*4.3. Experimental Conclusion*

The experiment verifies the feasibility of the FFBRB model proposed in this paper, and it can be seen from the experimental results that the FFBRB model experiment is superior to the other two methods.

In particular, the BP neural network method is used to obtain the experimental diagnosis value and the real value of the image fitting, high accuracy, but there is still a little gap compared with the FFBRB method, and the BP method cannot explain its process. The experimental results obtained by the ELM method are much different from the real values, the image fitting effect of the experimental results is relatively poor, the accuracy is relatively low, and there is a big gap compared with the FFBRB scheme. The FFBRB fault diagnosis scheme in this paper is relatively optimal among the three, and its experimental results have a good image fitting effect and high accuracy, showing advantages compared with the other two schemes.
