Predictive Maintenance data analysis on
SmartFactoryKL generated data
Based on the mutual correspondence between SDIL and SmartFactoryKL we identified
potential research fields for cooperation that meet with both strategic research goals. The
discussions led to a vision that is strongly related to the current trending research topics
“Industry 4.0” and “Internet of Things” and falls into the area of expertise of real time data
integration and knowledge management. SmartFactoryKL will be used as a realistic research
and demonstration platform. The SmartFactoryKL in itself is a manufacturer-independent
demonstration and research platform and unique in Europe. Here, innovative information and
communications technologies and their application are tested and developed in a realistic,
industrial production environment.
Manufacturing plants are characterized by a series of machinery that typically produce some
goods during a series of sequential processing steps in a production line. Each of these steps
makes use of different machines and tools that are customized for their specific task. The
proper functioning of that used machinery in turn depends on its environmental parameters
like voltage, pressure, temperature etc. – values that are typically monitored on gauges, control
lamps, etc. Modern machinery, which satisfies the paradigms of the Internet of Things (IoT) is
characterized by a large amount of sensors that continuously provide such status information
that is relevant for the production process. The sensors can be monitored, alarms can be raised
based on values exceeding given thresholds and stop the running production until some
engineer does some fixing. Moreover, as the nature of most sensors is much more
complicated, their values vary over time even in normal operation and a malfunction cannot be
detected with simple exceeding thresholds but requires time series analysis and recognition of
outliers. There is however a larger economic potential in observing and analyzing these
sensors. Even slightest deviations in these dynamic values from the expected values can lead
to significant quality loss in the produced good by so called butterfly effects. An intelligent
monitoring, storage and analysis of the sensory data can have multiple consequences:
 Analysis of time series of all sensors in a production line operating under normal conditions
allows training of anomaly detection algorithms. Such trained algorithms can classify the
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overall system performance much better than handcrafted rules for a selection of sensors
and their expected set values.
Archiving of machine data for later reference and analysis can be much more efficient, as
only states classified as “not normal” need to be stored.
Retrieval of such extraordinary conditions within data logs for later analysis is easier, as
not only time stamps but explainable conditions that led to the outliers can be used for
indexing the data.
Goods that were affected from outlier conditions during their production can be identified
by their serial numbers if serial numbers are used for indexing of (outlier) sensory data.
Outliers can be detected during live monitoring, enabling a much more efficient and faster
intervention, yielding in fewer failures of produced good and cost optimization.
Long term optimizations of outlier detection: Goods that were produced under outlier
conditions but do not get significant defect in their mid-term life cycle can serve to accept
their conditions as “normal” thus allowing the anomaly detection algorithms be become
more relaxed.
At the other hand, goods that go broken too early in their life cycle and that were produced
under outlier conditions can be used to sharpen/increase the conditions for production stop
in the future. This might reduce cases of warranty and enables further cost optimizations.
The intended joint research that will be conducted within this project addresses prototypical
investigations towards the above mentioned aspects. The sensory data that will be used within
this project will be extracted from the SmartFactoryKL production line and thus resembles real
data that contains the following information:
 Module information, e.g., energy consumption, operating hours, production status
 Product information (about order, client, priority)
 Topology information (neighbors left/right, port ids)
 Process information (process time, energy)
 Transport information (transport status, errors, etc.)
The SmartFactoryKL production line consists of five vendor-specific modules serving for the
manufacturing of an exemplary product, a customized business card holder. The assembling
process starts at the engraving module. Initially, all customized job data is written on the
product memory. The product itself carries this information along the whole lifecycle. The digital
product memory is realized through a RFID chip, which is integrated in the base plate of the
product. The engraving module afterwards unloads the base plate of the business card holder
and applies a customized engraving to it via a CNC-controlled miniature milling machine.
Thereupon, the engraved base plate is rotated and placed on a work piece, which bring it to
the next production module. The next two vendor-specific modules take over the mounting of
a clip to the casing bottom of the business card holder and the assembly of different colored
casing elements. A robot places the customized cover on the base plate and force fits the parts
together. Another module applies an individual 2D barcode on the top side of the business
card holder through laser marking. The final module performs two different tasks: a final
inspection of the product using a high-resolution camera, and the commissioning of the finished
customized business card holder. Before and after each processing step, the production
memory is read and updated via RFID. Modules can easily be removed or added during the
plant operation, which gives the ability to the plant operator to select the manufacturing module
of the provider that is most suited to the given requirements.
For the future development of such highly modular, multi-vendor production systems, it is
essential to monitor, to control and to process data across all stages of development.
Superordinate IT-systems need to access data continuously for this purpose. Key technologies
for enabling vertical integration within this plant structure are the communication standards
OPC UA and the integrated web server. The integrated OPC UA server in every vendorspecific module guarantees the problem-free interaction of different modules beyond
proprietary limits. In this way, information about plant topology, work piece specific energy
consumption, and status messages is transferred directly to superior IT systems. All these real
data that contains the information mentioned above can be used by the SDIL. The broad
application of future Industry 4.0 aspects will require new qualifications over the next few years;
predictive maintenance will be one of these key issues and will be tackled within this project.
Together with the partner IBM, SmartFactoryKL plans to run a joint project with Smart Data Innovation
Lab (http://www.sdil.de) with the following three phases::
1. By IT Summit 2015: SmartFactoryKL provided ~20GB generated data to SDIL that replicates
a specific problem in the past – an observed sporadic motor failure at several modules that
was finally identified as the result from the contact of the assembly belts of two adjacent
modules. This is a scenario demonstrated at SmartFactoryKL and represents a quantitative
extrapolation on SDIL by using the same analytics approach and technology at scale.
2. By HMI 2016: in preparation for HMI, SmartFactoryKL will run endurance tests for stability
assurance of the facility. They will generate massive data (and errors) that will then be
transferred to SDIL and analyzed. The results will be fed back in order to improve the
quality of the demonstration facility
3. Research will go on to identify different data gathering strategies, analysis techniques as
well as business models in the area of predictive maintenance, OEE and quality assurance
in the production context.
Data Innovation Community
Industrie 4.0
Projektpartner
Technologie-Initiative SmartFactory-KL e.V., DFKI, IBM
Ansprechpartner
Plamen Kiradjiev, kiradjiev@de.ibm.com
Dominic Gorecky, dominic.gorecky@dfki.de
Zeitraum
Nov 2015 - Juni. 2016