The acoustic pressure signal generated by blades is one of the key indicators for condition monitoring and fault diagnosis in the field of turbines. Generally, the working conditions of the turbine are harsh, resulting in a large amount of interference and noise in the measured acoustic pressure signal. Therefore, denoising the acoustic pressure signal is the basis of the subsequent research. In this paper, a denoising method of micro-turbine acoustic pressure signal based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variable step-size Normalized Least Mean Square (VSS-NLMS) algorithms is proposed. Firstly, the CEEMDAN algorithm is used to decompose the original signal into multiple intrinsic mode functions (IMFs), based on the cross-correlation coefficient and continuous mean square error (CMSE) criterion; the obtained IMFs are divided into clear IMFs, noise-dominated IMFs, and noise IMFs. Finally, the improved VSS-NLMS algorithm is adopted to denoise the noise-dominated IMFs and combined with the clear IMF for reconstruction to obtain the final denoised signal. Adopting the above principles, the acoustic pressure signals generated by a micro-turbine with different rotation speeds and different states (normal turbine and fractured turbine) are denoised, respectively, and the results are compared with the axial flow fan test (ideal interference-free signal). The results show that the denoising method proposed in this paper has a good denoising effect, and the denoised signal is smooth and the important features are well preserved, which is conducive to the extraction of acoustic pressure signal characteristics.

Integrated bearings, characterized by their unique structure, feature an inner ring that is seamlessly integrated with the shaft. This study is based on the theoretical framework of rolling bearing dynamics and considers bearing friction, lubrication, and Hertz elastic contact theory. A dynamic simulation model considering the interaction between the components of the rolling bearing is established. Additionally, a subroutine for calculating the interaction forces between the bearing components was written in C and compiled into a dynamic link library, which was then integrated with the dynamic simulation software. To solve and simulate the dynamics of the integrated bearing model, a sophisticated combination of a refined integration method and the predictor-corrector Adams–Bashforth–Moulton multistep technique was employed. The theoretical analysis offers insights into the vibration characteristics of the integrated bearings across different structural and operational parameters. Results indicate that a judicious selection of parameters, such as the curvature radius ratio of the inner and outer grooves and the gap of the cage pockets, can significantly enhance the bearings’ vibration and noise reduction capabilities. Furthermore, the application of an appropriate axial preload effectively reduces bearing vibrations, and there exists an optimal range of rotational speeds that minimizes these vibrations.

To address the challenges in understanding the raceway failure mechanisms of bearings under dynamic radial excitations, this study proposes a vibration evolution analysis method based on multi-source data fusion and a Granger causality test. Firstly, a vertical bearing vibration test bench that can simulate the dynamic excitation in engineering practice is built, and the bearing acceleration, inner ring displacement and cage data are collected at the same time. Subsequently, the evolution law and correlation relationship of bearing vibration signals during the expansion process of bearing raceway damage were studied. Based on this, a multi-source vibration data fusion method was proposed, and the effectiveness of different data fusion schemes in characterizing raceway damage expansion was compared. Finally, the Granger causality test was applied to analyze the causal relationship between the evolution of various vibration behaviors during the damage propagation process. Research results demonstrate that under complex loading conditions during sustained operation, the “False Brinelling” indentation gradually develops into raceway surface damage. The vibration behavior of bearings exhibits distinct stage-specific characteristics under dynamic radial excitations. Notably, variations in vibration behavior amplitude and transition timing between different operational phases demonstrate significant discrepancies. Significant alterations in causal relationships between vibration behaviors were observed throughout different degradation phases. The combined approach proposed in this paper, encompassing complex load simulation, multi-source data fusion, and causal analysis, offers a new understanding of the raceway failure mechanism of bearings under real-world operating conditions.

In view of the difficulty in identifying the state of the micro turbine blade, this paper uses the order spectral entropy analysis method to extract the characteristic information of the blade fault based on the measured micro turbine bearing vibration data. Firstly, a micro-turbine test bench was established. The normal temperature stable inlet flow was produced by a blower, the high temperature and pressure unstable airflow was generated by turbojet combustion chamber, and the working inlet mode of a micro-turbine with a wide range of speed changes was simulated. The vibration signal of the bearing was collected by shell drilling, a variety of time-frequency domain feature analysis methods based on vibration signal are difficult to effectively identify blade faults under the combined action of unstable airflow and frequent variable speed. In this paper, the bearing vibration data in time domain is converted to the vibration data in angle domain, and then the order amplitude and entropy were compared and analyzed. The results show that the proposed method can effectively identify the blade fracture and fouling faults under the driving of stable and unstable airflow in the speed range of 0–20 000 r min⁻¹. This method provides a new method for micro turbine blade condition monitoring through bearing vibration data.