  Multi-way data analysis has become an essential tool for capturing underlying
structures in higher-order data sets where standard two-way analysis techniques
often fail to discover the hidden correlations between variables in multi-way
data. We propose a multi-objective variational autoencoder (MVA) method for
smart infrastructure damage detection and diagnosis in multi-way sensing data
based on the reconstruction probability of autoencoder deep neural network
(ADNN). Our method fuses data from multiple sensors in one ADNN at which
informative features are being extracted and utilized for damage
identification. It generates probabilistic anomaly scores to detect damage,
asses its severity and further localize it via a new localization layer
introduced in the ADNN.
  We evaluated our method on multi-way datasets in the area of structural
health monitoring for damage diagnosis purposes. The data was collected from
our deployed data acquisition system on a cable-stayed bridge in Western Sydney
and from a laboratory based building structure obtained from Los Alamos
National Laboratory (LANL). Experimental results show that the proposed method
can accurately detect structural damage. It was also able to estimate the
different levels of damage severity, and capture damage locations in an
unsupervised aspect. Compared to the state-of-the-art approaches, our proposed
method shows better performance in terms of damage detection and localization.
