HANNOVER MESSE 2018, 23 - 27 April
Homepage>Exhibitors & Products >Predictive Analytics

Predictive Analytics

Fault diagnosis and prognosis to avoid machine downtime

Software Competence Center

Exhibitor details
Logo Predictive Analytics
Logo Predictive Analytics

Exhibition stand

Hall 2, Stand A44

Mr. Dr. Bernhard Freudenthaler
Executive Head Data Analysis Systems
Send E-mail

Product description

The term Industry 4.0 (the smart factory) has become a buzzword. Often huge data streams (e.g., machine data, process data, quality data) from diverse, heterogeneous data sources must be linked and analyzed in order to provide a useful basis for decision support and recommended
actions for humans. The application spectrum ranges from the process industry and production, to energy management to the manufacture and maintenance of machines and plants. Predictive maintenance is another important buzzword. If an industrial production system suffers unforeseen
disruption of a machine and therefore downtime, then this is a worst-case scenario: production is delayed and enormous costs can ensue.
Modern control systems are incapable of evaluating their own state so as to derive relevant information for the maintenance crew. The vision of machine diagnosis and prognosis is to close this gap. The goal is to predict
a time (warning) when corresponding service measures can avoid possible damage or downtime. This improves the state of a component or machine and production continues according to plan. Data mining and machine learning methods enable us to create fault prediction models in order to find this early warning point and thus plan predictive maintenance strategies.
The key is the combination of expert knowledge and data-based fault prediction models. This increases plant availability with reduced use of resources. The use of predictive maintenance strategies is
promising in many areas:
* Increased plant availability because fault prediction
promotes early detection of damages and reduced
down time
*Reduced material and energy costs because maintenance
is not bound to predefined schedules but
instead conducted as needed
*Improved planability of maintenance via state monitoring
*Heightened operational security by avoidance of
dangerous damages.
Navigate to for additional
information about Predictive Analytics und Predictive Maintenance.

All products offered by this exhibitor

Digital Twin

Develop Secure Software with Rigorous Systems Engineeringread more


Augmented Reality in Industry

Optimum Human-Computer Interaction for Weldingread more

Deep Learning

Knowldege- and Deep Learning Based Computer Vision Systemsread more

Rulebook Generator

Wissen aus Software generierenread more

Humans in Industry 4.0

Industry 4.0 (the smart factory) is usually equated with networking and improved flexibility. However, an important focus is the integration of humans in this new work environment. Those tasks where humans continue to be an important factor must be supported by machines. This elevates the importance more

Robust Embedded Visual Sensor Systems

Embedded visual sensor systems need to be literally robust not only because of their application domains. The robustness of the methods and algorithms also plays an important role in an environment with frequent interference, e.g. the noise as seen in the grayscale image on top. Increasingly, smart more

Prozess Mining for Industry

Processes are the heart of an enterprise and describe the interconnections of tasks in day-to-day operations. Modeling, analysis and improvement of these processes has been a research interest for many decades and for some years a focus of research and industrial projects at Software Competence Center more

Predictive Analytics

Fault diagnosis and prognosis to avoid machine downtimeread more


INDYCO (Integrated Dynamic Decision Support System Component for Disaster Management) is a decision support system which can 1) react rapidly and dynamically to changing situations during a disaster and 2) handle new disasters for which no contingency plan exists. The INDYCO system analysed different more

Stored items


Server communication error: Item could not be saved.