  Unsupervised health monitoring has gained much attention in the last decade
as the most practical real-time structural health monitoring (SHM) approach.
Among the proposed unsupervised techniques in the literature, there are still
obstacles to robust and real-time health monitoring. These barriers include
loss of information from dimensionality reduction in feature extraction steps,
case-dependency of those steps, lack of a dynamic clustering, and detection
results' sensitivity to user-defined parameters. This study introduces an
unsupervised real-time SHM method with a mixture of low- and high-dimensional
features without a case-dependent extraction scheme. Both features are used to
train multi-ensembles of Generative Adversarial Networks (GAN) and one-class
joint Gaussian distribution models (1-CG). A novelty detection system of
limit-state functions based on GAN and 1-CG models' detection scores is
constructed. The Resistance of those limit-state functions (detection
thresholds) is tuned to user-defined parameters with the GAN-generated data
objects by employing the Monte Carlo histogram sampling through a
reliability-based analysis. The tuning makes the method robust to user-defined
parameters, which is crucial as there is no rule for selecting those parameters
in a real-time SHM. The proposed novelty detection framework is applied to two
standard SHM datasets to illustrate its generalizability: Yellow Frame (twenty
damage classes) and Z24 Bridge (fifteen damage classes). All different damage
categories are identified with low sensitivity to the initial choice of
user-defined parameters with both introduced dynamic and static baseline
approaches with few or no false alarms.
