话筒
振动
加速度计
小波
故障检测与隔离
噪音(视频)
信号(编程语言)
计算机科学
状态监测
人工神经网络
断层(地质)
工程类
传感器融合
电子工程
人工智能
声学
信号处理
数字信号处理
地质学
物理
电气工程
图像(数学)
地震学
执行机构
操作系统
程序设计语言
电信
声压
作者
M. Saimurugan,Rampi Ramprasad
标识
DOI:10.1177/1077546316689644
摘要
The growing industrial sector utilizes machinery that needs to be monitored continuously by proficient experts and robust software to ensure a proper maintenance strategy. Condition monitoring using vibration signal analysis is one of the chief methods used in predictive maintenance strategy for rotating machinery. Generally, sound signal analysis is considered as secondary as it involves noise. In this paper, the signals for various rotating machinery faults are studied by simulating them in a machine fault simulator at various speeds and the best features are fused to obtain more efficiency in the fault diagnosis of rotating machinery. The vibration signal data obtained from accelerometers and sound signal data from a microphone is extracted for features using wavelet transform. The best features from vibration and sound signals are selected using the decision tree algorithm and are fused. Further, the features are classified using an artificial neural network and the corresponding efficiency at various motor speeds is found. The results of this paper imply that the signal fusion of vibration and sound by the decision tree algorithm is effective in machine fault diagnosis methodologies.
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