《洞云书屋》每周五介绍适用于中小企业物联网创新技术。 'dy_book' introduces the innovative internet of things technologies applied for SME every Friday. （注：本文是译文，original author: Abhinav Khushraj) In today’s competitive industrial world, predictive maintenance (PdM) is no longer a nice-to-have; it has become a necessity. Traditional PdM methods have several limitations. However advancements in wireless, cloud and AI technology are disrupting the way PdM has been done in recent decades. Companies are making use of these technologies to offer an end-to-end, easy-to-deploy PdM solution at extremely affordable price points. This trend is called the Industrial Internet of Things (IIoT). 在当今竞争激烈的工业世界里，预测性维修（PDM）已经不再是有它更好，而是一种必需品。传统的PDM方法有几个局限性。然而，无线、云和人工智能技术的进步颠覆了PDM近几十年来的工作方式。各公司正利用这些技术，以极为合理的价格点提供端到端、易于部署的PDM解决方案。这种趋势被称为物联网产业（IIoT）。 Run-to-Failure and Preventive Maintenance 运行故障和预防性维护 Traditionally, most factories have adopted either a reactive (run-to-failure) or a preventive maintenance (PM) model because of the high costs of predictive maintenance. In a reactive model, a machine is repaired or replaced only in the event of a failure. This leads to unplanned downtime and significant repair costs. Ideally run-to-failure is recommended for the least critical assets which have very little impact on operations. Under a preventive maintenance approach, maintenance teams create predefined repair schedules without considering the actual condition of the machine. Work order schedules are often based on manufacturer’s recommendations. These schedules can be unreliable because the actual operating condition of the machine may be different from the recommended operating condition. Also, improper PM can lead to unintended malfunction. In either of these approaches, the focus is not on assessing and predicting machine condition. 传统上，大多数工厂都采用反应式（故障运行）或预防性维护（PM）模式，因为预测维护成本高。在反应模型中，只有在故障发生时机器才被修理或更换。这会导致非计划停机和显著的修理费用。理想情况下，建议对最不关键的资产进行故障处理，而这些资产对操作的影响很小。 在预防性维护方法下，维护团队在不考虑机器实际情况的情况下创建预定义的修理计划。工作顺序表通常基于制造商的建议。这些时间表可能是不可靠的，因为机器的实际操作条件可能与推荐的操作条件不同。此外，不适当的PM可能导致意外的故障。 在这两种方法中，重点都不是评估和预测机器状态。 Traditional Predictive Maintenance 传统的预测性维护 Predictive Maintenance is referred to the maintenance approach of understanding the underlying “health” of a machine to determine if a corrective action is required. To determine the health of the machines, sensors are used to measure various machine parameters such as vibration, temperature and ultrasound. In continuous monitoring, wired sensors are installed on the machines are connected back to the asset management software. However, this is a very expensive solution and is often reserved for the small percentage of the most critical machines. For the balance of plant equipment, predictive maintenance has typically been conducted using a walk around program. A technician goes around the plant periodically and collects sensor data using a data collector. However, this traditional approach of once a month data collection is not truly predictive as it comes with several limitations. 预测性维护指的是理解机器的基本“健康”，以确定是否需要采取纠正措施的维护方法。为了确定机器的健康状况，传感器被用来测量各种机器参数，如振动、温度和超声波。 在连续监测中，安装在机器上的有线传感器连接到资产管理软件。然而，这是一个非常昂贵的解决方案，通常只保留在部分最关键的机器中。 对于工厂设备的平衡，预测性维护通常运用运行程序。技术人员定期在工厂周围使用数据收集器收集传感器数据。 然而，这种传统的每月一次数据收集方法并不具有真正的预测性，因为它有几个局限性。 ONCE-A-MONTH DATA COLLECTION NOT EQUAL TO REAL PREDICTIVE MAINTENANCE 每月一次的数据收集不等于真正的预测性维护 Here are the main reasons why a walk around program does not constitute a truly predictive maintenance solution. 这里有一些主要的原因，为什么运行计划不构成真正的预测性维护解决方案。 1. Failures between data collection rounds: In walk around programs, even in well run ones, technicians only collect data once every 30 or 90 days. The problem is that any defects that occur after the last data collection cycle remain undetected until the next data collection cycle. As a result, machines remain prone to failure which results in unplanned downtime. 2. Inconsistent data collection: In a walk around program, technicians may not place the sensors accurately and consistently. This leads to faulty data and inaccurate assessment of machine’s health. 