From nameplate via digital twin to asset health

It is frequently said that data is required in order to create valuable insights, but before this can happen, some important steps must be taken.

The typical domain of IIoT applications is manufacturing and production. Here the focus is on optimising installed assets, especially to increase efficiency and availability. The ultimate goal is to predict future asset behaviour based on historical data — often described as predictive maintenance or asset health monitoring. The majority of today’s assets in processing plants already deliver much more information than just a single process value. This additional information can range from more process values to self-diagnosis about the asset’s health or even the prediction, based on internally diagnosed device parameters, of potential problems that might occur in the near future.

This kind of information is often locked into the asset itself and can only be retrieved locally. The process automation plants that exist today are 5, 10 or even 20 years old and asset diagnostics was often not considered when they were planned. Although assets are replaced and new technology finds its way into existing plants over years of operation, the original integration of these assets into a PLC or DCS is rarely touched.

As a result, all the new device features and functions are not accessible without direct interaction with the asset itself. By changing this situation, the digitisation and interconnection of all operational assets offers enormous potential for cost savings and optimisation in the process industry.

Accessing asset information in existing plants: unlocking the hidden potential

While the philosophy of IIoT is focused on unlocking the hidden potential of connected devices, that of existing plants is often exactly the opposite: locked down systems, with no means of connecting the installed assets. Furthermore, in order to make use of the features and functions of an asset, an overview about what is actually installed in the plant is required: where an asset is installed and what it can actually offer — although not all data provided is always of use.

When talking about IIoT, it is frequently said that data is required in order to create valuable insights. Although this is true, it is often forgotten that before this can happen, some important steps must be taken. Prerequisites to analysing gathered data are knowing who or what the data provider is and what kind of data is to be accessed.

Without knowledge of the installed base and the data it will provide, analysis is practically impossible. In plants that have been around for a few years in particular, it is often not clear what the current installed base looks like.

• Who manufactured the devices installed?
• How many different device types are there?
• Are the devices still available or are some of them already obsolete?
   

Therefore, the first step into IIoT actually includes manual work: creating a list of all installed assets in a plant with at least some basic information such as manufacturer, asset type, location and a unique identifier (usually the serial number). Traditionally, this is done by sending a technician into the plant with a pen and paper to document the serial numbers, the manufacturer, the asset type and other relevant information such as location, etc. Afterwards the data is collated in a list and then the real work starts.

Normally, reliance on existing plant documentation is not recommended: documentation on installed assets is often outdated or incomplete and can hardly serve as a basis for information gathering. There is usually no other way than to go through the system physically, to manually identify, capture and create a new database. Obviously, this method of creating the database requires a great deal of time, effort and human resources. In addition, neither the data consistency nor the validity of the data is guaranteed. Although documenting the installed base is a crucial step towards the world of data analytics and the IIoT, for many the entry hurdle is too high, as the time and money spent on manually creating a simple list of installed assets outweighs the benefits.

Automatic database creation and digital twins: it only takes a few steps

With the technology available today, this manual data gathering entry hurdle can be substantially lowered. Mobile devices can be used to create a device database with the help of a smartphone app. It takes just a few steps and can be done in a matter of seconds for each asset.

For the unambiguous identification of the devices, a combination of the serial number and manufacturer is usually used. These can be found on the respective markings of the devices, which may comprise metallic identification plates, QR codes or digital RFID tag labels, etc. After identification of the asset and entry into the app, further information can be attached to its ‘digital twin’. This might be as geolocation (using the GPS functionality of the mobile device, for example), the tag of the asset, criticality and similar information, comments as well as photos and drawings of the instrument and its location. From tests with participants of different ages and education levels, the average time it takes to gather all this information and create the database entry for a single asset can be less than a minute. Obviously, in a plant with hundreds of assets, this still might become a tedious task.

Since the arrival of digital communication protocols such as HART, Profibus and FOUNDATION Fieldbus, the goal has always been to provide the user with more information from the field and unlock the data and features that the manufacturers built into their devices. All these protocols provide a standardised means of reading the electronic nameplate of connected assets, so by using a modern edge device there is now a means of accessing this information and transporting it to the cloud. This automates a major part of the manual work, massively reducing the effort required to create a digital twin of the installed base.

