Artificial Intelligence (AI) in Sensor Technology

In the age of Big Data and the Internet of Things (IoT), sensors are the "eyes and ears" of modern systems. However, traditional sensors often reach their limits. Enter AI-optimized soft sensors - an innovation that goes far beyond simple data collection.These intelligent soft sensors merge multiple data streams using machine learning and advanced signal processing to create virtual measurements. This enables real-time decision-making, future event predictions, and localized data analysis.

Unlike conventional sensors that rely solely on data collection, our soft sensors take a step further: they analyze and process data directly at the sensor node without the need to upload it to the cloud. This significantly enhances data security, energy efficiency, and response speed.

Key Benefits of AI-optimized Soft Sensors

• Data Filtering and Prioritization: Advanced AI algorithms distinguish critical signals from background noise, ensuring only the most relevant data is transmitted.
• Predictive Insights: By learning from historical data, soft sensors identify patterns to predict potential issues, such as failures, before they occur.
• Integrated Data Sources: Soft sensors combine input from multiple physical sensors and AI models into a unified, correlated signal.
• Real-Time Decision-Making: Localized data analysis enables immediate actions without human intervention.

Applications of Intelligent Soft Sensors

Medical Diagnostics

Wearable multisensor systems gather vital parameters from the human body, offering valuable insights into health conditions. Soft sensors go a step further by deriving "soft" parameters - complex metrics calculated from raw data. These metrics can detect early signs of conditions like wound development before symptoms appear.

The integration of miniaturized, high-precision sensors with AI-driven signal processing has revolutionized medical diagnostics and therapy.  By enabling real-time measurements and localized data analysis, soft sensors allow for faster responses to physiological changes, improving treatment outcomes, reducing costs, and shortening recovery times.

Machine Monitoring and Predictive Maintenance

The health of machinery often depends on factors like process stages, environmental conditions, and maintenance history. For instance, vibration levels or power consumption can vary significantly based on workload. By combining sensors with AI, soft sensors detect complex data patterns, representing a machine's operating state.

Soft sensors calculate "soft" metrics, such as stress levels or wear factors, that traditional sensors cannot measure directly. This data supports condition monitoring, which identifies anomalies - rare events signaling potential failures. If an anomaly is detected, the machine can be proactively shut down to prevent damage.

Additionally, this data helps estimate the remaining service life of equipment, enabling Predictive Maintenance. Maintenance can then be scheduled based on actual needs, minimizing downtime and reducing unnecessary part replacements.

Environmental Protection and Climate Action

While AI adoption has raised concerns about increased energy demand from data centers, soft sensors with Edge AI help reduce this burden. By analyzing data directly at the sensor node, they eliminate the need for constant cloud uploads and storage.

The combination of sensors and AI methods offers immense potential for climate protection: it can significantly lower factory energy consumption, guide buildings toward CO2 efficiency, and enhance real-time emissions forecasting and air quality monitoring in cities.