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自动化系统的基因组
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在商业上可以应用得着的自动化系统中,产品、设备、软件组合的独特方法以及适合其他独特要求的组合方式已经成为专有“嵌入式”知识。
监测和控制系统通过相互的联系、集成、显示和多种信息的调整运行,其中,信息的调整包括了设定点、调整、理想的输出、实时数据和历史数据的回顾、监测失败、极少数的中断运转时间等等。
由于自动化系统通过监测越来越多的输入输出点而追求更大效能,导致系统的复杂性正不断朝着推动更高的效率和产量方面增加。因此操作接口变得越来越复杂,所以对操作员来说,在操作技能和培训方面有了越来越大的需求。
在哪里能找到足够好的可以有培训空间的操作员?那么培训他们需要花费多长时间?事实上,要改善效能并不是仅仅通过培训操作员怎么样使用日益复杂的系统就可以达到的,而是取决于系统的不断发展并且系统能够有效地适应用最少的操作者生产出最大的产量的策略。
举例来说,操作员可以识别出并且尝试纠正少数的警告。但是,在真正的有害状态条件下,人为的干涉并不起作用,那么控制系统必须自动修正和自动达到最优化。也就是说,系统必须能够启发式地适应减少,而不是增加对操作员的需求。
除了显示程度、趋向性和警告之外,有效的系统还需要有诊断的能力。一般来说,在一个问题发生之后会有一个故障分析,但是改善效能来自于预防性的诊断(避免出现故障)和预测性的诊断(对将要发生故障的警告)。
最优化的控制补充进传统的分布式计算机系统(dcs)和可编程逻辑控制器(plcs)可使车间或工厂运营的收益尽可能的最大化。有两个最优化方法:回顾历史,确定变量的和控制策略组合方式从而得到最佳结果;推测、计算和预测最合适的变量组合和更进一步的改进的控制方法。
考虑以下方面:如果车间或者工厂的所有部分必须被复制在一个完全不同的地方,那该怎么办?传统的解决方法可能是尝试去复制全部,但是事实上这是不可能的。因为那样做的话不仅需要购置和安装同样的控制系统和软件,还包括长期积累的变动、软件补丁和修改、控制策略、诊断以及其他更多的东西。在新的地区可能有不同的需求,要使当地的原材料尽可能的被充分利用上并且改变地方本身需求,所以系统必须适应,而这种适应的进度必须被跟进;就好像与一个正在进行的、变化的实体保持密切联系一样。

控制系统的基因组
任何一个工厂、车间和进程都具有唯一性,其复杂性被休斯敦pas公司的埃迪·海比描述为:进程和控制的系统 “基因组”——与dna有相似性,并且基因组能使一个活着的实体具有唯一性。就像现实中生物的变异一样,控制系统的基因组一直在改变并且适应改变、增加装备和控制能力、“学习”控制策略。于是,保持对自动化系统基因组的追踪就是一个独特的概念,pas公司已经于1996年进行这方面的工作,并且将持续进行。
跟踪控制系统基因组牵涉到许多不同的仪器、测量方法和控制系统,比如有录制手段或者用类推法,“排序”方法等等,这些都是从不同的供应商那里购买,供应商中包括了庞大的主流自动化供应商和较小的独立设备供应商和系统集成商。基于这个原因,追踪系统基因组在某种程度上是不能只由某一个供应商(其典型地只能使他们自己的设备达到最优化)完成。必须还要有一个在追踪性能和多方系统的适应性很精通,并且致力于通过基因组的追踪达到进程最优化的独立公司。pas就是在这种快速发展市场中的先锋力量。

the "genome" of your automation system

beyond the use of commercially available automation systems, the unique mix of products, equipment, software and the way they are adapted to the specific requirements becomes proprietary "embedded" knowledge.
monitoring and control systems operate through the correlation, aggregation, display and adjustment of diverse information: set-points and adjustments, desired outputs, review of real-time and historical data, monitoring of failures, minimizing down-time and the like.
system complexity tends to increase in the continuous drive for better efficiency and productivity, as automation systems pursue increased effectiveness through monitoring and control of increasing numbers of bbbbb/output (i/o) points. the operator interfaces become increasingly complex, so that more and more operator expertise and training and are demanded.
where does one find enough good operators to train, and how long does it take to train them? improved effectiveness comes not from training the operator to use increasingly complex systems, but from developing systems that adapt effectively to maximize throughput with a minimum of operator involvement.
for example, operators can acknowledge and attempt to correct just a few alarms. but, under really adverse conditions, human intervention is ineffective, and the control system must be self-correcting and self-optimizing-which means that the system must adapt heuristically to reduce, not increase, the need for operators.
beyond just displaying measurements, trends and alarms, effective systems demand diagnostics. traditionally, this has been failure analysis-after a problem has occurred. but improved effectiveness comes from diagnostics that is preventive (to avoid failure) and predictive (warnings about future failures).
optimizing controls supplement conventional distributed control systems (dcs) and programmable logic controllers (plcs) to maximize profitability of plant or factory operations. there are two optimization approaches: historical review to determine what mix of variables and control strategies resulted in peak performance; and, extrapolating, calculating and predicting the optimum mix of variables and controls that will provide further improvements.
consider this: what if the entire plant or factory must be duplicated in a completely different bbbbbbbb? the conventional approach would be to try replicating the entire plant-which is virtually impossible. that involves not just the purchase and installation of the same control systems and software, but including the accumulated variations and software patches and modifications, control strategies, diagnostics and more. and there may be different requirements in the new bbbbbbbb that demand variations to optimize for local materials and changed local needs. the system must adapt, and the adaptations must be tracked; it`s like keeping track of a living, changing entity.
the control system genome
each factory, plant and process is unique, and the complexity is described with what eddie habibi, of pas inc., in houston, calls the process and control systems “genome” similar to the dna and genome that makes each living entity unique. just as living beings mutate, the control system genome keeps changing and adapting with modifications, additions of equipment and controls, and “learned” control strategies. keeping track of the automation system genome is a unique concept that pas has worked on since 1996, and this is still a work in progress.
recording, or to follow the analogy, "sequencing," the control system genome involves tracking many different instrumentation, measurement and control systems purchased from several different suppliers-large automation majors as well as smaller independent equipment suppliers and systems integrators. for this reason, system genome tracking is something that cannot be provided by just one of the primary suppliers (who typically can only optimize for their own equipment). it must be provided by an independent company that specializes in tracking the performance and adaptations of multi-vendor systems, and is dedicated to process optimization through genome tracking. pas is a pioneer in this fast-developing market.

 

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