Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Normal behaviour models for wind turbine vibrations: An alternative
  approach   The identification of abnormal behaviour in mechanical systems is key to
anticipate and avoid their potential failure. Thus wind turbine health is
commonly assessed monitoring series of $10$-minute SCADA and high frequency
data from sensors. To monitor wind turbine vibrations, normal behaviour models
are built to predict tower top accelerations and drive-train vibrations. Signal
deviations from model prediction are labelled as anomalies and are further
investigated. More efficient models are expected to help enhancing the
identification of abnormal behaviour. In this paper we assess a stochastic
approach to reconstruct the $1$ Hz tower top acceleration signal, which was
measured in a wind turbine located at the wind farm Alpha Ventus in the German
North Sea. We compare the resulting data reconstruction with that of a model
based on a neural network, which has been previously reported as a data-mining
algorithm suitable for reconstructing this signal. In order to focus the
discussion on the similarities and differences of both approaches, we limit our
evaluation to a single input-output system; in doing so, we avoid other
differences, i.e. loading type, pre-processing or model complexity. Our results
present evidence that the stochastic approach outperforms the neural network in
the high frequency domain ($1$ Hz). Although neural network retrieve accurate
step-forward predictions, with low mean square errors, the stochastic approach
predictions better preserve the statistics and the frequency components of the
original signal, remaining high accuracy levels. The implementation of our
stochastic approach is available as open source code and can easily be adapted
for other situations involving stochastic data reconstruction. Based on our
findings we argue that such an approach could be implemented in signal
reconstruction for monitoring purposes or for abnormal behaviour detection.

27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
1
The categories of this paper are: cs
14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
16
The categories of this paper are: stat
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat