在航空发动机可视化协同维修中,利用声音提示技术,通过改进的梅尔频率倒谱系数(Mel Frequency Cepstral Coefficients,MFCC)与头相关传递函数(Head-Related Transfer Function,HRTF)构建“频域-空间”双重映射模型,实现故障声纹特征的...在航空发动机可视化协同维修中,利用声音提示技术,通过改进的梅尔频率倒谱系数(Mel Frequency Cepstral Coefficients,MFCC)与头相关传递函数(Head-Related Transfer Function,HRTF)构建“频域-空间”双重映射模型,实现故障声纹特征的精准提取与三维声场定位。结合增强现实界面,设计多故障优先级动态调控机制,优化声学信息传递链。实验结果表明,将声音提示技术应用于航空发动机可视化协同维修,故障维修响应时间缩短至1.3 s以内,故障定位误差控制在±1.0 mm和±1.0°,同时显著减少了协同误判频次,提升了航空发动机维修的实时性与准确性。展开更多
Anti-aliasing spectrum analysis is essential for rotor blade condition monitoring based on Blade Tip Timing(BTT).The Multiple Signal Classification(MUSIC)algorithm,which exploits the orthogonality between signal and n...Anti-aliasing spectrum analysis is essential for rotor blade condition monitoring based on Blade Tip Timing(BTT).The Multiple Signal Classification(MUSIC)algorithm,which exploits the orthogonality between signal and noise subspaces,has been successfully applied for this purpose.However,conventional subspace selection methods relying on fixed thresholds are sensitive to variations in large eigenvalues.Furthermore,the complex disturbances during rotor operation and measurement complicate the identification of blade vibration characteristics.To overcome these challenges,this paper proposes Adaptive Subspace Separation(ASS)and Local Spectral Centroid(LSC)methods to improve the adaptability of subspace selection and the stability of frequency identification,respectively.The impacts of overestimating and underestimating the subspace dimensions on MUSIC's performance are derived mathematically.Simulation and experiments demonstrate the effectiveness of proposed approaches:ASS offers more accurate and stable subspace dimension selection and tracking,while LSC reduces the standard deviation of estimated frequencies by 30 percent.展开更多
The machining precision of blades is critical to the service performance of aero engines.The Leading Edge(LE) of high-pressure compressor blades poses a challenge for precision machining due to its thin size, high deg...The machining precision of blades is critical to the service performance of aero engines.The Leading Edge(LE) of high-pressure compressor blades poses a challenge for precision machining due to its thin size, high degree of bending, and significant change of curvature. Aimed at optimizing the machining error, this paper presents a framework that integrates toolpath planning and process parameter regulation. Firstly, an Iterative Subdivision Algorithm(ISA) for clamped Bspline curve is proposed, based on which toolpath planning method towards the LE is developed.Secondly, the removal effect of Cutter Contact(CC) point on the sampling points is investigated in the calculation of grinding dwell time by traversing in u-v space. A global material removal model is constructed for the solution. Thirdly, the previous two steps are interconnected based on the Improved Whale Optimization Algorithm(IWOA), and the optimal parameter combination is searched using the Root Mean Square Error(RMSE) of the machining error as the objective function. Based on this, the off-line programming and robotic grinding experiments are carried out. The experimental results show that the proposed method with error optimization can achieve 0.0143 mm mean value and 0.0160 mm standard deviations of LE surface error, which is an improvement of32.5% and 33.9%, respectively, compared with previous method.展开更多
文摘在航空发动机可视化协同维修中,利用声音提示技术,通过改进的梅尔频率倒谱系数(Mel Frequency Cepstral Coefficients,MFCC)与头相关传递函数(Head-Related Transfer Function,HRTF)构建“频域-空间”双重映射模型,实现故障声纹特征的精准提取与三维声场定位。结合增强现实界面,设计多故障优先级动态调控机制,优化声学信息传递链。实验结果表明,将声音提示技术应用于航空发动机可视化协同维修,故障维修响应时间缩短至1.3 s以内,故障定位误差控制在±1.0 mm和±1.0°,同时显著减少了协同误判频次,提升了航空发动机维修的实时性与准确性。
基金supported by the National Natural Science Foundation of China(Nos.52405088 and 92360306)the Postdoctoral Fellowship Program of CPSF,China(No.GZC20241446)+2 种基金the Natural Science Basic Research Program of Shaanxi,China(No.2024JC-YBMS-402)the Fundamental Research Funds for the Central Universities,CHD(No.300102254102)the Foundation of Beilin District,China(No.GX2455)。
文摘Anti-aliasing spectrum analysis is essential for rotor blade condition monitoring based on Blade Tip Timing(BTT).The Multiple Signal Classification(MUSIC)algorithm,which exploits the orthogonality between signal and noise subspaces,has been successfully applied for this purpose.However,conventional subspace selection methods relying on fixed thresholds are sensitive to variations in large eigenvalues.Furthermore,the complex disturbances during rotor operation and measurement complicate the identification of blade vibration characteristics.To overcome these challenges,this paper proposes Adaptive Subspace Separation(ASS)and Local Spectral Centroid(LSC)methods to improve the adaptability of subspace selection and the stability of frequency identification,respectively.The impacts of overestimating and underestimating the subspace dimensions on MUSIC's performance are derived mathematically.Simulation and experiments demonstrate the effectiveness of proposed approaches:ASS offers more accurate and stable subspace dimension selection and tracking,while LSC reduces the standard deviation of estimated frequencies by 30 percent.
基金supported by the National Natural Science Foundation of China (No. 52075059)Graduate Scientific Research and Innovation Foundation of Chongqing (No. CYB23021)the Innovation Fund of Aero Engine Corporation of China (No. ZZCX-2022-019)。
文摘The machining precision of blades is critical to the service performance of aero engines.The Leading Edge(LE) of high-pressure compressor blades poses a challenge for precision machining due to its thin size, high degree of bending, and significant change of curvature. Aimed at optimizing the machining error, this paper presents a framework that integrates toolpath planning and process parameter regulation. Firstly, an Iterative Subdivision Algorithm(ISA) for clamped Bspline curve is proposed, based on which toolpath planning method towards the LE is developed.Secondly, the removal effect of Cutter Contact(CC) point on the sampling points is investigated in the calculation of grinding dwell time by traversing in u-v space. A global material removal model is constructed for the solution. Thirdly, the previous two steps are interconnected based on the Improved Whale Optimization Algorithm(IWOA), and the optimal parameter combination is searched using the Root Mean Square Error(RMSE) of the machining error as the objective function. Based on this, the off-line programming and robotic grinding experiments are carried out. The experimental results show that the proposed method with error optimization can achieve 0.0143 mm mean value and 0.0160 mm standard deviations of LE surface error, which is an improvement of32.5% and 33.9%, respectively, compared with previous method.
