Automatic Detection of Real Damage in Operating Tie-Rods

Abstract

Many researchers have proposed vibration-based damage-detection approaches for continuous structural health monitoring. Translation to real applications is not always straightforward because the proposed methods have mostly been developed and validated in controlled environments, and they have not proven to be effective in detecting real damage when considering real scenarios in which environmental and operational variations are not controlled. This work was aimed to develop a fully-automated strategy to detect damage in operating tie-rods that only requires one sensor and that can be carried out without knowledge of physical variables, e.g., the axial load. This strategy was created by defining a damage feature based on tie-rod eigenfrequencies and developing a data-cleansing strategy that could significantly improve performance of outlier detection based on the Mahalanobis squared distance in real applications. Additionally, the majority of damage-detection algorithms presented in the literature related to structural health monitoring were validated in controlled environments considering simulated damage conditions. On the contrary, the approach proposed in this paper was shown to allow for the early detection of real damage associated with a corrosion attack under the effects of an intentionally uncontrolled environment.

A Damage Detection Approach for Axially Loaded Beam-like Structures Based on Gaussian Mixture Model

“…The data that will be discussed in the following come from an experimental set-up located in the Mechanical Engineering laboratory at Politecnico di Milano, in Italy. The set-up (see Figure 1 ) is composed by a series of nominally identical tie-rods, monitored for long time by an SHM system, for research purposes (e.g., [ 22 , 23 , 37 , 38 ]).…”

“…On some of the specimen, a chemical attack was carried out to intentionally introduce a corrosion process which progressively evolved through several months. A general corrosion caused a decrease of the nominal section of the tie-rod, due to a progressive electrochemical reaction between the metal and concentrated solutions of sodium hydroxide and hydrofluoric acids (more details on the corrosion process can be found in reference [ 23 ]). The data acquired during the deteriorative process will be used in Section 3 , to compare the damage detection indexes.…”

“…The data cleansing algorithm is presented and discussed in detail in Ref. [ 23 ]; however, since the data adopted in the following are those obtained after the removal of wrong identifications, some details are provided also here, for the sake of completeness.…”

“…To overcome the current limitations, in previous works [ 22 , 23 ], the authors proposed the adoption of a vibration-based data-driven approach that does not require knowledge of any physical variable of the problem, that does not require specific in situ tests and that can be adopted continuously, without human supervision. The statistical pattern recognition approach was adopted, framing the problem of tie-rod damage detection as a multivariate outlier detection one, by adopting damage feature vectors based on modal parameters.…”

“…Starting from these observations, in [ 23 ] the authors further developed the process, focusing on the automatisation of the algorithm. A multivariate damage feature based on a collection of eigenfrequencies associated with multiple vibration modes was adopted and an automatic data cleansing algorithm was proposed, to develop a robust algorithm that can be used in real applications.…”