Engineering Magazine – June 2019

ENGINEERING JUNE 2019 31

be pulled from potentially thousands of
machines at relatively low cost.
The concept of a Smart Factory is
entirely dependent on the connectivity
enabled by Industry 4.0. Machines
that can sense and communicate can
provide a vast amount of valuable data.
However, this data needs to be filtered
and analysed if it is to be translated into
actionable insight for manufacturers.
Much like the gathering of machine
data, historically this analysis would
have been a highly manual endeavour,
involving teams of expensive data
scientists. More recently, however,
organizations like Senseye have
developed intelligent software to
automate this activity.
Senseye’s software, which can
be applied to any machine from any
manufacturer, can crunch through all
of the available data from an individual
machine to create a bespoke algorithm
to identify problems and, crucially,
spot the signs that indicate if and when
a machine will fail in the future. This
prognostic approach allows engineers
to undertake precisely the right
maintenance activities during periods
of planned downtime and fix problems
before they can affect production.

Bespoke algorithms are important

because the condition data outputs

from machines - even two of the

same make and model - are as unique

as a human fingerprint. A.I. does the

heavy lifting here, fine-tuning the

performance of each algorithm to

maximize its accuracy.

Industry 4.0 is a vital part of this

process. Automation is only possible

because of the data that is gathered

from the machines, but it also requires

computing power to analyse that

data. This crucial analysis is performed

elsewhere on the internet, in the cloud,

where the resources exist to power

the A.I. and run the algorithms on a

continual basis, regardless of where the

machines are located.

The benefits of reducing

downtime

Recent developments in condition

monitoring and predictive maintenance,

made possible, in part, through Industry

4.0, are significantly improving the

performance of industrial machinery,

while making the task of operating and

maintaining it easier and more efficient.

The cost of unplanned downtime

is a huge drain for any manufacturing

environment. In the automotive sector,

for example, each minute that critical

machinery is offline will cost the factory

tens of thousands of pounds.

Senseye’s software is used by a

number of leading manufacturers,

including the automotive giant Nissan,

to avoid this costly downtime by

automatically forecasting failures and

enabling engineers to make repairs

months before a predicted failure

might affect production. Customers

have, typically, saved 40% on

maintenance costs and halved their

levels of machine downtime.

Although we are still in the early days

of the Industry 4.0, it is clear from the

experiences of Nissan and others that

enabling machines to gather data and

connect with services like Senseye in

the cloud are already driving significant

improvements in machine effectiveness

and efficiency. The longer-term

potential of these technologies has yet

to be realized, but it is clear that it will

be truly transformative.

Dr Simon Kampa is CEO and co-founder

of Senseye

http://www.senseye.io

SOFTWARE