The signs are there, now predict the future! Predicting System Failure and Reliability
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2014-08-29
Language
en
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Abstract
All kinds of machines and electronics are finding their way into our everyday lives and we
increasingly rely on their proper functioning. Especially when such machines are key to business
operations, their reliability is vital. For parking garages this is also the case, where a non-functional
machine can lead to both a short and long term loss of income. In order to guarantee that machines
stay operational, a generic predictive setup was developed. The basis of this setup was formed by sequences
of recorded events, which describe what happens to a device, including regular occurrences
as well as system failures.
Such a sequence of events are also known as time-series and our setup is aimed to predict future
time-steps of these series, which describe whether each possible event will occur in the future. Such
predictions can aid the process of deciding whether proactive maintenance should be performed or
not.
For our setup, we tested the Multilayer Perceptron and Conditional Restricted Boltzmann Machine
algorithms, as well a Recursive and Single Step paradigm for time-series prediction, which
determine how multiple future time-steps are predicted. We furthermore investigated the influence
of different data pre-processing methods on the setup's performance, such as the addition of extra
features to the dataset, which are generated using Principal Component Analysis, and the application
of an outlier treatment method, which enforces a maximum value during the normalisation
process.
The two mentioned data pre-processing methods were proven to be ineffective, while we demonstrated
that a setup utilising the Conditional Restricted Boltzmann Algorithm, in combination
with the Single Step prediction paradigm, was able to create generalised prediction models for
devices of different brands. Such a setup achieved similar average accuracy scores of almost 75%
for the events within predicted time-steps of the time-series, regardless of a device's type or brand,
which is sufficient for its intended real-world application.
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Faculteit der Sociale Wetenschappen