School of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Institute of Aerospace Machinery and Dynamics, Southeast University, Nanjing 211189, China.
Sensors (Basel). 2022 Nov 22;22(23):9043. doi: 10.3390/s22239043.
Signal processing is important in the balancing of the motor armature, where the balancing accuracy depends on the extraction of the signal amplitude and phase from the raw vibration signal. In this study, a motor armature dynamic balancing method based on the long short-term memory network (LSTM) and zero-phase filter (ZPF) is proposed. This method mainly focuses on the extraction accuracy of amplitude and phase from unbalanced signals of the motor armature. The ZPF is used to accurately extract the phase, while the LSTM network is trained to extract the amplitude. The proposed method combines the advantages of both methods, whereby the problems of phase shift and amplitude loss when used alone are solved, and the motor armature unbalance signal is accurately obtained. The unbalanced mass and phase are calculated using the influence coefficient method. The effectiveness of the proposed method is proven using the simulated motor armature vibration signal, and an experimental investigation is undertaken to verify the dynamic balancing method. Two amplitude evaluation metrics and three phase evaluation metrics are proposed to judge the extraction accuracy of the amplitude and phase, whereas amplitude and frequency spectrum analysis are used to judge the dynamic balancing results. The results illustrate that the proposed method has higher dynamic balancing accuracy. Moreover, it has better extraction accuracy for the amplitude and phase of unbalanced signals compared with other methods, and it has good anti-noise performance. The determination coefficient of the amplitude is 0.9999, and the average absolute error of the phase is 2.4°. The proposed method considers both fidelity and denoising, which ensuring the accuracy of armature dynamic balancing.
信号处理在电机电枢的平衡中非常重要，平衡精度取决于从原始振动信号中提取信号幅度和相位的准确性。本研究提出了一种基于长短时记忆网络（LSTM）和零相位滤波器（ZPF）的电机电枢动平衡方法。该方法主要侧重于从电机电枢不平衡信号中提取幅度和相位的准确性。ZPF 用于准确提取相位，而 LSTM 网络则用于提取幅度。该方法结合了两种方法的优点，解决了单独使用时相位偏移和幅度损失的问题，准确地获得了电机电枢的不平衡信号。不平衡质量和相位使用影响系数法计算。通过模拟电机电枢振动信号验证了所提出方法的有效性，并进行了实验研究以验证动态平衡方法。提出了两种幅度评估指标和三种相位评估指标来判断幅度和相位的提取精度，同时使用幅度和频谱分析来判断动态平衡结果。结果表明，所提出的方法具有更高的动态平衡精度。此外，与其他方法相比，它对不平衡信号的幅度和相位具有更好的提取精度，并且具有良好的抗噪声性能。幅度的决定系数为 0.9999，相位的平均绝对误差为 2.4°。所提出的方法同时考虑了保真度和去噪，保证了电枢动平衡的准确性。
