障识别准确率和实时性,为高空作业车的安全运行提供了可靠保障。
An electrical fault monitoring method of aerial work vehicle based on sound enhancement technology is proposed.Through wavelet packet decomposition and Mel Frequency Cepstrum Coefficient(MFCC) feature extraction,combined with Support Vector Machine(SVM) classifier,the high-efficiency fault identification of aerial work vehicle electrical system is realized.The experimental results show that this method can still maintain high fault identification accuracy and real-time performance in complex noise environment,which provides a reliable guarantee for the safe operation of aerial work vehicles.
作者
王壘
WANG Lei(Jiangsu Liugong Machinery Co.,Ltd.,Zhenjiang 212000,China)
出处
《电声技术》
2024年第12期76-78,共3页
Audio Engineering
关键词
声音增强技术
高空作业车
小波包分解
sound enhancement technology
aerial work vehicles
wavelet packet decomposition
