AI in Action: The Potential of Test Data
Test systems in the industry generate large amounts of data every day. There's real money
here – provided the data is processed and evaluated correctly. ZF Test Systems is therefore
introducing Advanced Analytics to its business. Red-ant offers the hardware solution for
recording and pre-processing NVH data (noise, vibration, harshness).
It's the last stop on the production line before a product departs the ZF world to reach the
customer: the end-of-line test bench. Here it's either thumbs up or thumbs down, green or
red, "OK" or "NOK". If ZF identifies in this phase any parts that are "not OK", this saves costs
and underpins the Group's reputation as a quality supplier. If NOK parts – such as a car
transmission – had been delivered to customers, this could have led to complaints or even a
recall of the vehicle.
Test Data – a Digital Treasure Trove
Consequently, ZF's end-of-line test systems test parts worldwide every second – and
document the result digitally. End-of-line test systems generate in this way up to 30,000
datasets per day. "Our machines produce digital information that is suitable for further
evaluation with AI algorithms," explains Simone Fuchs, responsible for Advanced Analytics at
ZF Test Systems. "We combine the know-how as machine manufacturers, as AI experts and as
specialists in discrete manufacturing – that is, wherever serial production of high-quality parts
is concerned. We can turn this domain knowledge into attractive practical solutions." These
can help to improve automation (and thus make production more efficient), to increase
quality or to reduce emissions throughout the entire manufacturing process, rendering it
more sustainable.
No wonder that many customers of ZF Test Systems also want to use their data – or are
encouraged to do so by consulting companies or software providers. "Many want to use AI.
But to put AI-based models in the right context in production, our domain-specific expertise is
needed," Fuchs explains. Therefore, ZF Test Systems wants to offer its analytics solutions as an
additional business in combination with the sale of the test benches. "The time is right. We
have been able to demonstrate our models in several pilot projects in recent years. The
resulting solutions are integrated into various production lines and create quantifiable added
value on a daily basis."
„Tatoo“ Makes Data Available
However, this didn't happen by itself. Anyone who wants to evaluate data with AI algorithms
must first compile it at a central location, prepare it, standardize it and make it available. Since
many analytics projects fail already at this stage, ZF Test Systems has developed its own
product for automatic long-term storage in a central system in collaboration with red-ant.
"Tatoo" can store all data during or after production in a central database, where it is available
for evaluations.
An example with end-of-line test benches at the ZF plant in Saarbrücken shows how the
potential of the data can be used. Together with experts from the ZF AI Lab Saarbrücken, ZF
Test Systems has developed an AI-based algorithm with which the NVH behavior of
transmissions and electric motors can be digitally assessed. In components that rotate at high
speed – or those that transmit enormous forces – irregularities in NVH behavior can indicate
potential damage later on. Sonograms provide information about which frequencies are
present at which speeds. Dr. Nicolas Thewes and his colleagues have developed algorithms
that create a pixel-perfect map from thousands and thousands of sonograms of OK-tested
transmissions, which is used as a reference for all subsequent tests. Put simply, the sonogram
of each new test run is compared with the reference model in real time. Based on any
deviations detected, the AI provides important insights to the production experts.
Pilot Project Finds Noise Anomalies per Pixel
This algorithmic approach enables end-of-line testing to find errors that have never occurred
before. And the AI-based processing of the test data goes even further: The large amounts of
data make it possible to train classification algorithms to assess very accurately the errors that
occur in NOK-tested components. "The enormous potential of our solution lies in this
automated classification," emphasizes Thewes. In the past, every NOK specimen, together
with its data, had to be thoroughly examined by product experts in order to identify the cause
of the error. Increasingly, an AI system can take over such tasks automatically, 24/7. The
algorithm already handles one-third of the classifications autonomously and accurately, with
the share expected to rise. The example shows how highly qualified personnel can be relieved
thanks to AI applications. It's a convenient problem solution at ZF's plants, where the experts
can now devote more time to their actual tasks. Additionally, this accelerates product
optimization – because specific reference points for improved quality in production can also
be derived from the NOK cases.
Rollout Approaching
The producing ZF units are so convinced by the approach and product of the Group's Test
Systems subsidiary that “wAIve Guard“ will soon be used at several plants. And not only in the
Electrified Powertrain Technology Division, but also in the Industrial Technology Division,
specifically at the wind power transmission plants in Lommel (Belgium) and Tianjin (China).
"With the rollout, we will gain further experience and, above all, further data, the crucial fuel
behind every AI solution," Thewes is convinced. By that time, Simone Fuchs and her team will
have already developed further use cases on how to turn production and test data into more
quality, more sustainability and more efficiency in production. A clear added value for ZF Test
Systems' and red-ant customers and at the same time a concrete example of how relevant
production and end-of-line test data can be used profitably.