The vibration and strain signal data is available at https://pan.baidu.com/s/1mwfuhPojCifviLOBzpS0IQ?pwd=up8l
This study introduces an innovative condition monitoring test rig for the differential planetary roller screws (DPRS), focusing on their performance in extreme conditions. It underscores the importance of anti-jamming and real-time monitoring for the DPRS reliability. The research presents a dynamic friction model using the Lagrange method, enhancing understanding of DPRS operations. Advanced signal processing techniques, including discrete wavelet transforms and a convolutional neural network, are implemented for effective feature extraction. A DWTC-BiGRU network is utilized to capture temporal dependencies, vital for monitoring DPRS under varying conditions. Experimental validation is conducted under thermal stress and load variations, demonstrating the system's durability and reliability. The study compares its method with existing algorithms, showing superior accuracy and robustness by combining mechanical modeling with computational techniques for real-time industrial monitoring.
Please cite "Experimental Investigation of the Differential Planetary Roller Screw Mechanism Intelligent Condition Monitoring and Public Dataset" doi: http://dx.doi.org/10.2139/ssrn.4863227