Higher-order transmissibility and its linear approximation for in-service crack identification in train wheelset axles

TL;DR

This paper introduces a new higher-order harmonic feature for crack detection in train wheelset axles, and proposes a linear approximation method that reduces computational effort while maintaining accuracy, enabling near real-time in-service damage identification.

Contribution

It develops a novel higher-order transmissibility feature for crack detection and introduces a linear surrogate model for efficient crack identification in train axles.

Findings

The HOTr feature effectively detects cracks.

The linear approximation reduces computational time significantly.

The method maintains high accuracy compared to nonlinear models.

Abstract

In-service structural health monitoring is a so far rarely exploited, yet potent option for early-stage crack detection and identification in train wheelset axles. This procedure is non-trivial to enforce on the basis of a purely data-driven approach and typically requires the adoption of numerical, e.g. finite element-based, simulation schemes of the dynamic behavior of these axles. Damage in this particular case can be formulated as a breathing crack problem, which further complicates simulation by introducing response-dependent nonlinearities into the picture. In this study, first, a new crack detection feature based on higher-order harmonics of the breathing crack is proposed, termed Higher-Order Transmissibility (HOTr), and, secondly, its sensitivity and efficacy are assessed within the context of crack identification. Next, the mentioned feature is approximated via use of linear system theory, delivering a surrogate model which facilitates the computation and speeds up the crack identification procedure. The accuracy of the proposed method in reproducing the delivered HOTr is compared against the nonlinear simulation model. The obtained results suggest that the approximation of the HOTr can significantly reduce the computational burden by eliminating the need for an iterative solution of the governing nonlinear equation of motion, while maintaining a high level of accuracy when compared against the reference model. This implies great potential for adoption in in-service damage identification for wheelset axles, feasibly within a near real-time context.