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Why The Confusion? Preventative vs. Predictive vs. Prescriptive

2023-09-20 11:58:50
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Illustration: © IoT For All

Today, you see it a lot with the term AI. It’s nearly impossible now to know what’s real and what isn’t. We certainly see it with people interchangeably using the three terms Preventative Maintenance, Predictive Maintenance, and Prescriptive Maintenance. Let’s put a stop to this.

Humans are very good at spin. It’s like that quote that Mark Twain famously used, “Lies, damned lies, and statistics.” And words are like statistics: people will twist them in a way that creates value for themselves.

Instead, we should use these words as they were meant to be used: to build up more holistic –  and inexpensive – maintenance plans that can benefit both our business and environmental goals.

So, to do our part, we decided to break it down one final time into basic definitions for preventative, predictive, and prescriptive so we can once again have normal conversations using these words – and make better decisions to boost machine health and empower the people who run them. 

Preventative Maintenance: Definition, Shortcomings

Preventative Maintenance means doing something to decrease the risk of a machine failing and causing expensive unplanned downtime. But it is often negatively associated with route-based maintenance – for example, just following the advice of the OEM when the best time is to change your oil on that particular machine.

So, it does come at a cost: it doesn’t protect you from unexpected problems and you may be doing more maintenance than you have to. And while organizations have become great at preventative maintenance, there’s a limit on how far it can take you.

Don’t get me wrong: You should be proactive and do Preventative Maintenance. After all, you should always change that oil – and it’s a step up from Reactive Maintenance where you are just putting out fires as they flare.

But you should strive to do it as smartly as possible (more on this later).

Predictive Maintenance: Definition, Advantages

When it comes to preventative, predictive, or prescriptive, being able to predict a problem is awesome, and people have been predicting problems without technology for a long time. For example, the experienced maintenance guy who can tap the side of a machine with a wrench and say, “Uh-oh, we got a problem here”.

So, Predictive Maintenance is a step up because you know something is up. And in these modern times, instead of some wonderful maintenance guy, you can run some simple algorithms with the data you’ve picked up from sensors – whether they’re handheld or IoT.

Unfortunately, all this data is telling you is that there’s a bad pattern in the data you collect – that something is different, that something is not right. Naturally, this is super useful. You can send in the techs to dig deeper into the problem and hopefully solve it before there’s a failure.

Predictive is not based on real time, but lagging indicators. You might be too late to do something about it – and is it useful if the machine still fails?

Prescriptive Maintenance: Definition, Advantages

Along comes Prescriptive Maintenance to take things a step further. Using more sophisticated AI algorithms on the data collected from IoT sensors, Prescriptive Maintenance tells you what the problem is in real-time, what to do about it, and in what time frame. It’s the full diagnostic package.

Taking Predictive a step further, Prescriptive dooms Predictive to extinction – since Prescriptive gives manufacturers a competitive advantage. And unfortunately, the manufacturing industry is proving itself slow to catch up to this fact.

But here’s the thing: to run a good maintenance program you need all three P’s – at the moment anyway. At one point, Predictive will be fully replaced by Prescriptive.

But until then, likely, you will always have some assets that due to their value, replaceability, or redundancy, you may never be able to financially justify a fully prescriptive solution. And that’s fine.

Avoiding The Spin…

You will always have to do some kind of Preventative Maintenance. But let a Prescriptive program make the Preventative program smarter, letting you know when, for example, you need to change the oil, and when you don’t, instead of just relying on the date or hours run.

In this way, you will be able to make it more efficient – so you’re not changing that oil every 500 machine hours, instead of every 200.

We’ve gone from the Dark Ages to the Age of Enlightenment in terms of maintenance. We’ve got super sophisticated AI backed by human expertise to create viable Prescriptive Maintenance. We are now able to understand the differences between preventative, predictive, and prescriptive.

In terms of value alone, you’ll want to take this next evolutionary step while still always remembering to listen to the algorithm when it tells you to change the oil (or not to).

After all, the algorithm has no time for spin.

