A Phase-Difference Method for Measuring Torque and Rotational Speed of Shafts Based on CNN-LSTM

Traditional torque measurement has greatly depended on the contact between sensors and the rotating shaft. However, the high-speed rotation of the shaft leads to significant sensor wear, compromising the stability of the measurement system. Additionally, the newly collected test data tend to be high in latency and noise, which introduces accuracy challenges for artificial intelligence algorithms in torque measurement. This article presents the measurement method, hardware platform design, data calibration, and software optimization of a torque and rotational speed measurement system, which uses a noninvasive method and accurately estimates torque and rotational speed under both low- and high-speed operating conditions by analyzing the mechanical phase difference between two pulse signals. The pulse signals are precisely captured by Hall sensors and a field-programmable gate array (FPGA) microcontroller circuit, while signal delay and noise are optimized through a convolutional neural network-long short-term memory (CNN-LSTM) artificial intelligence algorithm and system calibration programming. To verify the correctness of the proposed method, a test platform equipped with Hall sensors and servo motor and 200-N $\cdot $ m standard torsion device for measuring the torque and rotational speed under torque force generation device’s control was implemented. The effectiveness of the proposed method is demonstrated by achieving a measurement error rate within 1%, with the rotational speed error reduced from 3.15% to 0.86% and the torque error decreased to 0.96%.