Hydrogen Sensor Calibration via Neural Network Voltage Profiling

The patent applies preliminary action by performing comprehensive calibration measurements at multiple heating voltages and temperatures before production. The calibration data is collected in advance under various conditions (different heating voltages UH and temperatures T), and a neural network model is trained beforehand to compensate for component tolerances and environmental influences. This preliminary calibration eliminates the need for complex real-time calibration during production, resolving the contradiction between measurement accuracy and calibration complexity.

The patent implements feedback through a neural network model that is trained on comprehensive calibration data and then used to compensate for deviations in individual sensors. The model takes into account the specific characteristics of each sensor (such as membrane thickness and electrical resistance) and adjusts the measurement results accordingly. This feedback mechanism allows for high measurement accuracy without requiring complex calibration procedures during production.

The patent performs all necessary measurements at multiple heating voltages and temperatures in advance during the calibration phase. The comprehensive dataset collected at different heating voltages (including additional voltages beyond the normal operating range) is used to train a neural network model beforehand. This preliminary action ensures that when the sensor is used in production, no additional measurement time is required, as the model has already learned the relationships from the comprehensive calibration data.

The patent utilizes parameter changes by varying the heating voltage over a wide range during calibration (including voltages above and below the normal operating voltage) to capture the full behavior of the sensor. These parameter variations are used to train the neural network model, which then can accurately predict hydrogen concentration at the normal operating voltage without requiring additional measurements during production, thus reducing calibration time while maintaining high precision.

The patent implements feedback by using a neural network model that is trained on calibration data from multiple sensors with different component tolerances (membrane thickness, electrical resistance). The model learns to compensate for these individual variations and provides reliable measurements for each specific sensor. This feedback approach allows for high measurement reliability without requiring complex manual calibration procedures during manufacturing, as the model automatically adapts to each sensor's characteristics.

The patent applies self-service by enabling each sensor to compensate for its own component tolerances through the pre-trained neural network model. During production, the sensor's specific characteristics (such as membrane thickness and electrical resistance) are measured and fed into the model, which then automatically adjusts the measurement results. This self-service approach eliminates the need for complex external calibration procedures, simplifying manufacturing while maintaining high reliability.