The Complete Set of Distinguishable Diagnostic States for a Centrifugal Pump System

Paweł SZCZEPAŃSKI (paszczep@wp.pl), Robert KIJAK (kijakr@gmail.com)
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Abstract

In this article, the concept of determination of a complete set of distinguishable diagnostic states has been utilised, which has been well known since the 1980’s and, in essence, could refer to any item with any application and any structural features. The only condition set for this item, is its ability to be described by a structure consisting of single exit-point elements. As a consequence, it can become possible to assess, amongst other things, its layers, strong cohesions, transitive and antitransitive closures. Typical tools of such assessment are matrices: transition and reachability. The reason that a centrifugal pump has been selected is not only its importance and common use, but also specifics of interaction of its elements. It is possible to identify for them: independence, dependence, and interdependence. For particular distinguishable diagnostic states, the probabilities of its occurrence have been determined. These probabilities have become a starting point to estimate structure entropy and intensity of transition from the health to any particular failure state. Results of such estimation have been depicted by relevant graphs. A reference has been made to the Bayesian methods and the inseparable ‘serial reliability structure’, to identify their flaws that result particularly from simplifications leading to false and ambiguous diagnoses. The probabilities of health for particular elements have been differentiated by the selection of adequate Weibull indices.
离心泵系统可分辨诊断状态的完备集
在本文中,使用了自20世纪80年代以来众所周知的确定一整套可区分诊断状态的概念,本质上可以指具有任何应用和任何结构特征的任何项目。为这个项目设置的唯一条件是它能够被由单个出口点元素组成的结构所描述。因此,可以评估它的层、强内聚、传递和反传递闭包等。这种评估的典型工具是矩阵:转换和可达性。选择离心泵的原因不仅在于它的重要性和普遍用途,而且还在于其各要素相互作用的具体情况。可以为他们识别:独立、依赖和相互依赖。对于特定的可区分的诊断状态,其发生的概率已经确定。这些概率已经成为估计结构熵和从健康状态过渡到任何特定失效状态的强度的起点。这种估计的结果已经用相关的图表来描述。参考了贝叶斯方法和不可分割的“串行可靠性结构”,以识别它们的缺陷,特别是由于简化导致错误和模糊的诊断。通过选择适当的威布尔指数来区分特定元素的健康概率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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53 weeks
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