装备制造行业正朝着高效率、高精度方向发展，高端制造装备的功能和性能在很大程度上取决于高精密转轴等核心旋转部件。由于轴系零件装配精度、制造工艺和工作环境等因素的影响，导致加工出的转轴会存在一定的圆度误差，这会引起转轴在运转过程中产生振动。因此，圆度误差的在线检测与提取是监测转轴工作状态、评估转轴运行性能的重要步骤，对于转轴圆度误差的定性分析与定量评估成为近些年国内外研究的焦点。本文研究了转轴圆度误差在线提取与评估方法，主要内容如下：

首先，针对实验中采集的信号存在噪声干扰的问题，研究了时频域滤波方法，分别在时域采用二阶高斯回归滤波器、在频域内通过阶跃滤波器去除信号噪声，并通过仿真验证了时、频域滤波方法的有效性，此外，结合对同一信号的时频域处理，给出了信号时频域滤波的对应关系。

其次，分析了影响三点法误差分离精度的因素，针对振动信号存在异常值的问题，提出了采用鲁棒高斯回归滤波的传感器信号处理方法，实现了信号异常值的剔除；针对传感器噪声干扰问题，提出了基于谐波抑制的传感器安装角度优化方法，实现了误差分离过程中的噪声抑制。仿真与实验结果表明：所提出的方法可有效提升误差分离精度，此外，运用蒙特卡罗方法对误差分离过程的不确定性进行了定量计算。

最后，研究了圆度误差的在线评估方法，分别采用圆度与圆柱度评估方法对转轴单一截面与多截面圆度误差进行分析，提出了基于自然选择的混合粒子群算法的圆柱误差优化评估方法，并通过实验对比验证了本文优化算法的准确性。

本文研究了转轴振动信号时频域信号滤波方法，提出了可处理异常值以及抑制噪声的误差分离精度提升技术，并计算了分离过程中的不确定性，实现了圆柱度误差优化评估，为保障转轴圆度误差的高精度在线检测与评估提供支撑。

关 键 词：精密转轴，在线提取，误差分离，测量不确定度，自然选择的粒子群算法

The equipment manufacturing industry is developing in the direction of high efficiency and high precision, and the function and performance of high-end manufacturing equipment depend to a large extent on core rotating components such as high-precision rotors. Due to the influence of the assembly accuracy, manufacturing process and working environment of the rotor parts, there will be a certain roundness error of the machined rotor, which will cause vibration of the rotor during operation. Therefore, on-line detection and extraction of roundness error is an important step to monitor the working state and evaluate the operating performance of the rotor. Qualitative analysis and quantitative evaluation of the rotor roundness error have become the focus of research at home and abroad in recent years. This thesis studies the on-line extraction and evaluation method of the rotor roundness error, the main contents are as follows:

Firstly, in view of the noise interference in the signal collected in the experiment, the time-frequency domain filtering method is studied, and the second-order Gaussian regression filter is used in the time domain and the step filter is used in the frequency domain to remove the signal noise, and the effectiveness of the time-frequency domain filtering method is verified through simulation. In addition, combined with the time-frequency domain processing of the same signal, The correspondence of signal filtering in time-frequency domain is given.

Secondly, the factors affecting the error separation accuracy of the three-point method are analyzed. Aiming at the problem of abnormal values in vibration signals, a signal processing method using robust Gaussian regression filter is proposed to eliminate the abnormal values. In order to solve the problem of sensor noise interference, an optimization method of sensor installation Angle based on harmonic suppression is proposed to achieve noise suppression in the process of error separation. Simulation and experimental results show that the proposed method can effectively improve the accuracy of error separation. In addition, Monte Carlo method is used to quantitatively calculate the uncertainty of error separation process.

Finally, the online evaluation method of roundness error is studied, and the roundness and cylindricity evaluation methods are respectively used to analyze the roundness error of single section and multi-section of the rotor. The cylinder error optimization evaluation method based on nature selection particle swarm optimization is proposed, and the accuracy of the optimization algorithm in this thesis is verified by experimental comparison.

In this thesis, the time-frequency signal filtering method of rotor vibration signal is studied, and the error separation accuracy improvement technology which can deal with outliers and suppress noise is proposed. The uncertainty in the separation process is calculated, and the cylindricity error optimization evaluation is realized, which provides support for ensuring the high-precision online detection and evaluation of rotor roundness error.

Keywords: Precision rotor, On-line extraction, Error separation, Measurement uncertainty, NSPSO

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