# Blade Crack Diagnosis Based on Blade Tip Timing and Convolution Neural Networks
## 4 Conclusions and Forecast  
A CNN-based blade crack detection and analysis method is proposed to improve the low accuracy of blade fault diagnosis in rotating turbine blade vibration. Fault signal  
Figure 15: Detection results of crack location and depth of 1500 rpm blade.  
features can be automatically extracted and learned, and the high workload and uncertainty of traditional manual extraction are eliminated. Secondly, the numerical analysis of blade crack vibration measurement based on BTT is carried out. The numerical model of the sensor output voltage signal is established, and the effectiveness of the CNN fault diagnosis model in predicting the blade crack location and extent under the conditions of simulating the actual measurement deviation and noise is verified. The results show that the proposed CNN model can be applied to the complex problem of crack fault detection in untuned turbine blades. Finally, the vibration measurement of the rotating blade crack faults based on BTT is studied, and the results verify the effectiveness of the proposed CNN fault diagnosis model for the intelligent diagnosis of blade crack location and depth. The summarized conclusions are as follows.  
1. A fault diagnosis method for blade cracks based on convolutional neural networks has been developed. This method detects blade crack faults by deeply mining the data features of the input signal, and this method is anti-interference and noise-resistant.
2. The accuracy of traditional machine learning models such as GP, KNN, RFs, SVMs, and XGBoost for cracked blades is much lower than that of the CNN model proposed in this paper. Moreover, traditional machine learning models perform poorly in deviation conditions and are difficult to adapt to the actual complex testing environment.
3. Based on the proposed CNN crack detection model, a crack detection analysis for the rotating blade tip timing numerical model with cracks is constructed. The results show that the crack fault can be effectively identified by this method considering system deviation and signal noise. For the given four cases, the prediction accuracy of crack location is 92.9%, 87.7%, 90.8%, and 85.8%, respectively, while the prediction accuracy of crack depth is 95.4%, 90.7%, 94.6%, and 87.5%.
4. The effectiveness of the CNN fault diagnosis analysis model has been verified by rotating blade crack detection test BTT data. The method can effectively detect the crack location and depth condition. The detection accuracy of the crack location is 91.6%, and the detection accuracy of the crack depth is 88.2%, which has a higher diagnosis level compared to the other machine learning models.  
Vibration stress is an important parameter for the reliability of turbine blades. In subsequent research, the research on vibration stress detection of rotating blades based on the blade tip timing system will be developed to realize the inversion of the vibration stress of actual cracked turbine blades and to establish a blade safety monitoring method based on dynamic stress.  
Conceptualization, G.Z. and C.W.; methodology, G.Z. and C.W.; software, D.G.; validation, G.Z., W.Z. and D.G.; formal analysis, G.Z.; investigation, C.W.; re-sources, W.Z.; data curation, W.Z.; writing--original draft preparation, G.Z.; writing--review and editing, D.G. and C.W.; visualization, W.Z.; supervision, Y.X.; project administration, D.Z.; funding acquisition, Y.X. and D.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [Y.X.] grant number [National Science and Technology Major Project (J2019-IV-0022-0090)] And [D.Z.] grant number [111 project (B16038)].  
Not applicable.  
Data Availability Statement: Data is unavailable due to privacy.  
The authors gratefully acknowledge the financial supported by National Science and Technology Major Project (J2019-IV-0022-0090), and the 111 project (B16038), China.  
The authors declare no potential conflict of interest with respect to the research, authorship, and/or publication of this article.