纽柯工厂如何从状态监测中受益

过去几年，Azima DLI 的集成监控和诊断解决方案已广泛应用于纽柯钢铁公司的希克曼薄板厂。

自动化数据收集已被引入并与工厂现有的传统手动状态监测程序相结合。从这两种方法收集的数据通过一个单一的门户网站进行解释和显示，诊断由 Azima 分析师远程执行。

本文描述了这种集成解决方案的理由、开发、应用和好处。将介绍具体案例研究，同时讨论部署问题和已克服的障碍。

状态监测概述

状态监测（通常称为预测性维护）是提高工业可靠性和生产力的一种行之有效的方法。

其基本理念是可以使用技术来衡量和评估工厂资产和设备的状况，从而能够对维护活动做出明智的决策。

以这种方式，对需要关注的资产进行维护，而那些性能和状况被确定为正常的资产则被留下来发挥生产性作用。

状态监测的最初应用是在 1970 年代的公用事业和石化行业。在此期间，工厂进行了大规模的扩建和建设，工厂规模急剧扩大。

与依赖许多小型机器不同，工厂列车的规模和产能如此之大，以至于典型的炼油厂或大型发电厂完全依赖于极少数、非常大且非常昂贵的资本机器。

其中一项资本资产（主要是旋转机械）因机械故障而损失可能（而且确实）导致重大收入损失，对于一些较大的石化工厂，还会导致市场实际中断。

这一现实导致了故障保护系统（基于振动分析的警告/跳闸系统）的发展，以保护旋转资本机械免受灾难性故障的影响。

事实证明，这些故障保护系统在防止碰撞和限制机器故障造成的损害方面非常成功。这些系统很快被制度化，标准（API 等）被开发并被相应的行业接受。

由于故障保护系统的成功，许多工厂操作员开始怀疑类似的方法是否可以应用于工厂中无数的小型机器。

尽管其中一台较小机器的故障本身并不能关闭工厂，但这些机器的总维修成本使得状态监测在典型的炼油厂或化工厂中的应用前景广阔。

然而，应用故障保护系统的每台机器的高成本阻止了这成为现实。相反，技术响应了便携式测量仪器的发展。

从简单的仪表、手动日志表和趋势开始，机器监控程序诞生了。从 1980 年代后期开始，PC/计算机技术的爆炸式发展推动了计算机化手动数据收集系统的发展，这些系统在工厂维护市场上迅速取得了成功。

在很短的时间内，“数据收集器”系统（具有明确定义的测量功能的可编程黑匣子）的使用扩展到许多行业，包括钢铁行业。

定义要测量的机器和要进行的特定测量的“路线”在基于 PC 的软件中创建并下载到数据收集器。

人员将进入工厂使用设备收集数据，然后上传数据。然后将分析这些数据并发布报告，推荐适当的维护措施。

通过数据处理技术的巨大进步，这些所谓的“绕行系统”已经发展到极限不再是测量技术的地步。一天可以收集兆字节的机器状态数据，对技能的要求相对较低。这种方法代表了今天的现状。

“竞争性”巡查数据收集和分析

当前的数据收集器系统非常强大，与 1980 年代最初的系统相比，表现出近乎奇迹般的改进。现代数据收集器主要用于收集旋​​转机械振动数据（尽管通常可以输入其他标量和非动态数据，包括人工观察和评论）。

随附的软件可以显示收集的数据以进行分析。该软件通常支持合并其他外部数据，例如红外热成像和润滑剂分析。

在典型的电弧炉/小型钢厂环境中，此类绕动振动计划的实际执行和责任由内部或外部合同资源负责。

带有相关冷轧机的典型 AF/小型轧机可能有 500 到 600 台单独的机器，通过数据收集器/巡查方法进行监控/监视，每个月进行 5,000 到 10,000 次单独测量。

在典型条件下，这仅代表大约一到两个人周的测量工作。数据分析通常会再消耗一个人周。

今天的手动数据收集器系统的效率已接近顶峰。早期（1980 年代后期），微处理器和存储器中的处理技术限制了它们的性能，并且根据要进行的测量，技术分析师经常等待数据收集器执行给定的任务。

