Predictive maintenance in manufacturing
Manufacturing has been at the forefront of industries’ high expectations to implement the number one application of the Industry 4.0 paradigm for several years: predictive maintenance.
However, customers are not yet fully satisfied for several reasons
A lack of historical failure data to train machine learning algorithms
Time-consuming threshold-based monitoring approach
Expensive digital twins
Cloud-only and non-versatile AI-based solutions
Therefore, manufacturers expect software solutions that can remove these major obstacles to achieve a high return on investment.
Predictive maintenance in manufacturing
Manufacturing has been at the forefront of industries’ high expectations to implement the number one application of the Industry 4.0 paradigm for several years: predictive maintenance.
However, customers are not yet fully satisfied for several reasons
A lack of historical failure data to train machine learning algorithms
Time-consuming threshold-based monitoring approach
Expensive digital twins
Cloud-only and non-versatile AI-based solutions
Therefore, manufacturers expect software solutions that can remove these major obstacles to achieve a high return on investment.
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.
Located in Grenoble, Amiral Technologies is part of the Minalogic competitiveness cluster and benefits from a highly technological area focused on microelectronics.
Use cases
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.
Located in Grenoble, Amiral Technologies is part of the Minalogic competitiveness cluster and benefits from a highly technological area focused on microelectronics.
Use cases
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, Airbus cases, etc.).
This equipment often includes a large number of sensors, with high acquisition frequencies, which require powerful and adapted supervision software.
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.
Use cases
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, Airbus cases, etc.).
This equipment often includes a large number of sensors, with high acquisition frequencies, which require powerful and adapted supervision software.
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.
Use cases
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.
Use case
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.
