IT Services

From Smartphone Apps to Big Data Analytics.

IT Services

From Smartphone Apps to Big Data Analytics.

IT Services

From Smartphone Apps to Big Data Analytics.

IT Services

From Smartphone Apps to Big Data Analytics.

We are seeing increasing levels of complexity and are therefore breaking new ground in our software services. We apply agile development concepts and use the DevOps approach (Development Operations) to align the software in accordance with new requirements. In this way, for example, we develop apps for mobile devices, apply big data analytics and implement predictive maintenance.

Bertrandt is the ideal partner for trend developments such as autonomous driving, Industry 4.0, the smart grid or the Internet of Things (IoT).These topics document the new role of software and IT services in industry. We combine method expertise with many years of industry know-how in automotive engineering, electrical engineering, mechanical and plant engineering, aerospace, medical technology and the energy sector.

We develop using classical and agile project methods

Bertrandt works with classical development methods such as the V model or Automotive Spice. However, as projects become larger and requirements become more complex, rigid development and design processes based purely on performance specifications and milestones will fail. For that reason, Bertrandt also focuses on agile methods with user stories, short development cycles (sprints), a daily exchange of ideas and information (daily scrum meetings) and intensive feedback in sprint reviews.

These points have convinced our customers:

  • We deliver the initial implementable partial results and prototypes very quickly.
  • Users have an influence on the development at a very early stage.
  • Customer needs, questions concerning usability and the user experience (UX) are key methodological elements.
  • User studies and psychological reports have become the standard.

The DevOps approach represents a new understanding of quality in software development. The concept covers the entire life cycle of the development process – from the definition of the requirements and the build to delivery and customer support. This also applies to feedback relating to faults or improvements in a continuous process. Response times become shorter and shorter. App stores relay error messages in real time and corrections are made at very short notice.

Keywords for DevOps

  • Docker
  • Kubernetes
  • Azure DevOps

Bertrandt develops applications for mobile devices, for example for assembly support, the monitoring of machine pools, automated vehicle parking or the visualisation of data from the cloud.

  • With our “Guided Maintenance” HoloLens app, we use augmented reality to support service technicians in repair and maintenance work. A physical object such as a machine tool is expanded with the addition of virtual components and information. This provides the service technician with additional information on the machine and the maintenance work required.
  • The parking app enables a car to park itself automatically. The app calculates the route, including any obstacles or diversions. Computing takes place in the cloud; the data is compared with the local conditions and is updated with new sensor data.

One area of focus of our engineering and consulting services involved designing data-intensive processes, database-based developments and the use of methods for the analysis of large quantities of data. To achieve this, we have developed extensive know-how in order to process structured and unstructured data, to convert proprietary formats into suitable database structures and to use suitable database technologies such as SQL and NoSQL or Hadoop clusters.

  • We have set up the “Bertrandt Industry Cloud (BIC)” for the collection and storage of data, for analysis purposes, for the development of algorithms and for the application of machine learning. The BIC provides a finished basic toolbox that only needs to be adapted to the customer’s specific task. We combine methodological knowledge with 40 years of industry know-how relating to questions of development, design, production and support.
  • Automated driving requires very large quantities of data to be processed in real time. To do this, we use very large data storage systems and high-performance computing, which we have implemented in the Microsoft Azure Cloud with the aid of the “Bertrandt Automotive Cloud (BAC)”.   
  • We develop solutions for data labelling, for example to train a neural network with image data. For this, we use an automated process to mark millions of images with machine-readable descriptions.
  • We apply big data analytics for marketing projects in order to generate customer benefit analyses from sales data.

Keywords for the Bertrandt portfolio

  • Smart data management
  • Deep learning, neural networks, machine learning
  • Statistical data analysis
  • Data labelling
  • Predictive analytics
  • Pattern mining, pattern recognition
  • Correlation analyses, spectral analyses

Bertrandt supports customers from the mechanical and plant engineering industry and the logistics sector in setting up predictive maintenance solutions. In order to avoid malfunctions and machine downtime, system data are systematically collected and evaluated. This ensures that failure probabilities can be determined and the causes of faults can be detected at an early stage.

  • We advise manufacturers and plant operators on selecting measuring points, using smart sensors and developing and validating measuring equipment. For example, we equip older machines and plants which do not have the right interfaces with an IoT device for machine data acquisition.        
  • We develop solutions for storing data locally or for transferring data to a digital twin in the cloud. The entire life cycle of the real machine can be recorded on the virtual digital twin.
  • The machine data enables continuous monitoring for performance evaluation, making it possible to recognise deviations from normal behaviour and to determine failure probabilities.
  • All relevant performance data and the condition of the machine can be visualised on a dashboard.

Predictive maintenance services

  • Selection of sensors, sensor data fusion
  • Equipment functions, process sequences and measuring points
  • Machine data acquisition
  • Digital twins
  • Big data analytics
  • Machine learning
  • Design of dashboards
  • Apps for mobile devices

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