Search

The object of this study is the dynamics of hazardous parameters of the gas environment during the ignition of materials. The problem that was solved is the early detection of fires in the premises. The research results indicate the nonlinear nature of the dynamics of hazardous parameters of the gas environment in the absence and presence of materials ignition. It was established that the bispectrum amplitude, in contrast to the amplitude spectrum of the hazardous parameters of the gas medium, contains information on the reliable detection of fires. As such information, the value of the positive dynamic amplitude range of bispectrum is used. It was established that during the ignition of alcohol, the positive dynamics of the amplitude bispectrum of all dangerous parameters of the gas medium change. Significant changes are characteristic of smoke density (from 1dB to 30dB) and temperature (from 1dB to 70dB). The dynamic range of amplitude bispectrum for CO concentration is increased from 30dB to 70dB. Paper ignition was found to reduce the dynamic range of the amplitude bispectrum for smoke density from 40dB to 20dB. At the same time, the dynamic range of amplitude bispectrum for carbon monoxide concentration and temperature increases to 60dB. The ignition of wood causes an increase in the dynamic range of the amplitude bispectrum relative to the concentration of carbon monoxide from 40dB to 60dB, and the temperature – from 30dB to 40dB. It was established that when textiles are ignited, the range of dynamics of the amplitude bispectrum for temperature increases from 10dB to 60dB. The results indicate that the dynamic characteristics of the amplitudes of the bispectrum of the gas medium can be used in practice for the early detection of fires in the premises

This paper has substantiated a modified method that, within the framework of the adaptive zero-order Brown’s model, provides for increased accuracy in predicting processes with unknown dynamics masked by the noise of various levels. The forecasting method modification essentially involves an adaptive technique for determining the weight of the correction of the previous forecast, taking into consideration the recurrent state of the predicted process in time. To investigate the accuracy of the forecasting method, a test model of the process dynamics was determined in the form of a rectangular pulse with unit amplitude. In addition, a model of additive masking noise was defined in the form of a discrete Gaussian process with a zero mean and a variable value of the mean square deviation. Based on determining the exponentially smoothed values of current absolute forecasting errors, the dynamics of forecast accuracy were examined for the modified and self-adjusting methods. It was found that for the mean quadratic deviation of the masking noise equal to 0.9, the smoothed absolute prediction error for the modified method does not exceed 23%; for the self-adjusting method – 42%. This means that the prediction accuracy for the modified method is about twice as high. In the case of an average square deviation of masking noise of 0.1, the smoothed absolute prediction error for the modified and self-adjusting methods is approximately the same and does not exceed 10%. That means that at a low level of masking noise, both prediction methods provide approximately the same accuracy. However, with an increase in the level of masking noise, the self-adjusting method significantly loses the accuracy of the forecast to the proposed modified method.

This paper reports a study into the errors of process forecasting under the conditions of uncertainty in the dynamics and observation noise using a self-adjusting Brown's zero-order model. The dynamics test models have been built for predicted processes and observation noises, which make it possible to investigate forecasting errors for the self-adjusting and adaptive models. The test process dynamics were determined in the form of a rectangular video pulse with a fixed unit amplitude, a radio pulse of the harmonic process with an amplitude attenuated exponentially, as well as a video pulse with amplitude increasing exponentially. As a model of observation noise, an additive discrete Gaussian process with zero mean and variable value of the mean square deviation was considered. It was established that for small values of the mean square deviation of observation noise, a self-adjusting model under the conditions of dynamics uncertainty produces a smaller error in the process forecast. For the test jump-like dynamics of the process, the variance of the forecast error was less than 1%. At the same time, for the adaptive model, with an adaptation parameter from the classical and beyond-the-limit sets, the variance of the error was about 20% and 5%, respectively. With significant observation noises, the variance of the error in the forecast of the test process dynamics for the self-adjusting and adaptive models with a parameter from the classical set was in the range from 1% to 20%. However, for the adaptive model, with a parameter from the beyond-the-limit set, the variance of the prediction error was close to 100% for all test models. It was established that with an increase in the mean square deviation of observation noise, there is greater masking of the predicted test process dynamics, leading to an increase in the variance of the forecast error when using a self-adjusting model. This is the price for predicting processes with uncertain dynamics and observation noises.

This paper reports the rationale for the modification of Brown’s zero-order model, which ensures increased accuracy of the short-term fire forecast based on the use of the current measure of recurrence in the increments of the state of the air environment in the premises. A special feature of the proposed model modification is that the a priori model of the dynamics of the level of the time series of the measure of the current recurrence of increments in the air environment states determined by the dangerous factors of the fire has been modified. In this case, it is proposed that the new a priori model should take into consideration additionally the value of the current increments of the level of the studied time series. That makes it possible to negligibly reduce errors of the short-term forecast of fire in the premises without significantly complicating Brown’s zero-order model while retaining all its implementing advantages. The provided accuracy of the forecast for one step in advance on the basis of a time series of measures of the current recurrence of increments of the state of the air environment, determined from the experimental data during the ignition of alcohol and timber in a laboratory chamber, has been investigated. The considered quantitative indicators of forecast accuracy are the absolute and average errors exponentially smoothed with a parameter of 0.4. It has been established that for the proposed modification the value of the average absolute error does not exceed 0.02 %. That means that an error of the short-term forecast of a fire in the premises based on the proposed modification is an order of magnitude less than that in the case of using known Brown’s model at the smoothing parameter from an unclustered set. The results from the ignition of alcohol and timber in the laboratory chamber, in general, indicate significant advantages of using the proposed modification of Brown’s zero-order model for a short-term forecast of a fire in the premises.