Real-time reliability control will soon make it possible to drive profitability at the operator and equipment asset level.
实时可靠性控制可能将很快推动运营商以及设备资产等级的盈利率。
By Peter G. Martin, Ph.D., Schneider Electric出自耐施德电气公司的Peter G. Martin（哲学博士）
The ultimate objective of any industrial enterprise is to maximize and control operational profitability, safely, in real time. This is even more critical in today's manufacturing environment because of the ever-increasing speed of industrial business. For example, only a decade ago, many industrial plants had contracts with their electricity suppliers that designated the price they paid for a unit of electricity for an entire year. Today, on the open U.S. power grid, the price of electricity can change every 15 minutes.
任何工业企业都以稳定持续的追求业务盈利率最大化为最终目标。由于工业的不断加速发展，这个最终目标对于现今制造业环境尤为关键。例如，仅仅在十年前，许多工厂都已经与电力供应商签订全年电力供应协议以此制定电价。如今，在开放的美国电网，电价可以每15分钟更换一次。
Starting point 基点
Managing the business of industrial operations with monthly data from ERP reports is no longer feasible. You need to control operational profitability, in real time. Accomplishing that requires controlling and measuring the reliability of plant assets and asset sets, down to the equipment level.
通过ERP 报告的月度数据管理这种方式已不再可行。你需要实时控制业务盈利率。实现这一目标需要控制以及估量工厂资产以及资产组合的可靠性，并达到设备级。
Manufacturers have relied on various process-control methods and applications for more than 100 years. The primary objective has always been to safely increase plant production. Originally, single-loop feedback control was the preferred method, but it has been replaced by state-of-the-art process control in the past fifty years. Today, coordinated multiple-variable approaches, coupled with dynamic-process models, have enabled some very sophisticated predictive-control strategies.
制造商已经依靠多种程序控制方法走过了100多年了。根本目的是为了稳固地提升工厂生产率。起初，单环路反馈控制是首选方法，然而在过去的50年，已被顶尖的程序控制方法所取代。如今，协调的多维变量方法加上动态过程模型已经实现了一些极其复杂的预测性控制战略。
These advancements in process control have enabled manufacturers to continually increase operational throughput. However, there is an inherent risk in doing so. As industrial assets are pushed to deliver more, they move closer and closer to their reliability and safety thresholds. As a result, today's assets are under continuous strain that is degrading their reliability and affecting overall operational performance.
技术的进步使得制造商们可不断提升生产量。然而这样做具有潜在风险。随着工业资产最大化利用的期望越来越高，它们越来越接近其可靠性与安全性阈值。因此，如今的资产正处于一个持续被拉伸的状态，也正在减弱其可靠性和影响总体的运营效果。
Empowering today's workforce with real-time operational profitability data and process-control and real-time reliability-risk information will turn them into business-performance managers. Photo courtesy Schneider Electric
现今，授权全体员工掌握实时业务盈利数据，程序控制以及实时可靠性风险信息，能够让他们成为企业绩效管理者。
To counter that risk and alleviate the strain, industrial-maintenance tools and practices, intended to improve asset reliability, have progressed and evolved over the past two decades. Classic break-fix models, otherwise known as reactive maintenance, have been expanded first to preventive maintenance, then to predictive maintenance, and finally to prescriptive maintenance. Each of these advancements led to a corresponding increase in asset reliability. But manufacturers were soon stuck in a cycle because, even as advanced tools and techniques were being applied to improve asset reliability, process control became more sophisticated, fighting reliability improvements every step of the way.
在过去的二十年间，为了抵御风险以及缓解压力，提高资产可靠性的工业维护工具以及实操方面，取得了较大进步和发展。传统的故障修复模型也称作突发性维护，被延伸至预防性维护，然后到预测性维护，最后到标准化维护。而每一个进步都能使企业资产可靠性也相应增加。然而制造商们很快就陷入了困境。因为即使采用了先进的工具以及技术来提高资产的可靠性，程序控制也变得更加复杂，每一次提升都在向可靠性的提高提出挑战。
It turns out that more advanced technology isn't what we need. What we really need to do is rethink how we address this age-old issue, and that begins with how we measure asset reliability in the first place.
事实证明，更先进的技术并不是我们所需要的。我们需要做的是重新思考我们怎样去面对这个由来已久的问题，而这样做需要先从我们原来如何衡量资产可靠性着手。
图片内容转载自网络，本文内容为原作者观点，并不代表本公号立场。
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