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Intelligent condition-based prediction of machinery reliability

机译：基于智能条件的机械可靠性预测

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摘要

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models that attempt to forecast machinery health based on condition data. This paper presents a novel approach for incorporating population characteristics information and suspended condition trending data of historical units into prognosis. The population characteristics information extracted from statistical failure distribution enables longer-range prognosis. The accurate modelling of suspended data is also found to be of great importance, since in practice machines are rarely allowed to run to failure and hence data are commonly suspended. The proposed model consists of a feed-forward neural network whose training targets are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density function (PDF) estimator. The trained network is capable of estimating the future survival probabilities of an operating asset when a series of condition indices are inputted. The output survival probabilities collectively form an estimated survival curve. Pump vibration data were used for model validation. The proposed model was compared with two similar models that neglect suspended data, as well as with a conventional time series prediction model. The results support our hypothesis that the proposed model can predict more accurately and further ahead than similar methods that do not include population characteristics and/or suspended data in prognosis.
机译：预测机械故障的能力对于降低维护成本，减少运营停机时间和安全隐患至关重要。状态监视技术的最新进展已经产生了许多预测模型，这些模型试图根据状态数据预测机械的运行状况。本文提出了一种将人口特征信息和历史单位的悬浮状况趋势数​​据纳入预后的新方法。从统计故障分布中提取的总体特征信息可以实现更远的预后。还发现，准确地对挂起的数据建模非常重要，因为在实践中，机器很少会发生故障，因此通常会挂起数据。提出的模型由前馈神经网络组成，其训练目标是使用Kaplan-Meier估计器和基于退化的故障概率密度函数（PDF）估计器估计的资产生存概率。当输入一系列条件指标时，训练有素的网络能够估算经营资产的未来生存概率。输出的生存概率共同形成估计的生存曲线。泵的振动数据用于模型验证。将该提议的模型与忽略悬浮数据的两个类似模型以及常规时间序列预测模型进行了比较。结果支持我们的假设，即与不包含人群特征和/或暂挂数据的预后相似的方法相比，所提出的模型可以更准确地预测未来。

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  • 来源
    《Mechanical systems and signal processing》 |2009年第5期|1600-1614|共15页
  • 作者

    Aiwina Heng; Andy C.C. Tan; Joseph Mathew; Neil Montgomery; Dragan Banjevic; Andrew K.S. Jardine;

  • 作者单位

    CRC for Integrated Engineering Asset Management, Faculty of Built Environment & Engineering, Queensland University of Technology, Brisbane, Queensland 4109, Australia;

    CRC for Integrated Engineering Asset Management, Faculty of Built Environment & Engineering, Queensland University of Technology, Brisbane, Queensland 4109, Australia;

    CRC for Integrated Engineering Asset Management, Faculty of Built Environment & Engineering, Queensland University of Technology, Brisbane, Queensland 4109, Australia;

    Centre for Maintenance Optimization & Reliability Engineering, Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, Ontario, Canada;

    Centre for Maintenance Optimization & Reliability Engineering, Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, Ontario, Canada;

    Centre for Maintenance Optimization & Reliability Engineering, Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, Ontario, Canada;

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  • 正文语种 eng
  • 中图分类
  • 关键词

    artificial neural networks; condition-based maintenance; condition monitoring; prognostics; reliability; suspended data;

    机译：人工神经网络;基于状态的维护;状态监测;预后可靠性;暂挂数据;

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