All use cases of the blind failure prediction software


The manufacturing sector has for several years been at the forefront of industries' high expectations to implement the number one application of the Industry 4.0 paradigm: predictive maintenance. However, customers are not yet fully satisfied.

Why? For several reasons:

  • lack of historical failure data to train machine learning algorithms
  • a time-consuming condition-based monitoring approach
  • expensive digital twins
  • cloud-only AI-based solutions that are not multi-purpose


Manufacturers are therefore looking for software solutions that can remove these major obstacles to achieve a high return on investment.



Because transport is an ever-growing connected sector - connected cars, connected planes and helicopters, connected trains, connected ships - operators and manufacturers need to mobilise huge investments in sensors to deliver low-carbon vehicles that are ideally 100% safe.

The ability of predictive maintenance software solutions to adapt to different operating contexts, to be integrated with devices/sensors, to provide versatile models, is crucial for these companies.



The energy sector is a resource-intensive business involving several critical and expensive pieces of equipment that need to be monitored to achieve the highest possible uptime. The market is undergoing a rapid transition driven by new types of energy, changing distribution models and changes in supply and demand. Companies need to adapt quickly. In addition, the sector is undergoing a rapid transformation due to environmental constraints. The diversity of equipment and data types, combined with the lack of historical failure data, makes it very difficult to design cost-effective predictive modelling solutions.




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