Embedded artificial intelligence
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Embedded AI is an ever-evolving field, and with advances in technology, we can expect to see more AI capabilities embedded in a wide range of devices and systems to improve their functionality and autonomy.
The integration of embedded artificial intelligence in scientific technical devices offers impressive possibilities for the real-time analysis of data and the detection of patterns or anomalies.
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2 - Improved local processing capabilities
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Improved local processing capabilities: Embedded processors will become more powerful, allowing more complex AI models and more advanced tasks to be performed directly on devices, without depending on an internet connection.
By integrating an embedded AI, it becomes possible to perform real-time data analysis, directly on the device itself. This allows for instant results and quick action based on extracted insights without going through the energy and time-consuming cloud.
By performing the processing locally, results can be obtained much faster, enabling faster and more efficient decision-making, reduced latency. Data to be sent to the cloud can be drastically reduced, reducing bandwidth usage costs.
The notion of security is also important, indeed local processing is better protected against cyberattacks and data leaks for equipment that collects sensitive data.
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3 - Miniaturization and integration
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Embedded AI systems are increasingly small and integrated into compact devices, which facilitates their integration into a wide range of objects, embedded systems, sensors... By integrating AI directly into devices such as sensors, miniaturization enables faster decision-making and response. This is crucial for applications requiring real-time feedback
Devices with embedded AI are becoming more compact, lightweight and unobtrusive, making them easy to integrate into tight environments. Miniaturization and integration can lead to lower manufacturing and deployment costs for embedded AI devices.
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4 - Predictive maintenance
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On-board AI can monitor critical train components, such as motors, braking systems and tracks, in real time and detect anomalies or signs of impending failure. This allows maintenance work to be planned proactively, breakdowns to be avoided and train downtimes to be reduced.
Onboard AI can analyze data from sensors onboard trains to detect faults or issues on the tracks. This makes it possible to optimize maintenance and repair operations.
By anticipating potential failures, predictive maintenance makes it possible to plan maintenance interventions proactively, thus avoiding unforeseen equipment failures. This reduces costs associated with emergency repairs, equipment downtime and production interruptions.
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5 - Learning
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Embedded artificial intelligence can monitor in real time the various components and systems of an equipment, such as motors, sensors, electrical circuits, etc. Using machine learning and data processing algorithms, it continuously analyzes the data collected by the built-in sensors to identify patterns, trends and anomalies.
Using techniques such as machine learning, in-vehicle AI can detect weak signals that indicate impending problems, even before they are visible or detectable by other traditional means of maintenance. For example, it can spot unusual temperature fluctuations, abnormal vibrations, pressure variations, etc.
By analyzing historical data and comparing it to normal patterns, on-board AI can predict potential failures and determine the optimal time to perform necessary maintenance. It can also recommend the best corrective actions to take, whether it is a simple preventive maintenance or a more in-depth intervention.
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