因此，他的效率是有限的。今天的处理技术已经发展到所需的采样时间和数据数字化的基础数学是时间限制，而不是硬件和软件的性能。

由于测量技术的变化，期望在数据收集时间方面有任何显着的收益是不合理的。

在手动数据收集程序中，实现 60% 到 70% 的负载因子（实际进行测量所花费的时间，而不是从点到点移动、上传和下载数据等）是一项重大成就。

状态监测的数据收集部分消耗 70% 或更多的经常性运营/劳动力成本并不罕见。

当然，数据收集只是执行成功的状态监测程序过程的一部分。数据分析、筛选流程和报告对于降低成本和提高正常运行时间至关重要。

在大多数情况下，具有适当经验和培训的人员的可用性和技能是状态监测计划成功的决定性因素。收集数据并不难；正确使用数据要始终如一地实现要困难得多。

尽管测量技术非常强大，但特定工厂的状态监测成功与否仍然取决于从工厂机器收集和正确解释数据的能力。

手动数据收集过程，即使存在有效的数据收集和熟练的分析，本质上仍然是快照，可能无法反映工厂设备经历的实际操作条件。这是因为数据的日常变化比每月收集路线显示的要多。

从历史上看，钢铁行业一直愿意采用状态监测作为提高钢厂生产率的手段，钢厂中有许多成功的项目。

无论是外包还是内部执行，都存在与成功实施状态监测计划相同的问题。最困难的问题是在状态监测角色中保留适当的技能和经验。

为了成功，个人必须：

• 非常熟悉旋转机械和机器部件——个人必须了解联轴器、轴承和齿轮、机器动力学（至少在某些基本层面）以及影响机器磨损的因素。

• 至少熟悉（或能够并愿意接受培训）更深奥的概念，如频谱分析、频率分析、相位、调制等，并对一些通常不属于词汇表的基本概念有足够的理解具有必要机械技能的个人。

• 掌握计算机技术，至少达到高级应用程序用户的水平——当前所有的状态监测技术都在很大程度上涉及计算机的使用。

• 做一个熟练的沟通者。清楚地解释给定建议背后的原因是成功的状态监测计划的关键且经常被忽视的要求。

• 愿意（或有愿意的人员）定期进行高温、肮脏、重复的数据收集工作，这些工作通常涉及难以进入或危险的区域。

不难理解为什么长期保持适当的人力资源，无论是内部还是外包，都难以实现。在状态监测技术的发展年代（1970 年代末至 1990 年代初），大多数工厂，无论规模大小，都有一两个（有时更多）资源用于状态监测项目。

这种供应导致了一个有效的“农场俱乐部”系统，该系统为厂内项目和外部合同/顾问来源提供了经验丰富的人员。工厂人事变动、人员轮换、退休等，造成了这样的人员数量有限和减少，而这些工人的成本却在上升。

具有适当经验和技能的人力资源的可用性下降导致探索将数据提供给分析师的方法。通过这种方式，具有必要技能的人可以覆盖比其他方式更多的工业房地产。

移除监控的兴起

自 1970 年代后期以来，工厂和过程控制与通信自动化的采用在复杂性和市场接受度方面都显着提高。每张桌子上和每个控制室里的电脑现在都是常态。工厂和工厂通常拥有非常复杂和广泛的 IT 网络，用于管理和过程控制/自动化。

许多不同行业的第一种方法涉及将与机器状况相关的数据传输到现有的人机界面 (MMI)/人机界面 (HMI) 系统。

特别是，现有的机械保护（警告和跳闸）系统绑定到控制室 HMI/MMI 界面，以便操作员可以查看振动、温度和其他机器状态参数。

通常，这些只是数量级的标量值，虽然在信息和可能的机器问题的指示方面很有价值，但在趋势、分析和数据解释能力方面却缺乏。

因此，工厂人员收到的信息是，给定的机器振动更大，或者轴承温度上升等。这就留下了这些变化发生的原因以及工厂应如何应对这些变化的问题。

技术进步、互联网以及在人员有限的情况下更有效地监控机器的需求推动了扩展远程监控系统的发展。

技术和互联网不仅可以在内部提供机器数据（到控制室等），还可以随时随地访问信息。这种新方法使用现场安装的传感设备和仪器、某种聚合设备、高级软件以及访问工厂和外部网络进行数据传输。

