With the rapid development of mechanical equipment, the mechanical health monitoring field has entered the era of big data. However, the method of manual feature extraction has the disadvantages of low efficiency and ...With the rapid development of mechanical equipment, the mechanical health monitoring field has entered the era of big data. However, the method of manual feature extraction has the disadvantages of low efficiency and poor accuracy, when handling big data. In this study, the research object was the asynchronous motor in the drivetrain diagnostics simulator system. The vibration signals of different fault motors were collected. The raw signal was pretreated using short time Fourier transform （STFT） to obtain the corresponding time-frequency map. Then, the feature of the time-frequency map was adap- tively extracted by using a convolutional neural network （CNN）. The effects of the pretreatment method, and the hyper parameters of network diagnostic accuracy, were investigated experimentally. The experimental results showed that the influence of the preprocessing method is small, and that the batch-size is the main factor affecting accuracy and training efficiency. By investigating feature visualization, it was shown that, in the case of big data, the extracted CNN features can represent complex mapping relationships between signal and health status, and can also overcome the prior knowledge and engineering experience requirement for feature extraction, which is used by tra- ditional diagnosis methods. This paper proposes a new method, based on STFT and CNN, which can complete motor fault diagnosis tasks more intelligently and accurately.展开更多
为了解决全球定位系统(Global position system,GPS)微弱信号的快速捕获问题,在基于快速傅里叶变换(Fourier transform,FFT)捕获方法的基础上,改进过去的相干积分或非相干积分,提出了一种新的改进微弱信号捕获算法,采用批处理方式提高...为了解决全球定位系统(Global position system,GPS)微弱信号的快速捕获问题,在基于快速傅里叶变换(Fourier transform,FFT)捕获方法的基础上,改进过去的相干积分或非相干积分,提出了一种新的改进微弱信号捕获算法,采用批处理方式提高捕获增益,并运用多普勒补偿,提高信号累加时间容限,进一步提高信号捕获灵敏度。仿真测试表明,该方法较传统的FFT算法,提高了捕获概率,最后在FPGA上具体实现了该方案。展开更多
基金Supported by National Natural Science Foundation of China(Grant No.51405241,51505234,51575283)
文摘With the rapid development of mechanical equipment, the mechanical health monitoring field has entered the era of big data. However, the method of manual feature extraction has the disadvantages of low efficiency and poor accuracy, when handling big data. In this study, the research object was the asynchronous motor in the drivetrain diagnostics simulator system. The vibration signals of different fault motors were collected. The raw signal was pretreated using short time Fourier transform （STFT） to obtain the corresponding time-frequency map. Then, the feature of the time-frequency map was adap- tively extracted by using a convolutional neural network （CNN）. The effects of the pretreatment method, and the hyper parameters of network diagnostic accuracy, were investigated experimentally. The experimental results showed that the influence of the preprocessing method is small, and that the batch-size is the main factor affecting accuracy and training efficiency. By investigating feature visualization, it was shown that, in the case of big data, the extracted CNN features can represent complex mapping relationships between signal and health status, and can also overcome the prior knowledge and engineering experience requirement for feature extraction, which is used by tra- ditional diagnosis methods. This paper proposes a new method, based on STFT and CNN, which can complete motor fault diagnosis tasks more intelligently and accurately.
文摘为了解决全球定位系统(Global position system,GPS)微弱信号的快速捕获问题,在基于快速傅里叶变换(Fourier transform,FFT)捕获方法的基础上,改进过去的相干积分或非相干积分,提出了一种新的改进微弱信号捕获算法,采用批处理方式提高捕获增益,并运用多普勒补偿,提高信号累加时间容限,进一步提高信号捕获灵敏度。仿真测试表明,该方法较传统的FFT算法,提高了捕获概率,最后在FPGA上具体实现了该方案。
