+++ Predictive maintenance enhances efficiency and sustainability at
the BMW Group +++ Smart digital monitoring and maintenance prevent
unplanned production downtimes +++ Cloud-based platform integrated
into global BMW Group production network +++
Munich. When it comes to maintaining
production systems, the solution of choice at the BMW Group is to use
sensors, data analytics and artificial intelligence (AI). So rather
than the previous approach of rule-based maintenance at regular
intervals, predictive maintenance is carried out, based on the current
condition of the system. This not only prevents unscheduled downtimes
in production but also makes an important contribution to
sustainability and the efficient use of resources by ensuring optimum
system availability. Innovative, cloud-based predictive maintenance
solutions are currently being rolled out across the global production network.
Predictive maintenance as an early warning system in production.
The increasing digitalisation of maintenance has made a predictive
approach more and more important. By monitoring equipment and status
data, predictive maintenance can forecast system failures before they
actually happen. To optimise the upkeep of systems, data is used to
decide when to replace components as a precaution so as to prevent
unnecessary downtimes. Predictive maintenance also enhances efficiency
and sustainability by ensuring intact components are not exchanged too early.
Forecasting states via a cloud-based platform.
Predictive maintenance uses a cutting-edge cloud platform to obtain
early warnings about potential production downtimes. The data comes
directly from the manufacturing systems themselves, which are
connected to the cloud only once, via a gateway, for monitoring, and
then constantly transmit data – usually once a second. Individual
software modules within the platform can be switched on and off
flexibly, as needed, to accommodate changing requirements immediately.
And with a high degree of standardisation between its individual
components, the system is globally accessible, highly scalable and
allows new application scenarios to be implemented easily and existing
solutions to be rolled out fast.
Predictive maintenance allows maintenance and repair processes to be
carried out as required by the actual condition of the system and
planned into already-scheduled production downtimes. Repairs can be
more accurately targeted and carried out more cost- and
resource-efficiently. In addition, extending running times prolongs
the service life of tools and systems significantly. The guiding
principle behind the provision of this solutions is: Developed once,
rolled out often – across the BMW Group production network.
Diverse range of applications.
The flexible, highly automated systems in mechanical drivetrain
production manufacture a conventional engine or casing for an electric
motor every minute. To keep these machines in good condition,
predictive maintenance uses simple statistical models – or predictive
AI algorithms, in more complex cases – to detect any anomalies. It
then issues visual warnings and alerts to inform employees that
maintenance is due.
Over in the bodyshop, the welding guns perform about 15,000 spotwelds
each per day. To prevent potential downtimes, data from welding guns
around the world is collected by specially developed software. It is
then sent to the cloud to be collated and analysed with the help of
algorithms. All the data is displayed on a dashboard for worldwide use
to support the maintenance processes.
In vehicle assembly, predictive maintenance helps prevent downtimes
in conveyors. At BMW Group Plant Regensburg, for example, the control
units of the conveyor systems work 24/7 to send data on points such as
electrical currents, temperatures and locations to the cloud, where it
is constantly evaluated. The data specialists can then identify the
position, condition and activities of every conveyor element at any
given time. Predictive AI models use the data to detect any anomalies
and locate technical problems.