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<p>In this paper, the useful life prediction of traveling wave ultrasonic motors is taken as the research object to explore the estimation method of motor life and analyze the main mechanisms affecting the life of motors. The degradation pattern of motor performance is stochastic and nonlinear, and two modes of remaining useful life prediction models were established based on Wiener process theory. The parameters of the model were estimated based on Maximum Likelihood Estimation and multidimensional search optimization method. The performance of each degradation model was evaluated by introducing Akai Information Criterion and error evaluation method. Through the construction of an ultrasonic motor life test system and the life test, experimental data that can reflect the degradation of motor performance were obtained. The feasibility of the proposed method was verified using simulated signals and experimental datasets of motors. Compared with the existing methods of remaining useful life prediction, the modeling approach proposed has its uniqueness and superiority.</p>

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Remaining useful life prediction of ultrasonic motor based on nonlinear stochastic degradation process

  • 期刊论文

  • 发表日期：

    2025年4月12日

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  • Yuepeng Gao,
  • Zhiyuan Yao,
  • Lin Yang

摘要

In this paper, the useful life prediction of traveling wave ultrasonic motors is taken as the research object to explore the estimation method of motor life and analyze the main mechanisms affecting the life of motors. The degradation pattern of motor performance is stochastic and nonlinear, and two modes of remaining useful life prediction models were established based on Wiener process theory. The parameters of the model were estimated based on Maximum Likelihood Estimation and multidimensional search optimization method. The performance of each degradation model was evaluated by introducing Akai Information Criterion and error evaluation method. Through the construction of an ultrasonic motor life test system and the life test, experimental data that can reflect the degradation of motor performance were obtained. The feasibility of the proposed method was verified using simulated signals and experimental datasets of motors. Compared with the existing methods of remaining useful life prediction, the modeling approach proposed has its uniqueness and superiority.