基于光纤光栅传感器的精冲机换向阀阀芯卡滞间接故障诊断OA

As high-end manufacturing equipment,the fine blanking press was directly affected by the health of its hydraulic valves in terms of service life and machining accuracy.Current research on diagnos-ing spool sticking faults has primarily relied on manually constructed testing platforms,lacking validation in industrial environments.Therefore,a fault diagnosis method was proposed for spool sticking in the rapid approach system directional control valve of the fine blanking press,based on FBG sensor and 1D convolu-tional neural network(1DCNN).Using the HFB-500 fine blanking press as the test subject,AMESim simulations were conducted to investigate the impact of spool sticking on the slide and valve,thereby deter-mining the measurement points and data acquisition methods for the FBG sensor.Subsequently,experi-mental data were processed through Savitzky-Golay(SG)filtering and baseline drift removal.The results demonstrate that the FBG sensor may accurately capture the characteristic increase in rapid approach time during spool sticking faults.Finally,a classification accuracy of 99.9%was achieved by 1DCNN for spool sticking faults.The superior performance of 1DCNN was verified through comparative analyses with sup-port vector machine(SVM)and long short-term memory(LSTM)models,demonstrating its capability to accurately identify spool sticking faults.