Comparative Evaluation of a New Sensor for Superparamagnetic Iron Oxide Nanoparticles in a Molecular Communication Setting

Testbeds are required to assess concepts and devices in the context of molecular communication. These allow the observation of real-life phenomena in a controlled environment and therefore present the basis of future work. A testbed using superparamagnetic iron oxide nanoparticles (SPIONs) as information carriers was constructed with regard to this context and requires a sensitive receiver for the detection of SPIONs.<br>This paper focusses on the comparison between a newly presented device (inductance sensor), a previously constructed SPION sensor (resonance bridge), and a commercial susceptometer as reference. The new inductance sensor is intended to improve on a low sensitivity achieved with the previous device and restrictions with respect to sample rate and measurement aperture encountered with the susceptometer. The signal-to-noise ratio (SNR) for each device is assessed at a variety of SPION concentrations. Furthermore, the sensors bit error rates (BER) for a random bit sequence are determined.<br>The results show the device based on an inductance sensor to be the most promising for further investigation as values both for BER and SNR exceed those of the resonance bridge while providing a su?ciently high sample rate. On average the SNR of the new device is 13 dB higher while the BER for the worst transmission scenario is 9% lower. The commercial susceptometer, although returning the highest SNR, lacks adaptability for the given use case.

Comparative Evaluation of a New Sensor for Superparamagnetic Iron Oxide Nanoparticles in a Molecular Communication Setting

Testbeds are required to assess concepts and devices in the context of molecular communication. These allow the observation of real-life phenomena in a controlled environment and therefore present the basis of future work. A testbed using superparamagnetic iron oxide nanoparticles (SPIONs) as information carriers was constructed with regard to this context and requires a sensitive receiver for the detection of SPIONs.<br>This paper focusses on the comparison between a newly presented device (inductance sensor), a previously constructed SPION sensor (resonance bridge), and a commercial susceptometer as reference. The new inductance sensor is intended to improve on a low sensitivity achieved with the previous device and restrictions with respect to sample rate and measurement aperture encountered with the susceptometer. The signal-to-noise ratio (SNR) for each device is assessed at a variety of SPION concentrations. Furthermore, the sensors bit error rates (BER) for a random bit sequence are determined.<br>The results show the device based on an inductance sensor to be the most promising for further investigation as values both for BER and SNR exceed those of the resonance bridge while providing a su?ciently high sample rate. On average the SNR of the new device is 13 dB higher while the BER for the worst transmission scenario is 9% lower. The commercial susceptometer, although returning the highest SNR, lacks adaptability for the given use case.

Gas Turbine Bearing Wear Monitoring Method Based on Magnetic Plug Inductance Sensor

In this paper, a gas turbine bearing wear monitoring method based on the magnetic plug inductance sensor is presented. Using the induced magnetic field of the magnetic pulse generated by the inductance coil, this method is applied to measure ferromagnetic wear debris in the oil, and the condition of bearing wear can be monitored and predicted on line. To estimate the mass of different particle size of ferromagnetic debris, the sample databases of debris mass were built, the oil capturing experiment was conducted, and the mapping model for the voltage signal of the sensor and the captured accumulation of ferromagnetic wear debris based on the BP (Error Back Propagation) neural network was established. Moreover, the kernel method was used to calculate the voltage distribution of the debris sensor in the step time of wear prediction, and the mean value and confidence boundary value of the signals in the expected step time were obtained. Moreover, the prediction model of the bearing wear was established by a linear regression method to predict the mass of ferromagnetic wear debris generated by bearing wear. Finally, a lubricating oil debris detection system was designed, and the bearing wear test was conducted on the bearing wear testing rig. The results showed that the monitoring method can continuously monitor and dynamically predict the condition of bearing wear online with the advantages of stability and automatic purifying lubricant oil.