Fatigue failure is the main type of failure that occurs in gas turbine engine blades and an online monitoring method for detecting fatigue cracks in blades is urgently needed. Therefore, in this present study, we propose the use of acoustic emission (AE) monitoring for the online identification of the blade status. Experiments on fatigue crack propagation based on the AE monitoring of gas turbine engine blades and TC11 titanium alloy plates were conducted. The relationship between the cumulative AE hits and the fatigue crack length was established, before a method of using the AE parameters to determine the crack propagation stage was proposed. A method for predicting the degree of crack propagation and residual fatigue life based on the AE energy was obtained. The results provide a new method for the online monitoring of cracks in the gas turbine engine blade.

The acoustic emission (AE) signals of metal materials have been widely used to identify the deformation stage of a pressure vessel. In this work, Q235 steel samples with different propagation distances and geometrical structures are stretched to get the corresponding acoustic emission signals. Then the obtained acoustic emission signals are de-noised by empirical mode decomposition (EMD), and then decomposed into two different frequency ranges, i.e., one mainly corresponding to metal deformation and the other mainly corresponding to friction signals. The ratio of signal energy between two frequency ranges is defined as a new acoustic emission characteristic parameter. Differences can be observed at different deformation stages in both magnitude and data distribution range. Compared with other acoustic emission parameters, the proposed parameter is valid in different setups of the propagation medium and the coupled stiffness.

The weak fault acoustic emission (AE) signals collected in the actual operating conditions of the engine are often submerged in the strong background noise. This paper proposes a denoising method of AE signals based on the combination of autoencoder and wavelet packet decomposition (AE-WPD) to address the above problem. Firstly, the wavelet packet is used to decompose engine background noise signals and noise-containing fault AE signals to enhance the local analysis capability of the autoencoder. Then, the dataset of each frequency band after decomposition is created. Among them, background noise signals are regarded as normal datasets. Noise-containing fault signals are treated as outlier datasets. The difference between each frequency band of background noise signals and noise-containing fault signals is analyzed. The autoencoder model is trained, validated and tested for effectiveness. In addition, a comparison is made with other commonly used denoising methods. Four types of evaluation indexes are introduced to quantitatively assess various methods. Finally, the real engine background noise signals with different signal-to-noise ratio (SNR) are added to the fault AE signals to verify the robustness of the proposed AE-WPD method. The experimental results show that the proposed AE-WPD method outperforms other denoising methods at different SNR. This lays the foundation for engine structural condition monitoring and subsequent fault identification and localization.

The structure of a high-performance engine is becoming more and more complex, so it is very important to accurately and quickly locate the faults to ensure its operation safety. The acoustic emission (AE) signal which is caused by the structural damage of engines contains important structural integrity information; however, the accuracy of existing AE location methods is affected by complex structures, such as stiffeners, holes, variable wall thickness, and interface coupling. Therefore, this paper proposes a new AE source location method that combines the two-step Akaike information criterion (AIC) based on the dispersion curve and the time difference matrix (TDM). This method can precisely locate the faults of complex structures without considering the wave velocity. Through theoretical calculation and numerical simulation, this paper intends to show that the proposed method is superior to the traditional AIC and can track the arrival time of the AE signal more accurately. In addition, experiments on different structures illustrate that the proposed method has higher location accuracy in complex structures. This paper also analyzes the location sensitivity to the number and array of sensors in a complex structure and puts forward an optimal scheme of sensor layout on the condition of high location accuracy. The results show that the proposed method can be used as a reliable tool for AE source location and fault monitoring of complex structures. It will have a wide application prospect in the engine and other fields.

The mass transfer performance is unclear to limit the development of organic flow battery, which is regarded as the emerging electrochemical energy storage technology. The mass transfer behaviors in the electrode are influenced by the flow fields, which is indispensable. A three-dimensional and steady numerical model of the organic flow battery is established and the results are verified by the experiments data. The battery performance and mass transfer behaviors are analyzed under different flow field for the charge/discharge processes based on this model. Compared with other flow fields, the interdigital flow field can achieve the best charge-discharge performance, which is mainly due to the improvement distribution uniformity of active species. The average concentration of the interdigital flow field is 45.1% higher that of the conventional flow field. The effects of the flow rates and initial concentration of electrolyte on the battery performance are investigated, and the results indicate that appropriate inlet flow rate can lead to the highest net discharge powers and power-based efficiency of the flow battery. Although the increasing of the initial concentration can improve the battery performance, the improvement is too slight when the concentration is high enough, which can lead to the waste of active species.

In view of the complexity of the engine mechanical structure and the diversity of faults, this paper presents a one-dimensional convolutional neural network (1DCNN)-vision transformer (ViT) ensemble model for identifying engine faults based on acoustic emission (AE) signals. The 1DCNN-ViT ensemble model combines 1DCNN and ViT. Firstly, AE signals of various faults are collected on the engine fault test rig. The dataset is constructed from its High-Mel Filterbank feature, which applies to AE signals. The proposed model has advantageous performance on this dataset. Secondly, the proposed model has a higher test accuracy than other new models. Finally, the fault data with different signal-to-noise ratios are input into the trained models, and the proposed model has better anti-noise performance. Overall, the proposed method can more accurately identify the AE signals of engine faults. It can be used as an effective method to diagnose engine faults.