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An Improved Failure Rate Model Applied to Baghouse Failures

机译：应用于布袋除尘器故障的改进故障率模型

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The Weibull distribution has been routinely used for nearly half a century to describe a range of equipment and system failures as a function of time. A variety of conditional failure distributions, including break-in and wear-out patterns, can be accommodated by the Weibull distribution. In this paper, a new and improved model is proposed that can be employed to describe the failure rate distribution as a function of time for many of the applications for which the Weibull distribution has been classically used. Unlike the Weibull model that requires specifying or evaluating six empirical coefficients over the entire break-in through wear-out period, the proposed Dupont, Ricci, and Theodore (DRaT) model only requires four empirical coefficients. The paper provides both the development and derivation of the DRaT model, along with procedures for generating numerical values for the four coefficients based on observed failure rate data using least squares regression analysis in an EXCEL spreadsheet environment. Two illustrative examples related to baghouse failures complement the presentation. The first example illustrates the determination of the four coefficients from observed bag failure data. The second example applies these results to a real-world application predicting the time when a baghouse becomes out of compliance due to excessive bag failure. The proposed DRaT model appears suited to represent failure rates for many situations that have historically employed the Weibull distribution. The advantages of the new model, in terms of its simplicity and ease of implementation, should make it a robust and effective substitute for the Weibull distribution for future failure rate applications such as the baghouse failure example presented in this paper.
机译：威布尔分布通常已使用了近半个世纪，用以描述一系列设备和系统故障随时间变化的情况。 Weibull分布可以适应各种条件性故障分布，包括磨合和磨损模式。在本文中，提出了一种新的和改进的模型，该模型可用于描述许多经典使用Weibull分布的应用程序的故障率分布随时间的变化。与Weibull模型需要在整个磨合期指定或评估六个经验系数不同，建议的Dupont，Ricci和Theodore（DRaT）模型仅需要四个经验系数。本文提供了DRaT模型的开发和推导，以及在EXCEL电子表格环境中使用最小二乘回归分析基于观察到的故障率数据为四个系数生成数值的过程。与布袋除尘器故障相关的两个说明性示例补充了该介绍。第一个示例说明了从观察到的袋子故障数据确定四个系数的过程。第二个示例将这些结果应用于实际应用程序，该应用程序预测由于过度的袋子故障而导致集尘室变得不合规的时间。提出的DRaT模型似乎适合于表示历史上采用Weibull分布的许多情况下的故障率。新模型的优点在于它的简单性和易于实施性，应该使其成为将来失败率应用（例如本文中提出的布袋除尘器故障示例）的Weibull分布的稳健而有效的替代品。

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