Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hybrid deep fault detection and isolation: Combining deep neural
  networks and system performance models   With the increased availability of condition monitoring data and the
increased complexity of explicit system physics-based models, the application
of data-driven approaches for fault detection and isolation has recently grown.
While detection accuracy of such approaches is generally good, their
performance on fault isolation often suffers from the fact that fault
conditions affect a large portion of the measured signals thereby masking the
fault source. To overcome this limitation and enable a more accurate fault
detection, we propose a hybrid approach combining physical performance models
with deep learning algorithms. Unobserved process variables are inferred with a
physics-based performance model to enhance the input space of a data-driven
diagnostics model. To validate the effectiveness of the proposed method, we
generate a condition monitoring dataset of an advanced gas turbine during
flight conditions under healthy and four faulty operative conditions based on
the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dynamical
model. We evaluate the performance of the proposed method in combination with
two different deep learning algorithms: feed forward neural networks and
Variational Autoencoders, both of which demonstrate a significant improvement
when applied within the hybrid fault detection and diagnostics framework. The
proposed method is able to outperform pure data-driven solutions, particularly
for systems with a high variability of operating conditions. It provides
superior results both for fault detection as well as for fault isolation. For
fault isolation, it overcomes the smearing effect that is observed in pure
data-driven approaches and enables a precise isolation of the affected signal.
We also demonstrate that deep learning algorithms provide a better performance
on fault detection compared to the traditional machine learning algorithms.

35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol