工业机器人关节具有柔性，会产生较大的工作振动。针对机器人关节发生故障时，如何从测量得到的混合振动信号中分离出故障分量的问题，提出一种模型与数据混合驱动的关节振动分离方法。首先，建立多物理量信号和系统动力学相结合的执行器动力学响应模型，并以该响应信号作为振动分离时的参考。其次，考虑噪声影响构造了幅值谱百分位序列，利用变点分析确定最优噪声阈值，并设计带通滤波器分离噪声。针对测量和滤波带来的参考振动和混合振动间相位误差问题，提出可调节因子动态时间规整相位校正方法。最后，由去噪和相位校正后的混合振动减去参考振动实现故障分量分离。在机器人关节实验台上的实验结果表明，所提方法能从关节振动中有效地分离出故障分量。
Abstract
Owing to the inherent flexibility of the industrial robot joints, the machinery manifests heightened vibrational tendencies during operation. To address the challenge of isolating fault components within mixed vibration signals acquired during instances of robot joint malfunctions, a vibration separation method for robotic joints based on a mixed drive consisting of models and data is proposed. Initially, the actuator dynamics response model is constructed by amalgamating multi-physics signals with system dynamics. This response signal serves as the benchmark signal in the process of vibration separation. Subsequently, the amplitude spectral percentile sequence was developed. Variable point analysis is employed to ascertain the optimal noise threshold, complemented by the design of a band-pass filter for noise segregation. Additionally, efforts are made to eliminate phase errors arising from measurement and filtering between the reference vibration and mixed vibration signals, a method employing Adjustable Factor Dynamic Time Warping is presented. Ultimately, the separation of fault components is achieved by subtracting the reference vibration from the denoised and phase-corrected mixed vibration. Experimental findings obtained from a robotic joint test platform substantiate the efficacy of the proposed methodology in successfully isolating fault components from joint vibrations.
关键词
机器人关节故障 /
故障分离 /
可调节因子动态时间规整 /
噪声分离 /
数据生成
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Key words
Robot joint faults /
Fault separation /
Adjustable Factor Dynamic Time Warping /
Noise separation /
Data generation
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