No abstract

The approaches developed and generalized scheme of identification of the technical state of the object based on the analysis of several parameters. Identification is based on the introduction of the median line of the system, forming a local zone states. The scheme forms a platform for the development of specific models and algorithms focused on the identification of technical objects based on data from sensors of physical quantities. To identify the possible states of a model prediction of a physical quantity changes in real time, based on the use of the linear adaptive filter. The filter is based on the model of autoregression. A scheme of performance prediction, which is to implement the procedure, consisting of three stages. Investigations using an adaptive time series to predict the rapidly changing physical quantity based on the sensor data. It is shown that for the identification of the state is necessary to develop models and algorithms that determine the proximity of the integrated current values of the parameters to their median values. It is necessary to take into account the different states of configuration and rank configuration of the integrated degree of intimacy.

The performance of the technical object is determined based on the evaluation of its parameters. Sensors of physical quantities are used to collect data on the values of the parameters of the controlled object. The performance evaluation of an object depends on the accuracy of the parameter measurement. The measurement accuracy is determined by the sensor conversion characteristic. If the sensor calibration tests are performed correctly, the conversion characteristic will accurately reflect the relationship between the measured parameter and the output electrical signal. A method for assessing the quality of the conversion characteristics of the microprocessor sensor, which is based on the use of Hurst index. The sensor of slowly changing physical quantities is considered. Based on the results of several test cycles obtained at a fixed ambient temperature, a series similar to the time series is formed. The initial series is subjected to additional processing before evaluation. The Hurst exponent is determined for the obtained series. The value of the Hurst index determines the quality of the test results. The possibility of using fractality index to assess the quality of tests is also considered.

No abstract

Аннотация-Погрешность измерений микропроцессорного датчика давления зависит от правильности построения его характеристики преобразования, которая формируется на основе результатов градуировочных испытаний. В процессе градуировочных испытаний возможны отклонения от установленной методики, которые могут привести к неточностям характеристики преобразования и, как следствие, к ухудшению метрологических характеристик датчика. Для оценки качества характеристики преобразования предложено использовать методы фрактального анализа. В процессе испытаний данные с датчика давления снимаются циклически при фиксированных значениях параметров среды. Результаты нескольких циклов испытаний при неизменных параметрах среды должны быть независимыми друг от друга и аналогичными по форме. Оценка степени повторяемости формы циклов испытаний и их взаимной независимости определяется с помощью показателя Херста. Ключевые слова-характеристика преобразования, показатель Херста, датчик, давление, испытание, временной ряд.