In this series, we covered different topics on predictive maintenance. If you’re searching for similar topics, you may come across the term “digital twin.” In this video, we’ll discuss what a digital twin is, why you would use digital twins, and how they can be created.

预测性维护是数字双胞胎的关键应用领域之一。数字双胞胎被定义为操作中真实物理资产的最新表示。让我们使用一个例子来了解这意味着什么。假设我们在不同地点的网站上有井，我们操作多个泵以从地面提取油和气体。在数字双胞胎的定义中，我们将作为资产所指的资产可以是诸如泵的阀门的系统的组件，或者它可以是系统，泵本身，或者它可以是系统的系统有多个泵的井网站。在这里，我们假设我们的资产是泵。可以通过创建一个模型来实现泵的最新表示，该模型将与来自泵的传入数据更新以表示其当前状态。在我们进入如何使用不同类型的模型创建数字双胞胎之前，让我们谈谈您从数字双胞胎中获得的好处。

我们说有多个泵运行在每一个well site. We know that these pumps contain parts such as valves, seals, plungers, that are very expensive. Therefore, we want to prevent failures by predicting them in advance, which, in turn, will help us reduce downtime. We may also want to identify faults that develop in this system and get insights into what parts may need repair or replacement. This will also help us better manage our inventory. All the pumps may have similar functionality; they can even be produced by the same manufacturer. But different operating conditions will affect how efficiently these pumps will work. We want to be able to monitor the whole fleet, simulate future scenarios, and make comparisons with the aim to increase the overall efficiency of the fleet. This will help us with operational planning.

Now that we discussed what a digital twin is and why you would use it, let’s discuss how you can create it. The modeling method we need to use really depends on our intended use of the digital twin. For example, if we want to predict the remaining useful life of the pump for optimizing maintenance schedules, then we can use a data-driven model such as the ones we discussed in the previous videos. Our knowledge on the type of the data from the pump will determine which model we’ll be using. For example, if we don’t have complete histories from the fleet but know a safety threshold, then we can use a degradation model to estimate the remaining useful life of the pump. This degradation model is constantly updated using the data from the pump measured by different sensors such as pressure, flow, and vibration. If our intended use of the digital twin is different—let’s say we want to simulate future scenarios and monitor how the fleet will behave under those scenarios—then we can use physics-based modeling. An example would be a physical model like this one, which is created by connecting mechanical and hydraulic components together. This model is fed with data from the pump and its parameters are estimated and tuned with this incoming data to keep this model up to date. Using this model, you can inject different types of faults and simulate the pump’s behavior under different fault conditions. Similarly, a Kalman filter can be also used as a digital twin, which can model the degradation of the pump as a state and periodically update this state to represent the current condition of the pump. These are some examples of how a digital twin can be created. Based on the intended use, the digital twin can also be a combination of these models.

既然你知道如何创建一个数字双胞胎，你可能会想知道我们需要为舰队创造多少个数字双胞胎。对于我们需要创建一个独特的数字双胞胎的每个资产。这意味着对于不同井网站的每个泵，我们需要创建一个唯一的数字双单，该双胞胎已被初始化为特定的泵的参数。基于预期用途，泵可能有多个数字双胞胎。例如，如果要进行故障预测和故障分类，则需要创建提供这些不同目的的不同型号。

All these digital twins are connected through the Internet of Things and they share information. An important feature of a digital twin is that it captures its real asset’s history. Earlier we mentioned that the digital twin model is being updated periodically to represent its real asset’s current state. Over time, these past states become the asset’s history. The type of information included in this history might differ based on how we’re using the digital twin and what’s captured in the current state. For example, if we’re using the digital twin for fault classification, then the history captured by each digital twin can be the operational data from the specific pump and its healthy and faulty state. In the future, the operational data from one pump can be compared to these digital twin histories to understand how other pumps behaved under similar faults and how it affected the fleet’s efficiency.

Being able to monitor the whole fleet using digital twins also brings other advantages in terms of planning operational events and improving maintenance strategies. Imagine a situation where one of the pumps is expected to fail soon. Using digital twins, you can assess how this will affect the efficiency of the fleet and what it will cost to you. Based on this analysis you can either order replacements and run your pump in a suboptimal state until you get the new parts. Or you can pay more for shipping and get the parts immediately to schedule maintenance as soon as possible.

随着数字双胞胎帮助您了解其资产的历史，它们也可以帮助您使用未来的规划。您可以使用数字双胞胎模拟数百个未来的场景，以了解天气，舰队尺寸或不同的操作条件等某些因素会影响性能。这将帮助您管理您的资产并通过提前通知您的维护人员对预期失败的信息进行优化操作，因此他们可以规划未来的维修和更换。

In summary, a digital twin is an up-to-date representation of an asset in operation. The data captured from the asset and the environment are periodically sent to the digital twin, which is being updated with this data and tuned to its real asset. Every individual asset has a unique digital twin that also captures the history of its real asset. The modeling method we need to use to create a digital twin is driven by our intended use. By using digital twins, you can predict failures in advance and reduce downtime, better manage spare part inventories, monitor and manage your fleet, do what-if simulations, and optimize operations.

For more information on designing predictive maintenance algorithms with MATLAB and Simulink, don’t forget to check out the product page. Below this video, you’re also going to find other products you can use to design models for creating digital twins.