+++ Predictive maintenance at BMW Group Plant Regensburg –
AI-supported system monitors conveyor technology during assembly +++
Integrated, learning maintenance system identifies potential faults
early, avoiding more than 500 minutes of vehicle assembly disruption
every year +++
Regensburg. Preventing unplanned stoppages before
they can occur is the aim of the smart analysis system being used in
assembly at BMW Group Plant Regensburg. Predictive maintenance is
proactive and preventive – and this is precisely what the smart
monitoring system offers. Data-driven analyses of conveying equipment
allows potential faults to be identified early and avoided – thereby
maintaining optimal vehicle production flow. The artificial
intelligence (AI)-supported system avoids an average of around 500
minutes of disruption per year in vehicle assembly at the Regensburg
plant alone.
Data analysis for faster, preventive response to potential disruptions
For assembly at BMW Group Plant Regensburg, vehicles are generally
attached to mobile load carriers or skid systems, which pass through
the production halls in a chain. Any technical fault in the
state-of-the-art conveyor systems can bring assembly lines to a
standstill – requiring more maintenance effort and thus resulting in
higher costs. To prevent this from happening, the innovation team at
BMW Group Plant Regensburg has developed a system that can identify
potential technical defects early – and thus avoid any lost
production. The conveyor elements affected can be removed from the
assembly line and repaired, away from production. The advantage is
that the monitoring system does not require any additional sensors or
hardware, but evaluates existing data from installed components and
conveyor element control. An alarm sounds if anomalies are found.
For example, the load carriers used to transport vehicles through
assembly send various data to the carrier control system. This data is
then transmitted via the carrier and plant control system to the BMW
Group’s own predictive maintenance cloud platform. This is where the
analysis begins: The algorithm constantly searches for irregularities,
such as fluctuations in power consumption, abnormalities in conveyor
movements or barcodes that are not sufficiently legible, which could
trigger a malfunction. If anomalies are found, the maintenance control
centre receives a warning message, which it assigns to the maintenance
technician on duty. “The surveillance monitors at our control centre
run 24/7,” explains project manager Oliver Mrasek. “This enables us to
respond quickly to any kind of fault report and take the affected
vehicle out of the cycle.”
Implementation – AI-supported, standardised and cost-effective
Predictive maintenance is not a standalone solution, stresses Mrasek.
The system was standardised in collaboration with the BMW Group’s
central shopfloor management and other plant sites to facilitate swift
and straightforward rollout to other BMW Group plant locations around
the world. This approach is also cost-effective. “We don’t need any
additional sensors, so the only costs are for storage and computing power.”
Machine-learning models developed in-house were also implemented in
the system, which uses so-called heatmaps with various colour codes
for different abnormalities to visualise the model’s findings. “This
allows us to map different fault patterns in various components and
respond to them in a targeted manner,” explains Mrasek.
Based on these practical findings, the algorithms are continuously
improved and refined. The team is currently in the process of
connecting additional installations, optimising the system and
integrating recommended actions into fault messages. The fault message
could, for example, indicate similar problems that have occurred in a
system. This simplifies troubleshooting for maintenance technicians –
for example, if an impeller on the conveyor trolley is defective.
“Optimal predictive maintenance not only saves us money, it also means
we can deliver the planned quantity of vehicles on time – which saves
a huge amount of stress in production,” explains Deniz Ince, the
team’s data scientist.
The next goal: Predictability – and two patents.
Mrasek and his colleagues have been working on data-driven monitoring
of conveyor technology for the past six years. Today, around 80
percent of the main assembly lines are already monitored in this way.
“We can’t detect or prevent every single fault in advance, of course –
but we are currently avoiding at least 500 minutes of downtime per
year in vehicle assembly alone,” he explains. It is easy to calculate
how much this adds up to. At BMW Group Plant Regensburg, a vehicle
rolls off the assembly line roughly every minute – every 57 seconds,
to be exact – and the system is already being used in conveyor systems
at the plant sites in Dingolfing, Leipzig and Berlin.
The aim is to further exploit the possibilities of artificial
intelligence, with the system learning to estimate how much time
remains between detection of the fault and the potential stoppage.
This would help technicians decide how soon they need to perform
maintenance and allow them to prioritise, if needed. Mrasek also sees
further potential in other areas of the plant: “We are currently
testing whether we can also use the system for the equipment used to
fill our vehicles with brake fluid and coolant, for example.”
Although there are already numerous options for predictive equipment
maintenance, Regensburg’s integrated learning system is, so far, the
first of its kind. Compatibility with predictive maintenance is
therefore already being written into tenders for new conveyor
technology. Equipment manufacturers are also praising the system,
since they benefit from its evaluations as well. The BMW Group has
already registered two patents for its in-house development.
BMW Group Corporate Communications
Dominik
Hämmerl, Communications Regensburg and Wackersdorf
Cell phone:
+49 151 6060 3889, Email: Dominik.Haemmerl@bmw.de
Saskia Graser, Head of Communications Regensburg and
Wackersdorf
Cell phone: +49 151 6060 2014, Email: Saskia.Graser@bmw.de
Media website: www.press.bmwgroup.com
Email: presse@bmw.de
BMW Group Plants Regensburg and Wackersdorf
The BMW Group has viewed itself for decades as the benchmark for
production technology and operational excellence in vehicle
construction – including at its locations in Regensburg and
Wackersdorf.
The BMW Group vehicle plant in Regensburg has been
in operation since 1986 and is one of more than 30 BMW Group
production locations worldwide. A total of up to 1,000 vehicles of the
BMW 1 Series, BMW X1 and BMW X2 models come off the production line at
Plant Regensburg every workday – destined for customers all over the
world. Different types of drive trains are flexibly manufactured on a
single production line – from vehicles with internal combustion
engines to plug-in hybrids, to fully-electric models.
High-voltage batteries for the electric models built in Regensburg
are also produced locally, in direct proximity to the vehicle plant.
They are assembled at the electric component production facility,
which opened in 2021 at the Leibnizstrasse location.
BMW
Innovation Park Wackersdorf also belongs to the Regensburg site. The
55-hectare campus built in the 1980s was originally intended as a
nuclear reprocessing facility. The BMW Group has located its cockpit
production there, as well as its parts supply for overseas plants. In
addition to BMW as the largest employer, several other companies are
also based at Innovation Park Wackersdorf. A total of around 2,500
employees work there.
The BMW Group core staff at the Regensburg and Wackersdorf locations
in eastern Bavaria is made up of around 9,000 employees, including
more than 300 apprentices.
www.bmwgroup-werke.com/regensburg/de.html
