Predictive maintenance in manufacturing
Because manufacturing is a sector at the cutting edge of Industry 4.0, predictive maintenance plays a central role in anticipating breakdowns and optimizing performance. Yet customers often remain dissatisfied. Lack of historical data limits the effectiveness of machine learning algorithms. Threshold-based approaches are time-consuming and demotivating for teams.
Building digital twins is costly and energy-consuming, and some solutions lack the versatility to adapt to all use cases. Manufacturers are therefore looking for more agile, accessible and efficient tools to make the most of this strategic technology.
Microelectronics and failure prediction
At the top of the electronics chain, microelectronics generates a market worth several billion dollars each year.
Its cutting-edge instruments, complex production environment, and strong operational constraints make it an extremely demanding sector in which randomness must be kept to a minimum.
Thanks to DiagFit, our fault prediction software, manufacturers in this sector can look forward to zero unplanned downtime.
Test benches and quality supervision
Test benches in the manufacturing sector are subject to environmental constraints and sensor drifts, which can influence the device’s entire calibration and therefore the output batch quality. Test benches are therefore a key element in the production chain, which must guarantee the quality of potentially complex and critical equipment (satellites, aircraft cases, etc.). This equipment often includes a large number of sensors, with high acquisition frequencies, which require powerful and adapted supervision software, as DiagFit. In this case, predictive maintenance consists in detecting the drift of a sensor on the bench before a complete batch fails and has to be thrown away. This way, it allows the limitation of waste management and material costs, in addition to helping companies to be more environmentally responsible.
Predictive maintenance for robotics
Advanced robots need their precision to be guaranteed throughout their life.
Maintenance must be adapted to all possible use cases, which cannot all be anticipated in R&D.
This is why predictive maintenance models must be resilient to changes in use contexts, to changes in environment and potentially to changes in operating modes. DiagFit, our blind failure prediction software, was designed for this purpose.