这种系统的好处是显而易见的：

• 无需数据收集/分析人员在场即可更频繁地监控机器，并且能够观察由于负载、速度和操作条件而导致的机器状态动态变化。

• 能够将数据移到异地以进行远程咨询。

• 在多个位置拥有多个资源查看数据的能力。

• 能够监控非传统手动程序通常监控的参数。

• 能够在效率、吞吐量等方面评估机器性能，并将该数据与机器状况相关联。

Nucor Hickman 状态监测的历史

纽柯希克曼 (Nucor Hickman) 已实施状态监测计划 10 多年。该计划基于传统的人工调查方法和技术，并由外部合同资源提供。

每月收集整个工厂设备的数据，并经常调用故障排除和特定问题分析。数据收集完成后，进行分析并将带有建议的书面报告分发给适当的工厂人员。

工厂中的所有系统（环境、热轧机、连铸机和熔炼车间）都包括在内。 1998年，纽柯希克曼增设冷轧设备，配备酸洗线、镀锌线、RT轧机和退火能力。

传统的监控计划被扩展（几乎翻了一番）以包括工厂的这一部分。将冷轧机整合到计划中后，每月对 590 台机器进行监控。一项典型的调查需要两到三个人工周才能完成。

随着早期形式的远程数据收集和分析方法的出现，远程监控可能实用且有益的机会增加了。在第一种情况下，铸模水冷却泵传统上是一个维护问题，由于操作条件的变化而成为潜在的应用。

当时 Nucor Hickman 的模具水泵装置由三个 700 马力的对置吸入离心泵组成，直接连接到感应电机驱动器。在典型的操作中，两台泵在使用中，一台作为串联备用泵进行维护。

操作要求表明需要增加模具水流量，并且所有三个泵都投入使用。这对泵的振动水平产生了负面影响，并降低了对泵可靠性的信心。

没有剩余的备用容量（备用泵现在连续运行），这使得泵故障的影响很大。 Nucor Hickman 决定将一些早期的基于互联网的远程监控技术应用于泵。

预期的好处是更接近实时的问题检测和更好地了解泵的振动行为。此外，每个电机上的系统都增加了一个电流传感器，这将允许在每个泵上监控系统负载以及泵和电机振动。

该系统将报告来自每个泵的轴承温度、振动幅度、排放压力、电流幅度和频率成分/时域振动数据。这些数据可供负责监控泵行为的承包商分析人员以及工厂人员使用。

此外，这些数据可通过 Web 访问，因此可以通过 Internet 连接从任何地方访问。多个用户可以从多个位置同时访问系统。

安装了基于互联网的监测系统，在不同的操作条件下收集数据的频率更高。在调试在线远程系统的几天内，很明显，泵的振动行为变化比每月一次的数据显示的要大得多。

这是因为月度数据——即使是几年的数据——根本不足以引起注意。自动化系统支持的频繁收集清楚地显示了不同操作模式的 3:1 变化。此外，负载电流数据（无法从传统的每月一次的数据中获得）表明，这些泵的负载通常几乎没有达到容量，并且运行良好。

因此，委托进行了泵系统和操作的工程研究。该研究得出的结论是，尺寸错误、控制策略和管道配置对于所需的操作条件是不正确的。调整了泵的尺寸并进行了其他更改，以提供所需的增加流量。

