The sources of noise and artifacts arising during thermal imaging and the methods for thermal images filtering, including methods specific for processing of images generated by infrared sensors, are considered. In particular, distortions caused by the process of microbolometrer matrices self-heating due to internal and external heating sources and the methods for compensating such distortions are studied. The purpose of the study is to create a mathematical model of a bolometric matrix self-heating based on heat transfer equations and to develop an algorithm for suppressing of distortions introduced into thermal images by self-heating. The exponential models describing the propagation of heat in the microbolometer matrix are proposed and it is shown that the coefficients of the models after logarithming can be determined by the least squares method. For real thermal images, the coefficients of the model are determined, and situations are considered when the base temperature of the object is known and when it is necessary to restore it, and modifications of the exponential model in the form of an exponent from a complete and incomplete square are proposed. Computer simulation of the proposed distortion compensation algorithm has been carried out, a set of thermal images before and after processing has been presented, and a quantitative estimation of the degree of noise suppression caused by heating of bolometric arrays has been obtained. Based on the results of the work, it was determined that the exponential model provides a sufficient degree of closeness of the experimental and theoretically predicted temperature data, and the degree of difference between the data and the model was estimated. Recommendations are developed for the application of the proposed methods at known and unknown base temperature of the matrix. Proposals have been developed for further improving the mathematical model, including the situation of temperature changes over time, and for improving the efficiency of self-heating noise suppression algorithms.
Methods for diagnosing mechanisms and machines based on the analysis of vibration signals are considered. In particular, the comparison of various algorithms for analyzing vibration signals in the time and frequency domains was made, methods for selecting diagnostic features and methods for secondary processing were analyzed. The purpose of the study is to develop algorithms for selecting the vibration signal envelope based on empirical mode decomposition and decomposition of the signal into intrinsic mode functions, algorithms for the spectral estimation of envelopes and to choose a criterion for making a decision on object classification. It is proposed to choose the non-parametric Wilcoxon signed-rank test to determine the statistical significance of the difference between the parameters of normal and faulty objects. The multichannel microcontroller system for collecting data from an accelerometer and transmitting it to a computer via a local Wi-Fi network, including a number of independent data gathering nodes connected to a common distributed computing system, has been developed and experimentally studied. The computer processing of the recorded vibration signals for serviceable and faulty mechanisms was performed, including data decoding, Hilbert-Huang transform, spectral analysis using the Welch and Yule-Walker methods, and the choice of a diagnostic feature that provides maximum reliability of recognition. Based on the results of the work, it was determined that the empirical mode decomposition makes it possible to obtain vibration signal envelopes suitable for further diagnostics. Recommendations are developed for choosing the intrinsic mode function and the spectral analysis algorithm, it is determined that the first intrinsic mode function is the most informative for the mechanism under study. In accordance with the Wilcoxon criterion, the degree of diagnostic reliability was numerically determined in the analysis of the spectral power density of the vibration signal and the amplitude of peaks, and the comparison of probabilities of error-free recognition for various modifications of the algorithm was made.
The subject of research in the article are algorithms for fast calculation of autoregression coefficients in digital spectral analysis and estimation of the number of arithmetic operations required for their implementation. The aim of the article – comparative analysis of the speed of different algorithms for calculating the coefficients of autoregression as part of the algorithms of spectral analysis, including analysis of the complexity of their microcontroller implementation. Tasks to be solved: selection of spectral analysis methods suitable for diagnostics of technological equipment, analysis of methods for calculating autoregression coefficients and derivation of relations for estimating software complexity of algorithms and calculation of numerical estimates of addition and multiplication for some algorithms, adaptation of developed methods and estimates to microcontrollers. spectrum Applied methods: algorithm theory, Fourier transform, natural series, microcontroller programming. The results obtained: it is shown that spectral estimation methods based on Yul-Walker equations, which require the calculation of autoaggression coefficients, combine sufficient resolution and resistance to interference with acceptable implementation complexity. Estimates of the number of additions and multiplications for the Levinson, Durbin, and Trench algorithms are obtained, and their comparative analysis is performed. The calculation times for microcontroller arithmetic with fixed and floating points were count upon. Conclusions: When constructing spectrum analyzers for the diagnosis of technological equipment, it is advisable to use the Yul-Walker method. A comparison of Levinson, Durbin, and Trench algorithms for calculating autoregression coefficients showed that the Trench method requires a minimum number of additions, and the Durbin method requires a minimum number of multiplications. At microcontroller realization of spectrum analyzers, it is necessary to consider features of the arithmetic used by the controller. The Trench method is the fastest in the case of floating-point arithmetic and small-scale modeling. In other cases, Durbin's method is more effective.
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