Optimization of turbine production through AI-supported quality analysis

Quality assurance plays a crucial role in the highly specialized production of turbines. Our approach uses machine learning and the analysis of production data from thousands of turbines to identify patterns in sources of error and efficiently ensure final quality.

Problem definition

Quality assurance in the production process of turbines requires precise monitoring of several critical steps. Errors in these steps can have a serious impact on the final quality, and identifying and evaluating them is a challenge.

Solution approach

We implemented AI models that analyze production data to identify sources of error at an early stage and assess their impact on the final quality of the turbines. In addition, the final quality is checked against quality standards by analyzing turbine noise.

The model

The AI models use specific algorithms to evaluate production data and compare it with noise recordings from the turbine tests. These models are trained to recognize patterns that indicate potential faults and enable a differentiated assessment depending on the tolerance range of the turbine.

1. machine learning for production data

Our machine learning algorithms analyze production data to systematically identify patterns that indicate potential sources of error and thus optimize the production process.

2. thematic analysis

By comparing the noise of a turbine with defined quality standards, our AI models can check compliance with quality criteria and ensure that each turbine meets the specific requirements.

Results and benefits

The application of this AI-supported analysis has significantly improved the company's ability to monitor the production process and ensure the quality of the turbines. By precisely identifying sources of error and their influence on the final product quality, targeted improvements can now be made. Noise-based quality testing allows an objective assessment of turbine performance and supports compliance with high quality standards. This leads to more efficient production, a reduction in rejects and increased customer satisfaction.

  • Early fault detection in the production process

  • Differentiated quality assessment based on noise analysis

  • Customizable AI models for specific tolerance ranges

  • Optimization of production efficiency and quality assurance

This use case illustrates how advanced AI technologies and data-driven analyses are revolutionizing quality assurance in production and enabling a high degree of precision and efficiency.

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