摘要
为实现柴油机异响故障的不解体诊断,采用双麦克风采集发动机噪声信号,利用小波分解技术对采集的声压信号进行分解,通过比较不同的小波分解方法,确定用于故障诊断的声音特征参数,最后构建BP神经网络进行模式识别。试验结果表明,采用双麦克风对发动机进行故障诊断的方法准确性高,可以应用于发动机异响故障诊断。
In order to diagnose the abnormal sound fault of the diesel without disassemble,dual microphones are used to collect noise signal of the engine in this paper,and the sound pressure signal is decomposed with wavelet decomposition technique.By comparing different wavelet decomposition techniques,it determines the sound feature parameter for fault diagnosis,and establishes a BP neural network for pattern recognition.The test result shows that diagnosing engine fault with dual microphones is accuracy,and it can be applied to diagnose the abnormal sound fault of engine.
作者
曾荣
曾锐利
梅检民
张帅
丁雷
ZENG Rong;ZENG Ruili;MEI Jianmin;ZHANG Shuai;DING Lei(Postgraduate Training Brigade, Army Military Transportation University, Tianjin 300161, China;Military Vehicle Department, Army Military Transportation University, Tianjin 300161, China)
出处
《军事交通学院学报》
2017年第12期23-28,共6页
Journal of Military Transportation University
基金
天津市自然科学基金项目(15JCTPJC64200)
关键词
柴油机故障诊断
双麦克风
多分辨分析
声信号
BP神经网络
diesel engine fault diagnosis
dual microphones
multi resolution analysis
sound signal
BP neural network
