A New Approach for ARMA Pole Estimation Using Higher-Order Crossings

TL;DR

This paper introduces a novel ARMA pole estimation technique leveraging higher-order crossings, transforming crossing counts into pole estimates via autocorrelation, with minimal data storage requirements.

Contribution

It presents a new method for ARMA pole estimation using crossing counts, which simplifies data storage and enhances control system analysis.

Findings

The method accurately estimates ARMA poles from crossing counts.

It requires only crossing event features, reducing data storage needs.

The approach is applicable for control loop performance evaluation.

Abstract

The paper describes a new method for estimating the poles of an ARMA model using higher-order crossings. The method involves transforming counts of crossing events into estimates of ARMA poles via the autocorrelation domain. An important advantage of the method is that the crossing counts are the only features that need to be stored from the original data. The poles of an ARMA model of a control loop correspond to the roots of the characteristic equation and are thus useful for evaluating control performance.