Envelope analysis is an effective method for characterizing impulsive vibrations in wired condition monitoring(CM)systems. This paper depicts the implementation of envelope analysis on a wireless sensor node for obtai...Envelope analysis is an effective method for characterizing impulsive vibrations in wired condition monitoring(CM)systems. This paper depicts the implementation of envelope analysis on a wireless sensor node for obtaining a more convenient and reliable CM system. To maintain CM performances under the constraints of resources available in the cost effective Zigbee based wireless sensor network(WSN), a low cost cortex-M4 F microcontroller is employed as the core processor to implement the envelope analysis algorithm on the sensor node. The on-chip 12 bit analog-to-digital converter(ADC) working at 10 k Hz sampling rate is adopted to acquire vibration signals measured by a wide frequency band piezoelectric accelerometer. The data processing flow inside the processor is optimized to satisfy the large memory usage in implementing fast Fourier transform(FFT) and Hilbert transform(HT). Thus, the envelope spectrum can be computed from a data frame of 2048 points to achieve a frequency resolution acceptable for identifying the characteristic frequencies of different bearing faults. Experimental evaluation results show that the embedded envelope analysis algorithm can successfully diagnose the simulated bearing faults and the data transmission throughput can be reduced by at least 95% per frame compared with that of the raw data, allowing a large number of sensor nodes to be deployed in the network for real time monitoring.展开更多
利用希尔伯特(Hilbert)变换能够得到电力系统振荡中电流信号包络线的特点,根据系统正常运行、纯振荡以及振荡中再故障情况下包络线的不同变化趋势,采用两相电流包络线之差的突变量作为判别量,提出了一种新型的振荡中再故障判别元件,该...利用希尔伯特(Hilbert)变换能够得到电力系统振荡中电流信号包络线的特点,根据系统正常运行、纯振荡以及振荡中再故障情况下包络线的不同变化趋势,采用两相电流包络线之差的突变量作为判别量,提出了一种新型的振荡中再故障判别元件,该判别元件的计算精度不受电力系统振荡时频率变化的影响。大量 ATP 仿真证明了该元件在纯振荡过程中能够可靠不误动,并且可以快速有效地识别系统振荡中发生的各种故障。而且,文中采用的算法实现方便,计算量小,具有在工程中良好的实际应用价值。展开更多
This paper focuses on improving the detection performance of spectrum sensing in cognitive radio(CR) networks under complicated electromagnetic environment. Some existing fast spectrum sensing algorithms cannot get sp...This paper focuses on improving the detection performance of spectrum sensing in cognitive radio(CR) networks under complicated electromagnetic environment. Some existing fast spectrum sensing algorithms cannot get specific features of the licensed users'(LUs') signal, thus they cannot be applied in this situation without knowing the power of noise. On the other hand some algorithms that yield specific features are too complicated. In this paper, an algorithm based on the cyclostationary feature detection and theory of Hilbert transformation is proposed. Comparing with the conventional cyclostationary feature detection algorithm, this approach is more flexible i.e. it can flexibly change the computational complexity according to current electromagnetic environment by changing its sampling times and the step size of cyclic frequency. Results of simulation indicate that this approach can flexibly detect the feature of received signal and provide satisfactory detection performance compared to existing approaches in low Signal-to-noise Ratio(SNR) situations.展开更多
文摘Envelope analysis is an effective method for characterizing impulsive vibrations in wired condition monitoring(CM)systems. This paper depicts the implementation of envelope analysis on a wireless sensor node for obtaining a more convenient and reliable CM system. To maintain CM performances under the constraints of resources available in the cost effective Zigbee based wireless sensor network(WSN), a low cost cortex-M4 F microcontroller is employed as the core processor to implement the envelope analysis algorithm on the sensor node. The on-chip 12 bit analog-to-digital converter(ADC) working at 10 k Hz sampling rate is adopted to acquire vibration signals measured by a wide frequency band piezoelectric accelerometer. The data processing flow inside the processor is optimized to satisfy the large memory usage in implementing fast Fourier transform(FFT) and Hilbert transform(HT). Thus, the envelope spectrum can be computed from a data frame of 2048 points to achieve a frequency resolution acceptable for identifying the characteristic frequencies of different bearing faults. Experimental evaluation results show that the embedded envelope analysis algorithm can successfully diagnose the simulated bearing faults and the data transmission throughput can be reduced by at least 95% per frame compared with that of the raw data, allowing a large number of sensor nodes to be deployed in the network for real time monitoring.
文摘利用希尔伯特(Hilbert)变换能够得到电力系统振荡中电流信号包络线的特点,根据系统正常运行、纯振荡以及振荡中再故障情况下包络线的不同变化趋势,采用两相电流包络线之差的突变量作为判别量,提出了一种新型的振荡中再故障判别元件,该判别元件的计算精度不受电力系统振荡时频率变化的影响。大量 ATP 仿真证明了该元件在纯振荡过程中能够可靠不误动,并且可以快速有效地识别系统振荡中发生的各种故障。而且,文中采用的算法实现方便,计算量小,具有在工程中良好的实际应用价值。
基金sponsored by National Basic Research Program of China (973 Program, No. 2013CB329003)National Natural Science Foundation of China (No. 91438205)+1 种基金China Postdoctoral Science Foundation (No. 2011M500664)Open Research fund Program of Key Lab. for Spacecraft TT&C and Communication, Ministry of Education, China (No.CTTC-FX201305)
文摘This paper focuses on improving the detection performance of spectrum sensing in cognitive radio(CR) networks under complicated electromagnetic environment. Some existing fast spectrum sensing algorithms cannot get specific features of the licensed users'(LUs') signal, thus they cannot be applied in this situation without knowing the power of noise. On the other hand some algorithms that yield specific features are too complicated. In this paper, an algorithm based on the cyclostationary feature detection and theory of Hilbert transformation is proposed. Comparing with the conventional cyclostationary feature detection algorithm, this approach is more flexible i.e. it can flexibly change the computational complexity according to current electromagnetic environment by changing its sampling times and the step size of cyclic frequency. Results of simulation indicate that this approach can flexibly detect the feature of received signal and provide satisfactory detection performance compared to existing approaches in low Signal-to-noise Ratio(SNR) situations.
