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Predictive maintenance is an advanced method based on real-time data and machine learning that is used to predict potential equipment failures. By analysing operating data, it is possible to make accurate predictions that enable targeted, efficient maintenance to be performed.

Opportunities created by predictive maintenance

  • Greater efficiency and lower costs: If signs of wear and tear or other looming issues are detected early on, you can then take targeted and effective action in response. This leads to a reduction in operating costs, since resources are utilised more efficiently and costly emergency repairs are avoided.
  • Minimise downtimes: Predictive maintenance allows you to detect potential failures at an early time, which in turn minimises downtimes. Power generation plants are available more consistently, which is particularly important in times of high demand.
  • Optimise operating processes: Predictive maintenance enables you to anticipate maintenance requirements and makes it possible optimise operating processes. The efficiency of energy generation can be further increased by fine-tuning parameters.
  • Extend the service life of systems: By detecting signs of wear and tear at an early stage and performing preventive maintenance in response to this, system operators can extend the service life of their systems. This leads to greater sustainability and reduces long-term investment costs.

Risks involved with predictive maintenance

  • Privacy and data security: The use of sensitive company data raises concerns about privacy and data security.
  • High upfront investment: Implementation requires a high upfront investment, which can be a financial obstacle for smaller companies
  • Complexity involved in integrating systems: Integration into existing system landscapes can prove difficult and requires careful planning
  • Reliance on quality of data: Accuracy is heavily dependent on the quality of the database.

The ‘Value-based Maintenance’ project – a joint initiative of adesso and RWEG

Challenge: The failure of a power plant can lead to heavy financial losses for the energy supplier and have far-reaching effects on the supply of power to cities and regions. Effective monitoring and predictive maintenance of machines and power plant components are therefore essential. However, the flexible deployment of power plants and the vast number of sensors and data sources in modern power stations make it difficult to maintain an overview and identify when maintenance is needed in good time.

Solution: An integrative maintenance data platform, which aggregates all relevant data, processes it intelligently and visualises it in a user-friendly way on flexible dashboards, enables maintenance staff to monitor the condition of individual components in real time and identify maintenance requirements at any time. Machine learning models can be used to accurately predict wear and tear as well as the optimum maintenance windows.

Finding the right balance

Predictive maintenance is a key step in the energy generation process. There are immense opportunities here, from greater efficiency to extending the service life of the system. But one should not underestimate the risks involved either. A balanced approach, in combination with innovation and data security, paves the way for a sustainable, predictable future for the energy industry. In this changing environment, it is important to exercise care and foresight when you seek to exploit the new opportunities that are opening up.

Would you like to find out more about the topic of predictive maintenance and possible applications in the utilities space? Feel free to contact us.

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Picture Stephen Lorenzen

Author Stephen Lorenzen

Stephen Lorenzen is a managing consultant and has been working in the energy industry for almost five years. He sees himself as a pragmatic and interdisciplinary all-round consultant with several years of professional experience in innovation management, requirements engineering and classic as well as agile project management.

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