Every business needs their essential equipment to operate at peak efficiency and utilization to realize their return on capital investments.
This equipment could range from aircraft engines, turbines, elevators, or industrial chillers that cost millions to purchase.
For maintenance of this equipment, businesses follow the approaches below:
Cross posted from : InfoQ
Digital Twins technology brings the exact replica in digital format of a process, a product, or a service.
Basically, it takes real-world data about a physical object or system as inputs, and produces outputs in the form of predications or simulations of how that physical object or system will be affected by those inputs.
Some of the most common use case across the industry is given below:
According to a recent IoT implementation survey by Gartner, Inc Digital Twins are entering mainstream use:
13% of organizations implementing Internet of Things (IoT) projects already use digital twins, while 62% are either in the process of establishing digital twin use or plan to do so.
There are different types of Digital Twins, to choose the right digital twin, you would need to understand the needs and its benefits that you can get out of it.
Type of Digital Twin | Use Case Scenario |
Component Twin Ex. Rotor, Blade | Helps Field Services/Technicians to continuously monitor and offer predictive maintenance insights while reducing equipment downtime (planned and unplanned) and enable service-based business models. |
Asset Twin Ex. Turbine, Motor | Helps Marketing & Sales team to gather knowledge on customer’s preferences and actual usage of their product and can tailor messaging to drive revenue. |
System / Unit Twin Ex. Aircraft, Crude Unit | Helps Product designers, architects, and engineers to improve future product versions and engineering models to optimize product performance and efficiency, accelerating time-to-market. |
Process Twin Ex. Manufacturing Process | Helps Management to get new operational data feeds into production and planning models thus paving way for strategic insights, recommendations, and road maps. |
Sensors are the heart of any measurement, control, and diagnostic device. Device telemetry is collected using the smart sensors available on the hardware/software environment and then used to create the digital twin model of the physical equipment.
All of the data is then aggregated and compiled to generate actionable information. The digital twin model is then continuously updated to mirror the current state of the physical thing. It can then be used to effectively model, monitor, and manage devices from a remote location. It also enables continuous intelligence & estimated time for the next needed maintenance, which the maintenance system can use to schedule at the optimal time.
We can see in detail how to build digital twins in the sections below.
Not all use cases or business problems can be effectively solved by predictive maintenance using a digital twin. Here are important qualifying criteria that need to be considered during use case qualification:
Now that we have see how to qualify use cases for predictive maintenance using the digital twin approach, the next step is to explore the various options to build/deploy them.
This section summarizes the different frameworks available for building digital twins.
1. Eclipse Ditto – Digital twins can be built by leveraging pre-built capabilities, for example, routing requests to hardware, applying policies, etc.
2. Swim OS – Integrated solution for building scalable, end-to-end streaming applications.
3. iModel JS – Platform for creating, accessing, leveraging, and integrating infrastructure digital twins.
This section lists the various public cloud vendors offering digital twins.
1. Azure Digital Twins
Key concepts:
2. AWS Digital Twins
3. IBM Digital Twins
This section lists the various industrial vendors offering digital twins.
1. GE Predix concentrates mostly on asset-centric digital twins.
2. Bosch’s digital twin solution Bosch IoT Things has detailed technical documentation, including developer guides, demo applications, and hosted dashboard.
3. Siemens MindSphere platform for developing new digital business models for industrial companies.
In this walkthrough section, we are going to build a digital twin model of an Intel NUC kit so that we can:
Some of the parameters that determine the health of the Intel NUC Kit are (this forms the basis for building digital twins):
For building a digital twin of the Intel NUC kit, we are going to leverage the Eclipse Ditto framework that enables us to work with, and manage, the state of digital twins.
Following are the key capabilities of the Ditto Framework:
The first step is to define things and features. Things are generic entities and can be used to depict multiple features belonging to a thing. For example, physical devices like lawnmowers, sensors. In the below example, we are going to treat the entire Intel NUC Kit as a Thing.
Feature is used to manage all data and functionality of a thing that can also be grouped based on technical context. In the below example, we have CPU, Memory, etc.
Once we have defined Things/Features, we can now use any of the client SDK (ex. Python) to pull the respective sensor values and publish them to Eclipse Ditto.
Figure 12: HDD Temperature Sensors – Sample values
Figure 13: SSD S.M.A.R.T Sensors – Sample values
Figure 14: Intel NUC Board from Sensors – DetectBelow is a sample JSON format that is being used to represent the thing (here it Intel NUC Kit)
Each thing has a unique thingId and set of features that we discussed in the earlier section. We can also have attributes that describe the thing in more detail.
Also, we can find access control lists on who can perform read/write or use administer permissions.
{
"thingId": "org.eclipse.ditto.example:demothing",
"acl": {
"ditto": {
"READ": true,
"WRITE": true,
"ADMINISTRATE": true
}
},
"attributes": {
"manufacturer": "Demo Manfacturer",
"hostname": "Demohost"
},
"features": {
"TemperatureSensor": {
"properties": {
"temperatureValue": 20.5,
"lastUpdate": "2019-10-16 15:07:31.436733",
"samplingRate": 1
}
}, …..
}
}
}
The features section holds the current temperature value of the Temperature Sensor.
Once we have the data about features/attributes from the sensors we can use Ditto APIs to retrieve or modify the state of Thing/Feature/Attribute. All the state and properties can be read, updated, and collated.
There is also an option to route a command/message towards an actual device.
Now we have the time-series data available and stored in our Digital Twin Server, our next step is to build an ML model based on the data collected to predict failure based on core attributes such as CPU, Memory, Disk Space, connection state, or the performance of external interfacing systems.
Data for predictive maintenance model is time series data collected from the digital twin model.
Classification approach – predicts whether there is a possibility of failure in the next n-steps. Suited for Greater accuracy with less data.
Sample Data Set for Intel NUC core attributes:
This classification model is based on a decision tree classification approach where it offers a flowchart-like tree structure where an internal node represents a feature (or attribute), the branch represents a decision rule, and each leaf node represents the outcome.
After many iterations, below is the model’s confusion matrix & precise score. A highlight of the decision tree classification approach is it learns to partition based on the attribute value & also partitions the tree in recursively.
From the confusion matrix and our precision score, we are able to achieve ~96% accuracy in the determination of the possibility of failure.
Regression approach – predicts how much time (RUL) is left before the next failure. We need more data but it provides more information about when the failure will happen.
In order to predict the RUL for each engine, we are classifying it like below.
Category labeled as 2 is the most economically valuable. If we predict this class with good performance, it will permit us to operate an adequate program of maintenance, avoiding future faults and saving money.
Next step is to transform timeseries data to Recurrence Plots and run the model.
From the below confusion matrix, we can see that our model can well discriminate when the system is close to failure (2 labels: <16 cycles remaining) or when it works normally (0 label: >45 cycles).
We are satisfied to achieve the result with the 2D CNN model for the prediction of class 2 — i.e., near to failure.
Congrats! we now have built Digital Twin and ran a predictive maintenance model to predict the failures.
This section lists some of the challenges that should be handled while building digital twins.
Businesses are moving towards developing a predictive maintenance model using digital twins that optimizes the maintenance cycle with the advances in IoT space, extending the life of the part by reducing unplanned maintenance and labor costs. By using digital twins and the predictive maintenance strategy, companies gain cost savings and strategic advantages in the industry.
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