Industrial Analytics offer

From data science consulting and customised analytics solutions to Industrial AutoML Self-Service.

Industrial Analytics offer

Industrial Analytics Consulting

Use our expertise in Industrial Analytics as an accelerator for your plans. We are the partner at your side, with in-depth knowledge of the IoT and Analytics team, our experience from our own industrial application as well as from diverse customer projects. The spectrum is broad in terms of content and is tailored to your needs.

  • Selection and evaluation of suitable use cases
  • Consultation on data collection by using suitable hardware and software
  • Analysis of the existing data quality and possibilities for optimisation
  • Selection of suitable data formats and interfaces
  • Implementation of the solution in the existing infrastructure

Our data science experts are at your disposal with the necessary experience. From the industry, for the industry.

Example: Use Case Workshop - making ML-based services specific & tangible

The challenge of finding the specific entry point

  • In the field of ML-based services, there are numerous options and attractive possibilities.
  • You have already discussed some ideas and first use cases.
  • The next step: achieve first specific results with a pragmatic approach.

The focus on industrial applications

  • How you can create new digital services for your machines.
  • How you can increase the availability of your machines.
  • How you can use ML-based technologies for this.

The approach, pragmatic and result-oriented

  • One-day, personal workshop with Weidmüller Business Consultant & Data Scientist
  • Your multidisciplinary team of stakeholders - management, product, application, service, sales
  • We design the implementation plan together - your guide for implementation

Your benefit, for a quick first success

  • Achieving a common understanding among stakeholders about the goals & opportunities
  • Prioritisation of suitable application(s) and use cases
  • Sample elaboration, planning and tools to start the implementation specifically

Customised analytics solutions

With the selection of a suitable use case in the area of Industrial Analytics, the implementation can begin. In practice, a five-phase approach has proven to be successful. At the start of the project, the focus is on analysing the problem and defining the objective. The validation of the selected use case follows. This phase also determines which specific failures should be predictable. During the subsequent exploration phase, checks are carried out to see whether a defined error can be detected on the basis of the collected measured values or if a higher data quality is required.

In the proof of concept (PoC), a statistical model is developed for the automatic detection of the error and thus the technical and economic feasibility is checked using previously recorded data (offline analysis).

During the pilot phase, a functioning prototype is executed on a sample application at runtime (online analysis). In doing so, findings and experience values are collected, which are implemented in the final analytics solution during the last phase. This solution can be applied to an unlimited number of machines of the same type.

Industrial AutoML – ML Self-Service

Developing industrial analytics solutions usually requires the specific know-how of a data scientist. Our Automated Machine Learning tool allows you to use AI and ML-based models independently, without the need for external support. It enables you to generate models that can recognise the normal and erroneous behaviour ofyour machines based on your own data and application know-how. The platform independent software helps you to do this with automated model generation and a simple user interface.

Your special advantages
  • Based on your own data and expert knowledge
  • Without the need for external consulting or a data scientist
  • Automatic generation of models
  • Simpler and faster solution development process
  • Visualisation of machine data
  • Independent training of the software to detect normal operation and malfunction of the machine
  • Platform Independent
  • Independent continuous optimisation of the models