# Blade Crack Diagnosis Based on Blade Tip Timing and Convolution Neural Networks
## 1 Introduction
[14], established the rotating integral blade disc detuning identification and model real-time calibration (BR-IDC) method based on BTT and achieved the online real-time detuning identification of rotating blade discs through experiments. Research on the acquisition of vibration parameters based on BTT signals has also been in progress. Imperial College proposed a sensor waveform analysis method based on BTT and clearance sensor waveform analysis methods (BLASMA) that can be used to determine single and multi-harmonic blade vibration parameters for asynchronous and synchronous vibrations [15]. [PERSON] et al. [16] identified the blade arrival time using the rising and falling edges of the BTT signal and proposed a calibration procedure applicable to asymmetric BTT signals to improve the measurement accuracy. [PERSON] et al. [17] proposed a multimodal blade vibration reconstruction algorithm to reconstruct the unknown multimodal blade vibration signal and also proposed a minimum requirement for the number of probes. [PERSON] et al. [18] established an improved multi-signal classification (MUSIC) method to effectively identify the vibration frequency components in undersampled BTT signals. The difficulty in analyzing blade fault signals stems not only from the fact that the blade fault is unpredictable and unknown but also from the fact that the early fault signal created during operation is latent. Fault diagnosis methods based on deep learning can efficiently analyze vast volumes of data, automatically extract data feature values, and generate correct diagnosis findings rapidly. The convolutional neural network (CNN) is one of the most successful deep learning approaches, employing local connections and shared weights to extract information features from data, while avoiding overfitting issues [19; 20]. Research on blade fault diagnosis for undamped structures has gradually emerged recently. [PERSON] et al. [21] proposed a method based on an artificial neural network (ANN) model to identify the location and quantity of ice mass on wind turbine blades, and this method correctly predicted the ice mass distribution along the blades. [PERSON] et al. [22] applied machine learning to identify the impact of various blade failure circumstances on unit blades. [PERSON] et al. [23] applied computational fluid dynamics and finite element analysis to numerically simulate the damage detection of single cracked blades and proposed an effective method to predict crack location and size based on the variation the natural frequency of wind turbine blades. [PERSON] et al. [24] proposed a method for detecting blade faults by fusing tip clearance information with BTT data, which was validated by 16 sets of experiments. The results showed that the classification accuracy reached 95%, which was significantly higher than traditional diagnostic methods. [PERSON] et al. [25] used the k-means approach to classify faults in the BTT data of a titanium-bladed disk with cracks. It is clear from the review above that BTT has attracted a lot of attention and research because it is currently the most effective technical method for detecting blade vibration [10]. Additionally, with the development of big data and artificial intelligence, deep learning-based detection methods have been successfully applied to the prediction of equipment faults. Due to this, the purpose of this research is to implement and enhance more effectively the application of intelligent diagnosis methods in blade fault vibration detection to improve blade fault diagnosis, as well as to extend the application of BTT in the field of blade fault diagnosis by using deep learning. A fault diagnosis method for turbine blade cracks based on BTT and CNN is proposed in this research, which is validated in two ways: the numerical analysis of the intelligent diagnosis of rotating turbine blade cracks and the experimental examination of rotating blade crack fault diagnosis. The complete closed block diagram of the work is shown in Figure 1. The main contents mainly include:  
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