Virtual sensors are emerging as a transformative technology in the field of data measurement, control systems, and smart applications. Unlike physical sensors that rely on hardware to collect environmental or operational data, virtual sensors use advanced algorithms, machine learning models, and software systems to estimate or predict sensor readings. These software-based sensors analyze historical data, real-time inputs, and system behaviors to generate accurate estimations of parameters that may be difficult, expensive, or impractical to measure directly using traditional sensors.
One of the key benefits of virtual sensors lies in their cost-efficiency and adaptability. Physical sensors often require maintenance, calibration, and installation in environments that may be hazardous or hard to access. In contrast, virtual sensors can be deployed with minimal infrastructure, often integrated within existing digital systems. This makes them especially valuable in industries such as automotive, manufacturing, aerospace, and energy, where continuous monitoring and predictive insights are critical for…
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