An empirical study of fault diagnosis methods of a dissolved oxygen sensor based on ResNet-50

This work diagnoses online faults by classifying the real-time data collected by a dissolved oxygen sensor. Based on the three types of fault classification of dissolved oxygen parameters viz., complete failure faults, fixed deviation faults, and drifting faults, VGGNet, GoogLeNet, and ResNet-50 deep learning neural network models are used, respectively, to detect the faults. The experimental results show that the performance of the ResNet-50 model is the best, with a 98% dissolved oxygen failure accuracy rate. Whereas the accuracy rate of VGGNet and GoogLeNet can reach up to 0.9. The comparative experimental results of VGGNet and GoogLeNet show that the training loss function converges at 0.2. In contrast, the loss function of VGGNet and GoogLeNet after 150 rounds of training can only be reduced to about 0.3 and 0.5, respectively. The proposed model (ResNet-50) has good accuracy and reasonable generalisation ability.