Kalman filter adaptation for ability to control of complex systems
Аuthors
1*, 1**, 2***1. Peter the Great St. Petersburg Polytechnic University, 29, Polytechnicheskaya str., St. Petersburg, 195251, Russia
2. Bauman Moscow State Technical University, Moscow, Russian Federation
*e-mail: falcon.sergey@yandex.ru
**e-mail: garanin@spbstu.ru
***e-mail: erofey15042006@gmail.com
Abstract
In modern conditions, more and more attention is being paid to algorithms and procedures that have predictive functions, since statistical forecasting either does not work or is limited for various reasons [1]. At the same time, effective condition monitoring can improve system reliability indicators such as Mean Time between Failures (MTBF) and Reliability. As a concept, this article considers the issue of expanding the capabilities of the mathematical apparatus of reliability theory, from the perspective of the interaction of methods for monitoring system states with methods for determining reliability indicators. This article discusses an algorithm for applying the Kalman filter, a combination of recursive Kalman algorithms, to assess the state of a dynamic system in the presence of measurement noise, with a method for studying the properties of the Weibul distribution when analyzing information states of technical systems with critically high requirements.
Keywords:
Kalman filter, recursive algorithm (method) for assessing the state of a dynamic system, Weibul distribution, time to failure, reliability theoryDownload