CNN-LSTM-Based Fault Diagnosis and Adaptive Multichannel Fusion Calibration of Filament Current Sensor for Mass Spectrometer

The mass spectrometer filament current sensor reflects the health status of the filament by detecting real-time variation in filament current, providing an ideal means to monitor the safe use of the filament. However, the complex and changing environment inside and outside the sensor often affects the sensor, so it is important to perform fault diagnosis and calibration of the sensor. To address the problems of low fault identification accuracy and high calibration error of a single sensor due to insufficient fault samples, a convolutional neural network and long and short-term memory network (CNN-LSTM)-based fault diagnosis and adaptive multichannel fusion calibration of filament current sensors for mass spectrometers is proposed. First, two valid sensor fault datasets are generated by numerical simulation of on-orbit historical data collected by a spacecraft through the convolutional variational autoencoder (CVAE) model. Second, the fault identification of the dataset is performed by the CNN-LSTM model, and the designed weighted fusion loss function leads to the improved accuracy of the model. Finally, an adaptive multichannel calibration model is constructed, and a method for adaptively selecting calibration channels based on the characteristics of the fault samples themselves is proposed, which significantly reduces the calibration error compared with a single calibration model. The experimental results show that the method can improve the fault identification rate and reduce the calibration fault error compared with other fault diagnosis and calibration methods. It provides an effective sensor fault solution for mass spectrometer filament current sensors.