这提供了备用容量，而无需运行所有三个泵，并为改善冷却和模具寿命做出了重大贡献。如果没有远程监控系统提供的数据，这些问题不太可能被曝光。

远程监控技术的进一步应用安装在热轧机的布袋除尘风机和实用空气压缩机上。热磨空气压缩机为三级离心机，由两极感应电机直接驱动。

压缩机和电机每月接受一次监测，驱动电机经常出现轴承故障。每月一次的数据经常发现轴承故障，但趋势不稳定，没有检测到明确的根本原因。

应用远程监控技术后，很明显 - 与模具水泵一样 - 振动水平的变化比每月手动数据明显得多。

也很明显，这些变化具有密切跟踪环境温度的模式。由于轴承温度也受到监控，很明显，当环境温度升高时，振动水平会急剧增加。

远程监控系统提供的频率成分分析清楚地表明，振动增加的原因是电机转子不平衡。

对轴承磨损模式的进一步审查表明，电机的径向康拉德型滚珠轴承承受了极大的推力载荷。由此，很明显，在高温下，电机转子会轴向膨胀，而电机轴承配合中的间隙不足以允许这种膨胀，从而导致转子弯曲（因此不平衡）和电机轴承的轴向过载。

由于这一发现，压缩机驱动电机被替换为另一种设计。远程监控系统报告的振动水平仍然很低，可靠性显着提高。

在 Hickman 的冷轧机中，RT 轧机机架驱动装置配备了四台 5,000 HP 同步电机。电机是轴颈轴承机器，不可靠。

推力和由此产生的轴承故障，以及极片电气故障，导致 2004 年安装了基于电机上的接近探头和磨机齿轮箱上的加速度计的警告和跳闸系统。

该系统不仅提供警告和跳闸功能，而且还为工厂外的分析人员提供近乎实时的振动数据，包括轴轨道。远程分析人员可以在数百英里之外，查看近乎实时的数据，并就轧机机架的问题直接咨询讲坛操作员。

由于这些和其他方面的成功，很明显远程机械监控提供了传统方式无法提供的改进和功能。

然而，这个早期的系统确实有局限性。它依赖于与扫描仪/站点服务器的串行 RS-485 通信。随着系统的扩展，采样率变慢，无法及时获得最新数据。

它还使用了专有传感器，这限制了应用的可配置性和灵活性。 RT 磨机上的系统虽然功能强大，但价格昂贵，而且由于使用了 VPN 技术，一次只能使用一个用户。

从 2004 年底和 2005 年初开始，Nucor Hickman 开始在工厂部署无线技术。理由是热轧机中的运输和库存应用程序、起重机和系统，以及满足其他操作要求。

与此同时，使用相同无线协议的改进的远程监控技术正在市场上出现。这种由 Azima DLI 开发的新型远程监控技术使用商用现货 (COTS) 传感器、标准网络协议，并且在软件和应用方面更加灵活。

无线技术的融合、其在工厂的应用以及改进技术的可用性都推动了纽柯希克曼远程设备监控的扩展。

有趣的是，工厂内的其他应用程序证明了无线实施和部署是合理的。机械监控数据对网络的影响很小，即使在 Nucor Hickman 广泛部署，也只占网络流量的一小部分。

Nucor Hickman：美国最大的远程监控部署

截至本文发表时，纽柯希克曼薄板厂是美国最大的动态信号远程监控部署地点。

Azima 远程监控技术用于所有轧机冷却塔、所有袋式除尘器 ID 风扇、所有袋式除尘器反向风机、连铸机模具水泵、连铸机喷水泵、所有除垢泵、屋顶给料助焊剂系统抽风机和轧机空气压缩机。

轧机应急发电机组的远程监控系统安装正在进行中，并计划在不久的将来冷轧机的成形张力辊上安装。大约有 280 个传感器被远程监控。

工厂人员可以完全访问数据、警报历史、警报和警告、报告和报告/机器历史。警报通过电子邮件和/或手机短信发送。

Nucor Hickman 目前应用的系统通过工厂网络通过 802.11b 无线技术或标准以太网进行通信（不同于早期的系统，需要串行 RS-485 通信）。

单个小型站点服务器（标准 PC）位于工厂中，用作数据网关和缓冲设备。如果工厂外的连接中断，站点服务器将充当数据存储设备，在连接恢复之前缓冲数据。

关键功能之一是监控应用程序不需要在目标客户端 PC 上安装任何软件——只需要访问互联网和登录系统即可。

该系统由安装在目标机器上的传感器和一个传感器集线器组成，该传感器集线器对来自传感器的数据进行数字化和聚合。 Collected data is transmitted securely to the plant network via either 802.11b wireless or Ethernet. (Note that, although not currently employed at Nucor Hickman, the system also supports data transmission via a cellular interface, independent of the plant network.)

