基于问卷调查理论的铁路自动化监控系统决策支持水平

D. V. Efanov, V. Khoroshev
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引用次数: 0

摘要

作者关注复杂技术系统监测技术的发展。这样的系统包括许多异构的运动和静止的物体。该系统配备了内置和外部的技术诊断和监测手段,形成了有关测量结果和组件当前状态的信息信息。这些信息使服务人员能够及时防止过程停机并识别亚临界状态。这增加了被诊断对象的容错性。在组织技术诊断和监测系统时,往往不可能提供必要的诊断的完整性和深度,以作出准确的诊断和随后的预测。然而,所获得的信息允许在被诊断对象的状态中形成许多固有的诊断特征。该信息可能是服务人员操作诊断对象在软件级别实现决策支持子系统的来源。为了形成决策支持子系统的初始数据,作者建议使用以下数据:测量子系统的数据、诊断对象的历史数据、自动模式下监测系统的统计指标。作为统计指标,使用对象缺陷各组成部分的发生概率和诊断实施成本数据。这些数据根据使用寿命、诊断对象对流程的重要性、其对系统准备程度的影响等因素而变化。源数据在软件级别用于诊断算法在问卷表格中的实现。问卷是一个基于树的加权有向图。输出包含测试诊断对象的推荐操作顺序。这允许您实现最有效的缺陷定位。给出了铁路自动化领域关键设施监控技术开发的一个实例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Decision Support Level in Monitoring Systems for Railway Automation Based on Questionnaire Theory
The authors pay attention to the monitoring technologies development for complex technical systems. Such systems include many heterogeneous moving and stationary objects. The systems are equipped with both built-in and external means of technical diagnostics and monitoring, which form informational messages about the measurement results and the current state of the components. This information allows service personnel to promptly prevent a shutdown of the process and identify subcritical conditions. This increases the fault tolerance of the objects being diagnosed. When organizing systems for technical diagnosis and monitoring, it is often impossible to provide the necessary completeness and depth of diagnosis for making an accurate diagnosis and subsequent forecasting. However, the obtained information allows the formation of many diagnostic features inherent in the states of the objects being diagnosed. This information may be the source for the implementation at the software level of decision support subsystems by service personnel operating diagnostic objects. To form the initial data for decision support subsystems, the authors proposed to use the following data: data from measuring subsystems, historical data about the diagnostic object, statistical indicators from monitoring systems in automatic mode. As statistical indicators, the probabilities of occurrence in the various components of object defects and data on the diagnosing implementation costs are used. These data vary depending on the service life, the diagnostic object importance for the process, its effect on the system readiness, etc. The source data is used at the software level for the diagnostic algorithm’s implementation in the questionnaires form. The questionnaire is a tree-based weighted oriented graph. The output contains the recommended sequence of operations for testing the diagnostic object. That allows you to achieve the most effective localization of the defect. An example of the technologies developed for monitoring critical facilities from the railway automation field is given.
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