The paper presents results of the development and experimental study of an eight-channel search sensor for railway rails defectoscopy. The experimental sample of the search sensor is implemented on the basis of Hall sensors and is designed to record the changes of the longitudinal component of the scattering magnetic field of the structural elements of the track and defects. The choice, for the registration of defectoscopic signals, changes in the longitudinal component of the scattering magnetic field is due to the fact that the same component of the field is registered by regular singlechannel induction search sensors of the carriages-defectoscopes. The investigated search sensor contained eight Hall sensors SS49E, which were placed at intervals of approximately 1 cm in a row on a special board, which was fixed to the search ski of the carriage-defectoscope. Thus, an eight-channel search sensor covered the entire surface of the rail head. Defectoscopic information from eight Hall sensors was synchronously recorded on a personal computer using a multi-channel data acquisition system with a sampling rate of 20 kHz and a bit resolution of 12-bit of analog-to-digital converter. During the experimental researches on the carriage-defectoscope, with departure to the track section of the Lviv railway, it was found that the eight-channel search sensor registers all structural elements of the railway track, including welded rail joints. The adequacy of the records was proved by numerically differentiating the signal from the bolt rail joint. As a result of such verification, a signal was obtained, the shape of which corresponds to the signal of the induction sensor from the bolt rail joint, which is common to carriage-defectoscope operators. The signals of the adjacent channels are practically coincided. The worst case correlation coefficients were not less than 0,95.
KEYWORDS: Signal detection, Continuous wavelet transforms, Stochastic processes, Wavelets, Signal processing, Signal to noise ratio, Digital signal processing, Artificial neural networks, Defect detection
The report examines the issue of increasing the efficiency of detecting complex impulse stochastic signals in the process of their generation against the background of quasi-periodic deterministic interference by using wavelet transformations and neural networks. An example the detection of a triple-wave stochastic signal is considered. One of the most characteristic signs of the shape of such signals is a sharply expressed asymmetry: the amplitude of the negative part of the signal is usually 3-4 times higher than the positive maximum amplitude. The second very important feature is the ratio of the positive parts amplitudes of the signal: the amplitude of the right-hand side is always greater, or in extreme cases, equal to the amplitude of the left-hand side. The proposed technique for processing such impulse signals against a background of quasi-periodic interference by using wavelet-neural technologies for analyzing digital signals. For this purpose, an artificial neural network was constructed, which made it possible to detect such signals at the beginning of their development, starting from a signal-to-noise ratio of 1.5 times, which is twice as good as the threshold for visual analysis. The proposed technique can be used in the analysis of pulsed signals in radar systems, mobile railroad rail diagnostic systems by the Magnitodynamic method, as well as in the experimental work of processing digital stochastic signals of various objects, when it is necessary to observe the dynamics of the signal change.