In field trials, automatically generating an asset database in this way that included more than 800 assets in a single plant was found to be completed in less than four hours — from the installation of the edge device to the creation of the last digital twin.

But what happens next? How can the database be populated with additional data? Regardless of whether the database is created manually, via an app or via the edge device, today’s technology allows it to be connected to other databases, such as the manufacturer’s asset information system. This can mean device-specific documents such as manuals, certificates, etc are immediately at hand.

This is very important, as in today’s process automation plants the installed base often contains a large number of field devices from different manufacturers. To operate and maintain these plants economically, it is essential to have a comprehensive plant asset management system. Studies have shown that up to 70% of the time required to complete a maintenance job is spent on searching for information: only 30% is spent on actually doing it. Considering that in older plants up to 30% of the installed base could be already obsolete, not having up-to-date information on its current status represents a large obstacle to the smooth running of the plant.

A big step forward towards successful asset management would be to simply make the user aware of the obsolescence situation. Luckily, modern asset management systems can provide this information even in a mobile app. Here the obsolescence information is automatically generated in the database by cross-referencing the digital twin with data from the manufacturer’s database. Having all this information at hand not only increases the efficiency of maintenance technicians, but also reduces the risk of faulty or inefficient maintenance, as the correct information is provided to the right person. This does, however, require a well-maintained and comprehensive device information database, which can be created as described above.

Asset health: from static to dynamic asset information

Once the connection to the field has been established (via the edge device) and a comprehensive overview on the installed assets is available, the next step can be performed: the visualisation of asset health. Many modern field devices are able to output diagnostic values and device-specific trend parameters.

This asset data can be visualised to give users an indication of the availability of their assets. Gathering this information over a longer period of time and cross-referencing it with other process variables or external factors can then ultimately be used in a predictive maintenance application.

This is the logical step from static to dynamic asset information. Collecting and trending asset health over specific periods of time and storing this information in a database can ultimately lead to a collection of data that can then be used to forecast an asset’s health.

Of course, all these additional asset management features and functions should never compromise the security and integrity of the actual process. By adding a bypass channel (through the edge device and an Ethernet or fieldbus gateway) to the asset management database, the PLC or DCS remains untouched. This offers multiple benefits:

• No additional programming of the PLC/DCS is needed to unlock the asset features.
• Existing plants can be easily retrofitted without the fear of interfering with the existing process.
• A bypass establishes another level of security, as asset management data is clearly separated from process data.
   

Today’s field devices often have the necessary connectivity to also transmit data directly to the database already built in. This can be done by connecting through Wi-Fi, Ethernet technologies or even a mobile connection.

Digital service program for IIoT applications. For a larger image click here.

Security aspects

In order to understand the relevant security aspects, it is necessary to take a look at the network architecture. This will give the entry points for the security discussion and show critical points of interest. The data flow starts in the field at the instruments. Via interfacing devices like edge devices these data are transmitted into the cloud, where they are then transformed into information. There, additional data sources may be injected to create even more information. These can be vendor systems or user environments such as engineering tools or ERP systems.

The connection to the asset management database has to be established in a secure manner. The edge device is located behind the company’s firewall, but as an additional security measure, the connectivity between edge device and asset management database should be a one-way street. In this way there is no direct connection possible between the asset management database and the field network.

As security, trust and compliance are sensitive topics, a quality audit is essential. When the decision is to go for an IIoT offering, an accountable quality assessment of cloud services through a transparent and reliable certification process should be part of the process.

Any quality audit needs to consider different frameworks, laws and regulations that should include at least:

• ISO 27001: Information Security Management
• IEC 62443: Security for industrial automation and control systems
• Contract and compliance
• Data privacy
• Operational processes
• Software-as-a-Service
• ISO 20000: Service Management System

Conclusion: Do things better

The IIoT offers innovative ways to do things better and utilise assets that already exist. The installed base of a system can be captured and analysed using current and historic data. Asset information in the field is often recorded with a mobile smart device using a scanner app that reads an RFID chip, QR code or tag — alternatively, the asset information can be captured automatically by an edge device. All data is saved to the cloud, visualised on a dashboard, and from there further asset management capabilities can be realised.

Top image: ©stock.adobe.com/au/behindlens