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Predictive Maintenance Predictive Maintenance Based on AI

| Author / Editor: Bernd Groß / Alexander Stark

Whether in online marketing, medicine or e-commerce - Artificial intelligence can be used to leverage great efficiency potentials in maintenance tasks. An industrial company in Germany is already successfully using this technology.

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The use of artificial intelligence creates the basis for efficient predictive analytics models.
The use of artificial intelligence creates the basis for efficient predictive analytics models.
(Source: ©greenbutterfly - stock.adobe.com)

The topic of Artificial Intelligence (AI) is gaining importance across all industries. Industrial companies are primarily interested in putting state-of-the-art technology into practice: AI models are applied to real business processes in order to make more intelligent, data-based decisions in real time.

How AI Makes Processes More Efficient

According to current Bitkom statistics, half of all industrial companies consider productivity growth to be the most important advantage associated with AI and Industry 4.0. There, the planning and the optimal orchestration of processes are in the foreground: Artificial intelligence can be used to network machines and systems. This improves productivity and creates more transparent processes.

In addition, this technology facilitates the evaluation of large volumes of data, which are already common in companies today. This favors a shift towards predictive analytics models in maintenance. These models clearly differ from conventional maintenance approaches. Companies no longer only react reactively or preventively. If predictive maintenance is to be used efficiently, the process data needs to undergo the following three steps:

  • Data capturing
  • Analysis
  • Evaluation

The result is a statistic that calculates the probability of occurrence for certain events. On the basis of this information, users can guarantee uninterrupted operation of their systems. The date for equipment maintenance can be planned precisely and does not lead to costly production downtimes.

User Example: Using AI in Practice

Certuss from Krefeld, Germany also makes use of a predictive analytics system. The steam generator manufacturer has complex multi-stage production processes. Errors in one work step can trigger a chain reaction and lead to a production standstill. The Certuss team uses the Cumulocity IoT platform to predict possible deviations in the production chain. The maintenance service records and evaluates various data such as pressure, temperature and current water level.

In addition, by analyzing the data collected, it is possible to improve the performance of the machines and increase productivity. Machine learning can further optimize the overall process quality, cycle time and energy consumption. Disturbances can thus be significantly minimized, and costs saved.

This article was first published by MM MaschinenMarkt

Original by Sebastian Hofmann / Translation by Alexander Stark

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