Figure 1. How the Azima DLI Remote Monitoring System Works at Nucor Hickman

Data is sent via the plant network to the local site server and then out over the Internet to Azima DLI’s remote servers. Plant personnel, analysts or other authorized parties can then access the system via a secure Web portal.

Access and privileges are controlled by double password, and depending on privileges, a user can have rights to view data, edit system settings, analyze data and/or issue reports. All data, alerts and alert histories, and reports generated by analysts are maintained on the Web portal.

Histories of reports generated can be sorted and searched by plant area, date, machine, fault type and other criteria.

While the number of remotely monitored machines continues to expand at Nucor Hickman, many less critical machines remain under manual surveillance by operators on rounds once a month.

These machines include those in the balance of plant (hydraulic pumps, roll stand cooling pumps, furnace cooling pumps, and so forth). This monthly data is fed into the Azima DLI system and displayed via the same secure Web interface as the remotely collected data.

This means that a mill-wide view of equipment health – from all monitored machines, regardless of collection method – is visible via a single platform.

Azima analysts are responsible for monitoring and analyzing all posted to the Web interface.

What’s Involved in Getting Remote Monitoring Started

One of the most attractive features of the remote monitoring system at Hickman is that it is comprised primarily of low-cost, commercially available components (for example, COTS sensors) combined with advanced software and specialized sensor hubs. Careful planning and forethought is needed to ensure a successful remote monitoring deployment. Some of the lessons learned include:

• Careful thought should be given as to which machines are to be monitored and in what manner. In the case of Hickman, although the condition monitoring contractor had overall responsibility for the implementation and operation of the system, and for disseminating the analysis and results there from, each individual mill area had to decide what was to be monitored and how. Reporting relationships, access and other issues were all discussed and defined well ahead of the installation of monitoring equipment. There was a clear list of which equipment would benefit from automated remote monitoring and which could be sufficiently monitored by conventional methods.

• Number of points per machine to be monitored is an important consideration. Simply mimicking what would have been done in a manual monitoring program (multiple points and measurements per bearing) is usually unnecessary and adds cost while delivering small incremental value.

• If wireless transmission is to be employed, determine the best protocol for the site:802.11b/g, ZigBee, or 900 megahertz. In the case of Hickman, 802.11b was the preferred choice and that has been universally true in other applications as well. It offers the widest selection of available devices, and can cover a wide area with just a few access points. The 802.11b protocol has proven to be very reliable and is immune to RF interference, even in the melt shop area.

• Involve IT early. Agreement needs to be reached on security, protocols, etc. well before the installation of a remote monitoring system. What are the mill’s security expectations and policies? Within the plant, is traditional Local Area Network (LAN) acceptable, or is a Virtual Local Area Network (VLAN) necessary? IT support during deployment is crucial to successful and efficient startup. Connectivity must be verified, etc.

How Remote Monitoring Benefits Nucor Hickman

The initial deployment of the current generation of remote machinery monitoring technology commenced in July 2005. The installation and commissioning of Azima’s remote monitoring system is continuing to expand.

The hybrid approach of monitoring critical machines in parallel with traditional manual walk-around monitoring of balance-of-plant equipment, all reported via a common Web-based portal, has provided solid value to Nucor Hickman personnel. Several case studies demonstrating this value are briefly detailed below.

Case Study 1:Failure Caught Without Site Visit or Increased Costs
In the spring of 2006, a 1,500 HP baghouse fan induction motor failed due to a sudden stator short failure. All of the baghouse fans were equipped with remote monitoring hubs and were under surveillance.

The motor was replaced with a rebuilt spare. Immediately on restart of the fan, much higher vibration levels were noted by the remote monitoring analyst (who was not on site). Mill environmental department personnel, who were responsible for the baghouse and its equipment, were notified of the increased vibration.