3. Variable operating conditions: One of the most important things to consider during data collection is that the operating condition of the machine needs to be the same every single time. However, this is very hard to do. So when the analyst sees “high” sensor data they may inaccurately conclude it to be a machine fault. However, it could have just spiked due to increased loads! 4. Inaccessible machinery: Often machines are not easily accessible, either because there are safety concerns or they are behind a cage. So technicians end up not collecting data on such machines allowing them to fail 5. Manual Analysis: All of the collected data needs to be analyzed manually. It’s not real time and it’s hard to scale manual analysis when you are dealing with hundreds and thousands of machines. 1. 数据收集周期之间的故障：在运行程序中，即使是运行良好，技术人员只能每隔30或90天收集一次数据。问题是，在下一个数据收集周期之前，最后一个数据收集周期发生的任何缺陷都未被发现。结果，机器仍然容易发生故障，导致非计划停机。 2. 不一致的数据收集：在一个运行程序中，技术人员可能不会准确一致地放置传感器。这导致了错误的数据和对机器健康不准确的评估。 3. 可变的操作条件：在数据收集过程中要考虑的最重要的事情之一是机器的操作条件每次都是相同的。然而，这是很难做到的。所以当分析师认为传感器数据“高”，但他们并不一定认为它是一个机械故障。然而，由于负载增加，它可能会突然增加！ 4. 难以接近的机器：通常机器不容易进入，要么是因为有安全隐患，要么是在笼子后面。因此，技术人员最终没有收集到这些机器上的数据，从而导致它们的失败。 5. 手动分析：所有收集到的数据都需要手动分析。这不是真正的实时分析，在处理成百上千的机器时很难同步进行手动分析。 Up until recently, most industries relied on walk around programs because continuous monitoring was extremely expensive. But not anymore! Technology Trends are Paving the Way for a PdM Revolution Industrial IoT is making the transformation to continuous condition-based monitoring and predictive maintenance incredibly easy and affordable. There are four key trends driving this change: 直到最近，大多数行业都依赖于运行程序，因为连续监控非常昂贵。 技术发展正在为PDM革命铺平道路 工业物联网正在转变为基于连续状态的监测和预测维护，非常简单而且可负担得起。推动这一变化的主要趋势有四种： 1. Wireless Connectivity: With the advent of cellular and Wi-Fi connectivity, wireless connectivity is pervasive these days enabling wireless sensors to sense and transmit any machine parameter. Sensors no longer need to be wired which makes data collection automatic and cost effective. 2. Inexpensive Sensors: The smartphone revolution has really driven down the price of sensing. Miniaturized low-cost MEMS sensors are readily available and more affordable than ever before. 3. Cloud Computing: Cloud computing has become robust, highly secure and inexpensive. This gives industrial plants the ability to start small and scale up as their needs increase. 4. Artificial Intelligence (AI): Artificial Intelligence technologies have finally become mainstream. Monitoring time series sensors data is impossible to do manually. AI can serve as an effective “assistant” to analysts. 1. 无线连接：随着无线和Wi-Fi连接的出现，无线连接现在无处不在，使得无线传感器能够感知和传输任何机器参数。传感器不再需要有线，这使得数据收集自动化有了成本效益。 2. 廉价的传感器：智能手机革命确实降低了传感器的价格。微型低成本MEMS传感器是现成的，比以前更便宜。 3. 云计算：云计算变得强大、高度安全和廉价。这使工业工厂有能力从小型化开始并根据需求不断扩大。 4. 人工智能（AI）：人工智能技术终于成为主流。监控时间序列传感器数据是不可能手动完成的。人工智能可以作为一个有效的“助理”分析师。 While in isolation each of these technologies bring some value, when combined together into a single solution, they have the power to transform the industrial world. 虽然孤立，每一种技术都会带来一些价值，当它们结合在一起成为一个单一的解决方案时，它们就有能力改变工业世界。 Future of Predictive Maintenance is Here 预测维护的未来 Modern solutions involve wireless sensors that continuously collect sensor data. The sensors keep a close watch on the key parameters of the machine such as vibrations or temperature. Periodically, or when a threshold is exceeded, the sensors wirelessly send the data to the cloud through a wireless gateway such as WiFi, Bluetooth or various other wireless technologies available on the market. Once the data reaches the cloud, it is processed to extract the key parameters that are necessary to determine machine health. Users can view the trending of these critical parameters and set thresholds on a web or mobile dashboard. 现代解决方案包括不断收集传感器数据的无线传感器。传感器密切监视机器的关键参数，如振动或温度。周期性地，或者当阈值超过时，传感器通过无线网关（如WiFi、蓝牙或市场上的各种其他无线技术）无线地将数据发送到云。一旦数据到达云，就进行处理以提取确定机器健康所必需的关键参数。用户可以查看这些关键参数的趋势，并在Web或移动仪表板上设置阈值。
Infographic courtesy of Petasense
The more sophisticated solutions go further by including an analytics module that can analyze all of the sensor data in real time and automatically. Given all the recent advancements in AI such as deep learning and use of GPU chips, AI-based analytics are able to detect machine anomalies, do diagnosis and provide prognosis. Future advancements in this space involves combining multiple sensor parameters alongside historical work order information. And all of this is happening in real time!
