Virtual Sensing AI. These AI-powered systems infer physical conditions and environmental parameters using data from other sources, models, and machine learning.
Introduction
Virtual Sensing AI refers to the capability of artificial intelligence to estimate physical measurements or states that are not directly measured by a dedicated physical sensor. Instead, it relies on mathematical models, data fusion from other available sensors, and advanced machine learning algorithms to predict or infer the desired parameter. This capability extends the reach of monitoring and control in environments where traditional sensors are impractical, too costly, or simply cannot be deployed. The core idea is to create a 'soft sensor' or a 'software sensor' that digitally simulates the function of a physical sensor. When augmented with AI, these virtual sensors can learn complex, non-linear relationships between easily measurable inputs and hard-to-measure outputs, providing robust and often more accurate estimations than traditional model-based approaches alone.
How it works
Virtual Sensing AI typically operates by collecting various input data streams from existing physical sensors, system parameters, or historical records. This raw data might include temperature, pressure, flow rates, motor RPMs, or other readily available measurements. An AI model, often a form of machine learning such as neural networks, regression models, or deep learning architectures, is then trained on a dataset containing both the input data and the corresponding actual values of the parameter to be estimated (obtained during a calibration phase or from a high-fidelity sensor). The training process enables the AI to learn intricate patterns and relationships within the data, effectively creating a predictive function. Once trained and validated, the virtual sensor can take new, real-time input data and apply its learned intelligence to estimate the unmeasured parameter. For instance, a virtual sensor might estimate the internal temperature of a complex machine part by analyzing external temperature readings, power consumption, and run-time data, rather than requiring a sensor embedded directly within the part. There are generally three approaches to virtual sensing: model-based, data-driven, and hybrid. Model-based virtual sensors rely on physics-based equations to describe the system's behavior. Data-driven virtual sensors, heavily reliant on AI, build their models purely from historical data. Hybrid approaches combine both, using AI to refine or compensate for uncertainties in physics-based models, often leading to the most robust and accurate virtual sensors.
Key strengths
Virtual Sensing AI offers significant advantages over purely physical sensing solutions. Firstly, it dramatically reduces hardware costs and simplifies maintenance by minimizing the need for numerous physical sensors, especially in complex or harsh environments. Secondly, it enables the measurement of parameters that are difficult, impossible, or unsafe to measure directly, such as the wear state of internal components or conditions within a high-pressure vessel. Furthermore, virtual sensors provide enhanced data redundancy and fault tolerance; if a physical sensor fails, its readings might still be estimated by an AI-powered virtual sensor using data from other healthy sensors. They also facilitate predictive maintenance by estimating degradation or impending failures, and improve operational efficiency by providing real-time insights into critical but unmeasured system states. The ability to leverage existing data infrastructure makes them a highly scalable and adaptable solution.
Practical applications
- Predictive maintenance for industrial machinery
- Estimating battery state-of-charge and health in electric vehicles
- Monitoring air quality and pollution levels in smart cities
- Optimizing energy consumption in smart buildings and HVAC systems
How it compares
Virtual Sensing AI stands in contrast to traditional physical sensors, which provide direct, often highly accurate measurements of specific parameters. Physical sensors are the ground truth, but they come with installation costs, calibration needs, and susceptibility to environmental damage or wear. Virtual sensors, on the other hand, infer their readings, making them dependent on the quality and relevance of their input data and the robustness of their underlying AI model. They are a software solution, offering flexibility and scalability without the physical constraints of hardware. Another related concept is the 'digital twin,' which is a virtual replica of a physical asset, process, or system. Digital twins often integrate virtual sensors to provide a comprehensive, real-time understanding of their physical counterpart's status, performance, and health, especially for parameters that are difficult to measure directly. While a digital twin is a holistic model of an entity, a virtual sensor is a specific component within that model focused on inferring a particular measurement.
Best practices (2026)
- Ensure high-quality, diverse, and representative training data for AI models.
- Routinely validate and recalibrate virtual sensors against ground truth measurements.
- Implement robust monitoring for input data integrity to prevent 'garbage in, garbage out' scenarios.
Common pitfalls
- Over-reliance on a virtual sensor without periodic validation can lead to inaccurate estimations over time.
- Limited accuracy when operating outside the range of conditions seen during AI model training.
- Susceptibility to errors if the physical input sensors provide faulty or drifted data.