The peak measured gain and directivity are realized as 6.0 dB and 6.739 dBi respectively at 11.1 GHz. The FR4 substrate material found to optimum for proposed UWB antenna that has an overall measurement of 30 mm × 35 mm × 1.2 mm, possesses a large impedance bandwidth of 119.3%, and exhibits high gain and directivity. FR4, Jeans, Rubber, Fleece, Polyester and Rogers RT 5880 on the designed antenna performance for has been performed. The comparative investigation of the effect of different substrates materials i.e. The major focus of the presented work is to perform comparative analysis of different substrate materials on performance of a novel, small sized, high gain UWB antenna design suitable for on body WBAN applications. In the present scenario, on-body wearable antennas wave propagation and radiation pattern characteristics have been rigorously explored for study along with the growth of wireless body area networks. The design and development of a small-sized, high gain ultra wide band (UWB) antenna based on different substrate materials are presented for on body wireless body area network (WBAN) applications. This method significantly improves the accuracy of vibration signal collection of mechanical equipment. The greater the amplitude of vibration, the smaller the error. The error of the proposed method is 0.5% when the frequency is 80 Hz, relative to the displacement measurement method of the three-axis acceleration sensor at 8.3%, and the error of data calculation results is greatly reduced.
The simulation results of this article show the vibration data collected by the main controller, after Kalman filtering and piecewise trapezoidal integration method optimization.
In the integration process after filtering, using a piecewise integration method between acceleration peaks, the integration calculation is optimized to obtain the vibration displacement. Then, it uses the Kalman filter algorithm for data filtering, according to the mathematical model established by the system, thus choosing a suitable noise covariance calculation method. This article addresses the challenge of large error rate and low accuracy of the vibration signal collection of mechanical equipment failure, and proposes a mechanical equipment failure vibration signal collection and analysis based on computer simulation detection.
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