Further examination of the data identified the problem as an outer race defect on the inboard (drive end) bearing of the motor. Mill personnel requested an evaluation as to the likelihood of the motor continuing in service until the next maintenance outage.

Analysis of the data and the rate of change indicated that it was likely that the unit would indeed continue to run. In an attempt to increase the likelihood of a successful outcome, attempts at relubrication of the motor bearing were undertaken. Unfortunately, the relubrication actually increased the vibration, and the rate of deterioration increased dramatically.

Mill personnel were advised of the change, and monitoring surveillance increased (frequency of data collection can be increased remotely via the Azima system’s interface).

The unit continued to deteriorate and, by the weekend, had reached a stage wherein continued operation was questionable. A recommendation was made to remove the unit from service at the first opportunity. After mutual viewing of the data and trends by plant personnel in conference with the remote analyst, plant personnel decided to remove the unit from service.

A spare fan was placed in service while the motor replacement was undertaken. The spare fan also was equipped with sensors that reported to the Azima system.

Prior to beginning disassembly of the failed fan, the spare was restarted and vibration and performance data was reviewed by the remote analyst, who confirmed that the spare fan was running well and could be expected to give reliable service while the failed fan was repaired. Only after the confirmation of the health of the spare was the failed fan removed from service.

It should be noted that at no time during this episode was the equipment analyst on site at the mill. Problem detection, confirmation of the problem and diagnostics (including the condition assessment of the spare fan) were all conducted remotely with no site visits and no costs incurred.

In the case of the confirmation of the condition of the spare fan, the contractor analyst was in an airport hundreds of miles away and was still able to serve the mill.

It is unlikely that this level of detection, service and continued operation could have been achieved with conventional once/month survey method. Using conventional methods, it is likely that several site visits would have been required with extra costs incurred.

Figure 2. Trend Graph Showing Vibration Increase

Case Study 2:Air Compressor Runs the Last Mile
A centrifugal induction motor-driven air compressor had suffered from poor reliability for some time. Beginning in the spring of 2006, it was equipped with remote monitoring technology. Immediately upon installation of the system, dramatic variations in motor vibration level with compressor load were noted.

Remotely acquired and analyzed vibration data indicated that bearing fits were in poor condition, and that the spacer gear coupling associated with this compressor was partially locked up. A recommendation was made to not yet remove the unit from service, but rather to continue to run and monitor it while preparations for a repair were made.

Data also was provided to the motor repair vendor. The motor repair vendor concurred that the vibration data indicated a problem but that it was likely confined to the coupling.

The recommendation was made by the motor repair vendor to disconnect the coupling, run the motor solo, and take manual measurements to confirm the coupling problem. The coupling was disconnected, the motor was run solo and manual vibration measurements were undertaken.

The motor was actually worse in the uncoupled condition, and before the vibration measurements could be completed, the motor failed catastrophically.

When the motor repair was completed and the unit returned to service, the remote monitoring system was recommissioned and was able to confirm that the motor and compressor were in good condition and suitable for continued service. This condition persisted for several months, with the unit running well and remote monitoring continuing.

Unfortunately, following a mill outage, the compressor motor vibration exhibited a small but unmistakable increase in overall vibration on the motor. The melt shop personnel were notified and the recommendation was made to continue to run the compressor.

Monitoring frequency was increased and alert thresholds adjusted to compensate for the changes. No site visits were required and the increased monitoring and adjustments were accomplished remotely via the system’s interface.

A few weeks later, the adjusted alert levels were exceeded, and automated alerts were issued. No other changes were made.

Within a few more weeks, the steady trend upward in motor vibration continued. Plant personnel were continuously advised as to the deteriorating condition of the unit, as was the motor repair vendor. Finally, the deterioration reached a level where the remote analyst recommended removing the unit from service at the convenient opportunity.

The motor repair vendor sent personnel to the site to take manual vibration measurements on the motor. The manual measurements confirmed the problem and the unit was removed from service and sent out for repair.

A coupling issue and deterioration of the inboard motor bearing was confirmed. Again, no site visits by analysis personnel were required and the plant was able to “run the unit the last mile” without incurring catastrophic failure or mill outages.

