## 3 Experiments
### Fault Detection and Anti-Noise Analysis
At this time, the model reconstruction is affected by the data amplitude, which causes the reconstructed data value of the model to fluctuate widely around the original data value. The amplitude of the fault data for the noise fault and the short-term fault both exceeds 10 \({}^{\circ}\)C, but the  
Figure 10: The data reconstruction results of the model for different data faults. (**a**) The original data without fault, (**b**) The original data with noise fault and the ratio of fault data is 50%, (**c**) The original data with short-term fault and the ratio of fault data is 50%, (**d**) The original data with fixed fault and the ratio of fault data is 5%. amplitude of the fault data in the short-term fault is more severe than the noise fault, so the model has better detection effect on the noise fault than the short-term fault. For the noise fault with the ratio of fault data is 50%, the \(F_{1}\)_score_ value of the model is 73.40%, and the average reconstruction error is 1.6577 \({}^{\circ}\)C. For the short-term fault with the ratio of fault data is 50%, the \(F_{1}\)_score_ value of the model is 71.99%, and the average reconstruction error is 2.9828 \({}^{\circ}\)C. For fixed faults, as the ratio of fault data increases, the detection ability of the model shows a slow downward trend, but it does not decrease too much. For fixed faults with different ratios, the difference between the _precision_ value and the _recall_ value of the model is small, and there is no high _recall_ value and low _precision_ value phenomenon similar to the noise fault and the short-term fault. The optimal \(F_{1}\)_score_ value of the model is close to the optimal \(F_{1}\)_score_ value of the noise fault and the short-term fault, which indicates that the model can maintain the detection ability of the \(F_{1}\)_score_ value exceeding 70% for different faults. Figure 10d shows the reconstruction results of the model for fixed fault with the ratio of fault data is 5%. The fixed fault is the change of original sample to the data of other nodes. The test model is trained by the original data of Node 7, and the model has good transfer learning ability. For other nodes, the hidden mathematical features are similar to those of Node 7, so the model does not recognize the added other node data very well. Therefore, the ratio of fault data is 5% when the model achieves the best detection ability. We can be seen from Figure 10d, as the ratio of fixed fault data increases, the trend of sample data will approach the data of other nodes. Therefore, as the ratio of fault data increases, the detection ability of the model shows a slow downward trend, but because of the transfer learning ability of the model, the detection ability is not much reduced. When the ratio of fixed fault data is small, these fixed faults are noise compared to the original sample, so the model can identify these fixed faults well. For the fixed fault with the ratio of fault data is 5%, the \(F_{1}\)_score_ value of the model is 73.08%, and the average reconstruction error is 0.0861 \({}^{\circ}\)C. The experimental results show that the proposed model has good fault detection ability and anti-noise ability. Even if the fault data is inevitably mixed when collecting data, most of the fault data will be avoided when the model is reconstructed.