PROCEDURE FOR ESTIMATING THE BOUNDARY DISTANCE OF SHORE RADIO STATIONS IN VHF RANGE FOR THE SHORE-TO-SHIP DIRECTION

Сформулирована и детализирована методика оценивания граничной дальности береговых ОВЧ радиостанций в направлении Берег-Судно, которая основана на зависимостях напряженности поля, полученных эмпирическим путем. Определены численные значения граничной дальности ОВЧ радиосвязи применительно к типовому судовому радиооборудованию и шумовой обстановке вблизи судовой антенны. Проведена оценка максимальных допусков определения граничной дальности ОВЧ радиосвязи. The procedure for estimating the boundary distance of the shore VHF radio stations in the shore-to-ship direction, which is based on the dependences of the electromagnetic field strength obtained empirically, has been formulated and detailed. Numerical values of the boundary distance of VHF radio communication in relation to typical ship radio equipment and the noise environment near the ship's antenna are determined. The estimation of the maximum tolerances for determining the boundary distance of VHF radio communication is carried out.

A Novel Anti-Noise Fault Diagnosis Approach for Rolling Bearings Based on Convolutional Neural Network Fusing Frequency Domain Feature Matching Algorithm

The development of deep learning provides a new research method for fault diagnosis. However, in the industrial field, the labeled samples are insufficient and the noise interference is strong so that raw data obtained by the sensor are occupied with noise signal. It is difficult to recognize time-domain fault signals under the severe noise environment. In order to solve these problems, the convolutional neural network (CNN) fusing frequency domain feature matching algorithm (FDFM), called CNN-FDFM, is proposed in this paper. FDFM extracts key frequency features from signals in the frequency domain, which can maintain high accuracy in the case of strong noise and limited samples. CNN automatically extracts features from time-domain signals, and by using dropout to simulate noise input and increasing the size of the first-layer convolutional kernel, the anti-noise ability of the network is improved. Softmax with temperature parameter T and D-S evidence theory are used to fuse the two models. As FDFM and CNN can provide different diagnostic information in frequency domain, and time domain, respectively, the fused model CNN-FDFM achieves higher accuracy under severe noise environment. In the experiment, when a signal-to-noise ratio (SNR) drops to -10 dB, the diagnosis accuracy of CNN-FDFM still reaches 93.33%, higher than CNN’s accuracy of 45.43%. Besides, when SNR is greater than -6 dB, the accuracy of CNN-FDFM is higher than 99%.