DESIGN OF ROBUST RECURSIVE IDENTIFICATION ALGORITHMS FOR LARGE-SCALE STOCHASTIC SYSTEMS

The robust recursive algorithms, for identification of decentralized stochastic systems, are developed. It is supposed that stochastic disturbance belongs to a specified class of distributions which include the gross error model suitable for the description of outliers presence. Such an assumption introduces into the recursive algorithms a nonlinear transformation of prediction error. The given algorithms are robust with respect to uncertainty in the disturbance distribution. The individual subsystems are described with SISO (single-input single output) ARMAX model. Two algorithms are considered: the stochastic approximation and the least squares. Their comparison is based on simulations

Recursive joint Cramér‐Rao lower bound for parametric systems with two‐adjacent‐states dependent measurements

Joint Cramér-Rao lower bound (JCRLB) is very useful for the performance evaluation of joint state and parameter estimation (JSPE) of non-linear systems, in which the current measurement only depends on the current state. However, in reality, the non-linear systems with two-adjacent-states dependent (TASD) measurements, that is, the current measurement is dependent on the current state as well as the most recent previous state, are also common. First, the recursive JCRLB for the general form of such non-linear systems with unknown deterministic parameters is developed. Its relationships with the posterior CRLB for systems with TASD measurements and the hybrid CRLB for regular parametric systems are also provided. Then, the recursive JCRLBs for two special forms of parametric systems with TASD measurements, in which the measurement noises are autocorrelated or cross-correlated with the process noises at one time step apart, are presented, respectively. Illustrative examples in radar target tracking show the effectiveness of the JCRLB for the performance evaluation of parametric TASD systems

Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis