Experimental Study on Temperature Compensation for Dual-Axis MEMS Accelerometers Using Adaptive Mode Decomposition...

Area of Science:

• Sensor Technology
• Instrumentation
• Signal Processing

Background:

• Micro-Electro-Mechanical System (MEMS) accelerometers are crucial for motion sensing.
• Temperature variations cause significant bias drift, degrading accelerometer accuracy.
• Existing compensation methods often struggle with complex temperature dependencies.

Purpose of the Study:

• To develop a novel temperature compensation approach for dual-axis MEMS accelerometers.
• To mitigate temperature-induced bias drift and improve sensor performance.
• To enhance the reliability and accuracy of MEMS accelerometers in diverse environments.

Main Methods:

• Integration of Adaptive Mode Decomposition (AMD) for signal analysis.
• Application of Grey Wolf Optimization (GWO) for parameter tuning.
• Utilization of a Hybrid Convolutional-Recurrent Temporal Network (HCR-TN) for compensation modeling.
• Experimental validation across a wide temperature range (-40 °C to +60 °C).

Main Results:

• Significant improvements in bias stability were achieved.
• The proposed compensation method effectively reduced temperature-induced drift on both axes.
• The algorithm demonstrated robustness against noise and mechanical disturbances in practical tests.

Conclusions:

• The novel AMD-GWO-HCR-TN approach offers a powerful solution for MEMS accelerometer temperature compensation.
• This method enhances sensor accuracy and reliability for real-world applications.
• The findings pave the way for more dependable inertial sensing systems.