The future holds the promise of completely automating predictive maintenance. No more manual data collection, no requirement to be onsite near the machine. And with AI technology, it really helps scale the job of a machine expert. AI can keep a close watch on all machines and flag the problems ones. An expert needs to only review the health of the machines exhibiting anomalies. AI completely replaces the need for setting up and maintaining manual alarms. These alarms are hard to set accurately and as a result users end up losing confidence in a system. But with AI, alarms are data driven – data that is hard for humans to process but easy for computers to digest.
Historically, adopting advanced technology involved a huge upfront capex investment which often resulted in limited adoption of the technology. However, with the advent of these new technologies, that’s no longer the case. Sensors and software are becoming increasingly inexpensive. Further, industries can start small by monitoring a few critical machines and then scale to the entire facility gradually.
Most importantly, all this cool technology translates to tangible benefits. Industrial plants are able to eliminate unplanned downtime, lower preventive maintenance costs and reduce unanticipated repair costs.
更复杂的解决方案还包括一个分析模块，它能够实时、自动地分析所有传感器数据。鉴于人工智能的最新进展，如深度学习和使用GPU芯片，基于人工智能的分析能够检测机器异常，进行诊断和提供预测。未来在这方面的进展涉及多传感器参数结合历史工作秩序信息。所有这些都是实时发生的！
未来有望实现完全自动化的预测性维护。无需手动数据采集，无需现场靠近机器。借助AI技术，它确实有助于扩大机器专家的工作量。AI可以密切监视所有机器，并对问题进行标记。专家只需检查表现异常的机器的健康状况。AI完全取代了设置和维护手动报警的需要。这些警报很难准确设置，结果用户对系统失去信心。但是在人工智能中，警报是数据驱动的，这些数据对人类来说很难处理，但便于计算机消化。
从历史上看，采用先进的技术所涉及的巨大的前期投资往往限制技术的应用。然而，随着这些新技术的出现，情况不再如此。传感器和软件正变得越来越便宜。此外，行业可以通过监测一些关键机器开始小型化，然后逐步扩展到整个设备。
最重要的是，所有这些超酷的技术转化为有形的利益。工业工厂能够消除计划外停机，降低预防性维护成本，减少未预料到的修理费用。
Conclusion
结论
Everything from the food we eat to the fuel that powers our cars, from the medicines we consume to the electricity that lights our homes is powered by factories. These factories have millions of machines that turn, churn, mix, grind, and transport things. Wireless predictive maintenance with AI-based analytics make it possible to monitor, analyze and predict the health of these machines that are driving our everyday lives.
We see a future where preventive maintenance is entirely replaced by predictive maintenance. Repairs or corrective action are only required when predictive technologies indicate failing health of machines. With clear advantages over traditional approaches, wireless predictive maintenance is poised to transform maintenance forever.
从我们所吃的食物到汽车的燃料，从我们消耗的药品到家用照明。这些工厂有数以百万计的机器在旋转、搅拌、混合、研磨和运输东西。基于人工智能分析的无线预测维护使人们能够监视、分析和预测这些驱动我们日常生活的机器的健康状况。
我们预见未来，预防性维护完全被预测维修所取代。只有当预测技术表明机器的健康状况不好时，才需要进行修理或纠正措施。与传统方法相比，无线预测维护具有明显的优势，可以随时进行维护。
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