摘要
随着社会的发展,特钢行业面临着越来越大的竞争压力。能源价格、原燃料价格不断上升给特钢行业带来了越来越大的成本压力；同时社会对产品的需求也越来越个性化、小批量、多品种化,对企业的生产过程带来了越多的压力。特钢生产过程又具有复杂多变、工艺路线长、工艺交叉多等特征,外界环境的变化对生产工艺、计划等造成的影响巨大。特钢生产过程自身又是一个充满不确定性的过程,物料加工时间、物料成分、设备能力状态、物料选用的工序设备等都具有很大的变动性,加之交叉多变的工艺,给特钢的生产调度带来了很大的困难。为了及时响应生产过程内外部的压力,需要能够实时监控生产过程信息,查找过程中的“不稳定”信息,及时应对生产变动,提高生产效率,降低生产成本,增强产品竞争力。
针对生产过程扰动处理的需求,建立了生产信息实时监控模型,随时检测生产扰动,响应生产过程中的不确定性信息,提高系统的灵敏度。
建立了以信息熵为特征的生产扰动检测模型,及时判断生产中的不确定性是否在可控范围内。根据设定的识别阈值和系统动态的扰动检测偏离度值,判定系统的状态,为进一步的决策做出依据。
根据扰动的特点和原因,建立了生产扰动的故障树模型。分析生产扰动的原因,揭示生产扰动中来源,通过图形的方式使扰动分析更清晰。针对订单等显性扰动,通过xBOM的监控采用直接处理识别的方式。对于隐性扰动及不易识别的质量、设备能力等的扰动,建立了基于规则推理的扰动分类模型。对于为难以通过规则推理判定的扰动类型,采用证据理论的方式,判别扰动类型,实现扰动的快速识别。
通过扰动的快速识别,为下一步的重调度提供支持,增强系统的响应能力,提高系统的稳定性。
With the development of the society, the special steel company is facing more and more competitive. Energy price, raw materials prices and fuel price are rising quickly and bringing cost great pressures to the special steel industry. At the same time, the society demands the products more and more personalized, small quantity and variability. While special steel production is a complex, long process route and process-cross-over with each other, it brings huge impact with the change of external environments. The production of special steel is full of uncertainty. Material processing time, material element, equipment capacity state and material production process and so on has great changes. Combined with cross-over process, it brings big difficulty to the production of the special steel. In order to respond to the pressure from outer and inner system, it needs to monitor the real time information, find out the abnormal information, and respond to the changes of the production. It can improve the production efficiency, reduce production costs and enhance product competitiveness.
For the need of treatment to the disruption, it establishes a monitoring model of real time information. It can detect the disruption at any time and respond to the uncertainty to improve system sensitivity.
It establishes a detecting model with Entropy. It can determine whether the uncertainty is in the control range. According to the decision threshold setting and the system dynamic disrupt deviation value, it can determine the state of the system and provide the basis to the further decisions.
According to the characteristics and causes of the disruption, it establishes the fault tree model of production disruption. Through fault tree model of production disruption, analyze the causes of production disrupt, reveal the source of disruption, and make it more clearly through the way of graphical analysis. For orders and other dominant disturbance, it deals with them directly with the monitoring of xBOM. For the hidden and other difficult to identify disrupts, such as quality, equipment capacity, it establishes a Rule-Based Reasoning disrupt classification model. For the disruption difficult to determine by the type of rule-based reasoning, it uses Evidence Theory approach to determine the type of disruption to achieve rapid identification.
Through the rapid identification of disruption, it supports the next re-scheduling and enhances the system's responsiveness, increases system stability.
引文
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