机电产品故障诊断和退化预测一体化便携式终端研究OA

To address the urgent need for a multifunctional portable terminal used in health monitoring of spacecraft electromechanical products,a 2.5 kg integrated hardware-software device for fault diagnosis and degradation prediction was developed.The hardware features a 16-channel data acquisition box with a maximum sampling rate of 156 kHz,capable of interfacing with multiple types of sensors,including vibration,temperature,flow rate,rotational speed,and torque.The embedded software integrates four individual neural network models-BPNN,PSO-BPNN,GA-BPNN,and FNN-along with a multi-neural-network fusion(MNN)model.It also incorporates two degradation prediction algorithms(linear regression and Gaussian process regression)and a vibration analysis module,enabling a comprehensive workflow encompassing data acquisition,storage,diagnosis,and prediction.Six-fold cross-validation results showed that the MNN model significantly improved fault classification accuracy and model stability compared with those of single neural network models.For a spacecraft fluid loop pump with 36 samples under six-fold cross-validation,all test set diagnoses were correct,yielding a mean squared error(MSE)of only 0.010 3.Fatigue spalling faults on the pump bearing raceway,along with inner-and outer-ring spalling faults of the B7004C bearing,were accurately identified by the device.This research provides reliable modeling algorithms and integrated software-hardware support for health management of electromechanical products.