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  • Artificial Intelligence
  • Data Analytics
  • Predictive Analytics
  • Predictive Maintenance
  • Preventative Maintenance

  • Artificial Intelligence
  • Data Analytics
  • Predictive Analytics
  • Predictive Maintenance
  • Preventative Maintenance

参考译文
为什么会有混淆?预防性、预测性与规定性
图示:© IoT For All --> 今天,这个词在人工智能(AI)领域随处可见。如今,已经很难分辨哪些是真的,哪些只是炒作。我们确实看到人们把“预防性维护”(Preventative Maintenance)、“预测性维护”(Predictive Maintenance)和“规定性维护”(Prescriptive Maintenance)这三个术语混用。让我们就此止步。人类在“包装”信息方面非常在行。就像马克·吐温那句著名的名言:“谎言、该死的谎言和统计。”而词语就像统计数据一样:人们会以某种方式扭曲它们,以创造对自己有利的价值。相反,我们应该像这些词本来的含义那样使用它们,即建立更全面、更经济的维护计划,这样既能造福我们的企业目标,也能促进环境保护。因此,我们决定再详细解释一次这三个术语的基本定义,以便我们能重新用这些词进行正常交流,并做出更好的决策,提高设备健康状况,并赋能那些操作设备的人。**预防性维护:定义与局限性** 预防性维护是指采取某些措施,以降低设备出现故障和造成昂贵的意外停机的风险。但预防性维护常常与基于路线的维护(route-based maintenance)混为一谈——例如,只是按照原始设备制造商(OEM)的建议,在特定时间更换机油。因此,这确实是有成本的:它无法保护你免受意外问题的困扰,你也可能做了过多的维护。尽管各组织在预防性维护方面已经做得很好,但它的作用是有限的。别误会,你当然应该积极主动地进行预防性维护。毕竟,你应该定期更换机油——这比“被动维护”(Reactive Maintenance),也就是在设备出问题后才去灭火,要好得多。但你应该尽可能聪明地去做它(稍后会详细介绍)。**预测性维护:定义与优势** 在预防性、预测性和规定性维护中,能够预测问题是极好的。而且,人们已经通过非技术手段预测问题很久了。例如,有经验的维护人员用扳手轻敲设备侧面,然后说:“哦不,这里有问题。”因此,预测性维护是一种更进一步的方式,因为它告诉你有问题。在当今时代,你可以不再依赖于某位技术高超的维护人员,而是使用一些简单的算法来处理从手持或物联网(IoT)传感器中获取的数据。不幸的是,这些数据只是告诉你所收集数据中出现了异常模式——即事情不正常。显然,这非常有用。你可以派遣技术人员深入检查问题,并在设备发生故障前设法解决。预测性维护并非基于实时数据,而是基于滞后指标。你可能已经太迟采取行动,而且如果设备最终仍然发生故障,预测是否还有用? **规定性维护:定义与优势** 接下来是规定性维护,它更进一步。利用更复杂的AI算法对物联网传感器收集的数据进行分析,规定性维护可以实时告诉你问题是什么、该怎么做,以及应在什么时间范围内完成。它是一套完整的诊断方案。从预测性发展到规定性维护,前者注定会被后者淘汰,因为规定性维护为制造商提供了竞争优势。不幸的是,制造行业在这一点上进展缓慢。但关键在于:目前要想运行一个好的维护方案,你需要同时具备这三个“P”——预防性、预测性和规定性维护。在某个时候,预测性维护将被规定性维护完全取代。但在那之前,你很可能始终会有一些资产,由于其价值、可替代性或冗余性,你无法从经济上完全证明采用规定性维护方案是合理的。这也没关系。**避免炒作** 你将始终需要进行某种形式的预防性维护。但让规定性维护方案使预防性维护更加智能,例如告诉你何时需要更换机油,何时不需要,而不是仅仅依赖于日期或运行小时数。这样,你就能让它更高效——你不会每500小时就更换一次机油,而是可能每200小时才换一次。我们已经在维护领域从“黑暗时代”走向了“启蒙时代”。我们拥有由人类专业知识支持的超级复杂的AI,用于构建可行的规定性维护方案。我们现在已经能够理解预防性、预测性和规定性维护之间的区别。仅从价值来看,你希望迈出这下一个进化步骤,同时始终牢记,当算法告诉你该换机油(或不该换)时,要倾听它的建议。毕竟,算法可没时间去玩虚的。 推文分享 邮件分享 预测分析 预测性维护 预防性维护 --> 人工智能 数据分析 预测分析 预测性维护 预防性维护
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