Particle Filters and Auto-Encoders Combination for Damage Diagnosis on Hysteretic Non-Linear Structures Subject to Changing Environmental Conditions

Damages may naturally arise in structures within their life span due to the insurgence of phenomena related to normal operation. Their occurrence might also be favored by external boundary conditions the systems experience during their lifetime, such as time-varying environmental and operating conditions. Standard maintenance activities, such as scheduled non-destructive testing (NDT) and corrective maintenance, are typically carried out to improve the health and longevity of such systems, typically entailing long downtimes with significant economic impacts. In recent decades, condition-based maintenance strategies (CBM) or even predictive ones (PM) have increasingly gained popularity since, in principle, they allow to optimally intervene on the structure only when really required by its current conditions. These maintenance schemes require that a deep knowledge of the system current state of health and, possibly, of the main degradation mechanisms be available, which may rely on advanced structural health monitoring (SHM) systems being installed on the structures for performing real-time diagnosis and prognosis. Many approaches to SHM have been formulated, with several applications to mechanical, aeronautical, space, and civil structures. Particle Filters (PFs) have been proposed as a model-based, time-domain tool for estimating hidden, not observable system states, including those normally affected by damage, in particular, when the structure behavior is non-linear and affected by non-Gaussian disturbances and noises. Yet, in case of varying operating and environmental conditions, the SHM task often still turns out to be quite challenging, since the diagnostic features associated with damage can be significantly distorted. To overcome this issue, auto-encoders have successfully been employed to extract damage-related features in presence of such varying external conditions. Thus, this work aims at combining these two methods for developing an original approach to damage detection and localization in structures, robust with respect to changing environmental and operating conditions, capable of leveraging the specific benefits provided by the two aforementioned methodologies. The proposed algorithm is demonstrated with reference to the problem of damage diagnosis on a vibrating n-degrees of freedom system, featuring a non-linear stiffness component characterized by a Bouc-Wen hysteretic behavior and subject to varying temperature conditions.