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Prognostic modelling options for remaining useful life estimation by industry

机译：根据行业预测剩余使用寿命的预测建模选项

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摘要

Over recent years a significant amount of research has been undertaken to develop prognostic models that can be used to predict the remaining useful life of engineering assets. Implementations by industry have only had limited success. By design, models are subject to specific assumptions and approximations, some of which are mathematical, while others relate to practical implementation issues such as the amount of data required to validate and verify a proposed model. Therefore, appropriate model selection for successful practical implementation requires not only a mathematical understanding of each model type, but also an appreciation of how a particular business intends to utilise a model and its outputs. This paper discusses business issues that need to be considered when selecting an appropriate modelling approach for trial. It also presents classification tables and process flow diagrams to assist industry and research personnel select appropriate prognostic models for predicting the remaining useful life of engineering assets within their specific business environment. The paper then explores the strengths and weaknesses of the main prognostics model classes to establish what makes them better suited to certain applications than to others and summarises how each have been applied to engineering prognostics. Consequently, this paper should provide a starting point for young researchers first considering options for remaining useful life prediction. The models described in this paper are Knowledge-based (expert and fuzzy), Life expectancy (stochastic and statistical), Artificial Neural Networks, and Physical models.
机译：近年来，已经进行了大量研究以开发可用于预测工程资产的剩余使用寿命的预测模型。行业实施仅取得了有限的成功。通过设计，模型要遵循特定的假设和近似，其中一些假设是数学上的，而其他假设则涉及实际的实施问题，例如验证和验证所提议模型所需的数据量。因此，要成功地成功实施适当的模型选择，不仅需要对每种模型类型都有数学上的理解，而且还需要了解特定企业打算如何利用模型及其输出。本文讨论了选择适当的建模方法进行试用时需要考虑的业务问题。它还提供分类表和过程流程图，以帮助行业和研究人员选择适当的预测模型，以预测工程资产在其特定业务环境中的剩余使用寿命。然后，本文探讨了主要的预测模型类别的优点和缺点，以建立使它们比其他应用更适合某些应用程序的原因，并总结了每种方法如何应用于工程学预测。因此，本文应为年轻的研究人员首先考虑保留剩余使用寿命的预测方法提供一个起点。本文描述的模型是基于知识的（专家​​和模糊），预期寿命（随机和统计），人工神经网络和物理模型。

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  • 来源
    《Mechanical systems and signal processing》 |2011年第5期|p.1803-1836|共34页
  • 作者

    J.Z. Sikorska; M. Hodkiewicz; L. Ma;

  • 作者单位

    CASWA Pty Ltd., 24 Le Souef Drive, Perch, Kardinya, WA 6163, Australia,Department of Mechanical Engineering, University of Western Australia, Australia;

    Department of Mechanical Engineering, University of Western Australia, Australia;

    School of Engineering Systems, CRC/or Integrated Engineering Asset Management (CIEAM), Queensland University of Technology, Australia;

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  • 正文语种 eng
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  • 关键词

    prognostics; remaining useful life (RUL); maintenance; reliability;

    机译：预后剩余使用寿命（RUL）;保养;可靠性;

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