Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Statistical guided-waves-based SHM via stochastic non-parametric time
  series models   Damage detection in active-sensing, guided-waves-based Structural Health
Monitoring (SHM) has evolved through multiple eras of development during the
past decades. Nevertheless, there still exists a number of challenges facing
the current state-of-the-art approaches, both in the industry as well as in
research and development, including low damage sensitivity, lack of robustness
to uncertainties, need for user-defined thresholds, and non-uniform response
across a sensor network. In this work, a novel statistical framework is
proposed for active-sensing SHM based on the use of ultrasonic guided waves.
This framework is based on stochastic non-parametric time series models and
their corresponding statistical properties in order to readily provide healthy
confidence bounds and enable accurate and robust damage detection via the use
of appropriate statistical decision making tests. Three such methods and
corresponding statistical quantities (test statistics) along with decision
making schemes are formulated and experimentally assessed via the use of three
coupons with different levels of complexity: an Al plate with a growing notch,
a Carbon fiber-reinforced plastic (CFRP) plate with added weights to simulate
local damages, and the CFRP panel used in the Open Guided Waves project [1],
all fitted with piezoelectric transducers and a pitch-catch configuration. The
performance of the proposed methods is compared to that of state-of-the-art
time-domain damage indices (DIs). The results demonstrate the increased
sensitivity and robustness of the proposed methods, with better tracking
capability of damage evolution compared to conventional approaches, even for
damage-non-intersecting actuator-sensor paths. Overall, the proposed
statistical methods exhibit greater damage sensitivity across different
components, with enhanced robustness to uncertainty, as well as user-friendly
application.

26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
1
The categories of this paper are: cs
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn