Event Detection Platform (using pattern matching analytics)

This 18 month project aims to build a pre-commercial demonstrator consisting of an IT
platform which is capable of deploying Cybula’s event detection methods to monitor the
behaviour of complex assets (machines, engines, infrastructure and with medical applications,
people). These methods use novel pattern matching techniques on multi-variate, time-series
data; software analytics that are very much in demand as many organisations are swamped
with the flow of monitored asset data and they lack the tools to interact with the data. The
fundamental questions which these analytics help to answer are (i) is the asset operating as
expected? (ii) if there is an abnormality, has this event occurred in the past? (iii) what can be
learnt from previous events and finally (iv) what time do we have before action is required?
Cybula’s Signal Data Explorer, a benchtop software package, can be used by specialists to
browse large volumes of multi-variate data before building abnormality models or event
signatures and then validating them on archived data. This testing environment has attracted
the involvement of several ‘blue-chip’ companies across several market applications
(aerospace, rail, energy generation, oil & gas production and critical-care medicine) and
Cybula continues to invest in new features.
This demonstrator will allow these models and event signatures to be imported as workflows
from Signal Data Explorer and deployed on continuously streamed asset data. The user will
be able to structure their asset data and attach input parameters to feed the different models.
A visualisation module will enable the user to configure screen displays to visualise detected
abnormalities or events over time. The project anticipates the demonstration platform to be
capable of handling large scale asset monitoring problems involving thousands of monitored
variables and up to 100 detection models running simultaneously.