Figure 3. Vibration Trend on Compressor Drive Motor

Case Study 3:Remote Monitoring Enables System-Wide Process Optimization
A remote monitoring system can, as in the cases above, greatly expand on the capabilities of conventional rotating machinery vibration analysis. What many do not realize is that remote monitoring technology can also contribute in ways that are not possible with conventional manual monitoring.

The baghouse fans at NUCOR Hickman are vital to the plant. Maintenance of plant productivity, while still staying within permitted emission limits, is essential to plant profitability. Operating costs (in terms of power consumption) for several thousand horsepower of fan drives is significant.

The monitoring system, as applied to the baghouse fans at Hickman, incorporates vibration measurements along with load current measurements. Sampling rates are very rapid, and the baghouse fans at Hickman have an essentially unity power factor. The fan motors are not individually metered at the MCCs.

With the rapid sampling of the remote monitoring system, however, it became possible to get a reasonably accurate measure of fan load and operating cost.

Soon after commissioning the baghouse fan remote monitoring system, is was seen that variations in fan load and fan vibration were, not surprisingly, directly related to tap-to-tap cycles of the EAFs. All of the fans take suction from a common plenum, which is in turn fed by the furnace and canopy ducts.

Data collected by the remote monitoring system allowed observation of the dynamics of fan load as the melt shop underwent normal operation cycles. The ability to average and integrate the load data unexpectedly revealed that significant variation in fan HP load existed not only during furnace cycles but from one fan to another.

The data indicated a several-thousand-dollars-per-month variation in the operating cost of the fans. After data review, it became apparent that there was significant temperature (and thus density and mass flow) variation from one fan to the next.

The variation in load was confirmed by temperature measurements and infrared thermographic observation of the change in duct temperatures over time. Poor distribution in the plenum has been partially corrected by installing turning vanes in the plenum and adjusting damper control strategy.

Material improvements in fan efficiency have been realized as a result of these actions. The remote monitoring system allowed quantification of these issues and the ability to directly measure the effects of corrective action. This would not have been possible with conventional machinery monitoring techniques.

总结

Nucor Hickman is embracing new remote monitoring technologies and integrating them with its existing manual data collection process. By determining the most effective monitoring method for each machine – based on level of criticality, history of problems, and so forth – Nucor has established a comprehensive monitoring program that delivers increased uptime, reduced safety risks, and lower maintenance costs.

By choosing the Azima DLI monitoring and diagnostics system, Nucor has installed a flexible system that supports the integration of data collected both by automated system and manual rounds.

All data is presented via a single, secure Web interface. This enables mill-wide alerts to potential problems and delivers critical data to remotely located Azima analysts for review, analysis and advice.

In addition to providing more comprehensive monitoring, Azima’s remote monitoring solution has reduced the demand on existing resources at Nucor Hickman and frees them up to focus on maintenance rather than data collection. The program has been successful to date at Hickman, with clear successes and benefits, and further expansion is expected.

Acknowledgements
As with most technological pursuits the real reason for success is people. We wish to offer sincerest thanks to the team at Nucor Hickman. The manager of the environmental department, Wayne Turney, and the department supervisor, Dan Bullock, have been particularly instrumental in the ongoing implementation. Dave DaVolt, Rod Wycoff, Claude Riggin, Justin Smith, Ashley Tippet, Tom Wright and Lou Incrocci in the hot mill, cold mill and melt shop have all contributed to the successful demonstration of these technologies. Likewise, success would have been impossible without the support, expert advice and consultation from the Nucor Hickman IT group. Rudy Moser, department manager, and Jim Walmsley, network support, were essential in making the implementation a success.

On the part of Azima DLI, Dr. Ed Futcher and his development team created the tools to make the systems possible, and Heather De Jesús and Dave Geswein, Azima engineering, deployed the system at the mill. Nelson A. Baxter, vice president of diagnostics for Azima, was invaluable in technical support and expertise. Elsa Anzalone, account manager for Azima, made the case for what has been achieved in this project, and her contributions have been invaluable.

For more information on these and other condition monitoring technologies, visit the Azima DLI Web site at www.azimadli.com.