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A novel approach for predicting tool remaining useful life using limited data

机译：一种新的方法，用于使用有限数据预测剩余使用寿命的工具

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摘要

Wear, fracture, and other tool faults affect the quality of a machined workpiece and can even damage machine tools. The accurate prediction of remaining useful life (RUL) can prevent a tool from suddenly failing, an ability of significance for ensuring machining quality and providing effective predictive maintenance strategies. Most current approaches for predicting tool RUL are based on historical failure and truncation data. However, for new types of tools or when a similar tool has just launched, such failure and truncation data are limited or even unavailable, making RUL prediction a challenge when using previously proposed methods. To address this problem, a novel method for the prediction of tool RUL using limited data is proposed in this study. A time window is constructed to track the tool condition using sensor data, and its size can be dynamically adjusted according to the wear factor and increase rate. Then, a deep bidirectional long short-term memory (DBiLSTM) neural network in which sequential data are predicted and smoothed by forwards and backwards directions, respectively, is developed to encode temporal information and identify long-term dependencies. On this basis, multi-step ahead rolling predictions are then employed to predict tool RUL. Finally, the effectiveness of the proposed method is verified using the results of milling experiments. These results show that the proposed method is able to predict tool RUL with high accuracy using only limited data.
机译：磨损，骨折和其他工具故障影响加工工件的质量，甚至可以损坏机床。剩余使用寿命（RUL）的精确预测可以防止工具突然失败，这是一种能够确保加工质量和提供有效的预测性维护策略的能力。最新的预测工具RUL方法基于历史失败和截断数据。但是，对于新类型的工具或刚刚启动类似的工具时，这种故障和截断数据受到限制甚至不可用，使RUL预测使用先前提出的方法时的挑战。为了解决这个问题，在本研究中提出了一种使用有限数据预测工具rul的新方法。构造时间窗口以跟踪使用传感器数据跟踪刀具状况，并且其尺寸可以根据磨损因子动态调整并增加速率。然后，开发了通过前向和向后方向预测和平滑顺序数据的深度双向长期短期存储器（DBILSTM）神经网络以编码时间信息并识别长期依赖性。在此基础上，然后采用多步前进的滚动预测来预测工具rul。最后，使用铣削实验的结果来验证所提出的方法的有效性。这些结果表明，该方法能够使用仅使用有限的数据来预测高精度的工具rul。

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  • 来源
    《Mechanical systems and signal processing》 |2020年第9期|106832.1-106832.22|共22页
  • 作者

    Hai Li; Wei Wang; Ziwei Li; Liyi Dong; Qingzhao Li;

  • 作者单位

    School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu 611731 China;

    School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu 611731 China;

    School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu 611731 China;

    School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu 611731 China;

    School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu 611731 China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Tool; Remaining useful life prediction; Limited data; Adaptive time window; Deep bidirectional long-short term memory;

    机译：工具;剩下的使用寿命预测;有限的数据;自适应时间窗口;深度双向长期记忆;

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