Industrial AI systems are often built around a simple goal: use data from processes to improve decision-making.
In practice, this objective needs to take into account a variety of factors. From heterogeneous equipment, legacy systems, data formats and the requirement to maintain existing processes.
Industrial AIoT systems must therefore grapple with the full chain of information from its physical origins, to its analytical impact.
- Understanding the Industrial AIoT Data Pipeline
An industrial AIoT system can be seen as a set of distinct but connected layers.
1: Data acquisition
Using sensors and connected equipment, RFID readers and other technologies to identify and observe physical properties.
The specifics of the technologies will vary depending on the use case. Different aspects of a process need to be monitored such as equipment conditions, inventory and production progress.
The first engineering challenge is deciding what needs to be observed, and how frequently.
2: Edge processing and connectivity
Industrial data may need to be processed or filtered closer to its origin.
Edge computing can allow for certain data processing capabilities to be applied dependant on latency, connectivity and other factors in the environment.
Designers also need to think about the communication infrastructure, device management, security and behaviors in the case of disconnection.
3: Data integration and context
Raw sensor data is rarely useful on its own.
A temperature reading is only valuable when linked to the equipment it is monitoring, and the time at which it was recorded. Similarly, an inventory movement is only meaningful when it has been associated with an asset, and its subsequent movements.
Industrial systems may need to reconcile different sets of equipment identifiers and temporal information before combining data.
This layer is often critical, as even the most sophisticated AI model can fail if the information it receives is incorrectly associated.
4: Analytics and AI
Once relevant information has been gathered this can be analysed and processed using analytical systems. This can range from detecting anomalies and patterns, to forecasting and predictive maintenance.
Models can be selected based on the requirements, data available and tolerance for incorrect outputs.
A rules-based approach may be more appropriate in some situations, while statistical methods or machine learning may be required in others.
5: Operational integration
Finally, information needs to be integrated to the systems and people who can act on it. An alert is only valuable if the person or system receiving it can easily identify the equipment that triggered it, and take appropriate action.
Operational systems can be integrated with analytics to make this information available in a manner appropriate for the recipient.
- Common Engineering Challenges
Data quality and missing observations
Observation data may have missing, anomalous, or inconsistent values. Industrial data pipelines should take this into account, rather than assuming that all observations are valid.
Validation rules, monitoring and other mechanisms should be in place to deal with potentially faulty data.
Interoperability with existing equipment
Industrial environments often have equipment and systems from multiple different suppliers.
The integration aspect of industrial systems needs to account for existing interfaces, supported standards or data formats. A solution may need to be engineered for a specific set of equipment or processes.
Latency and system availability
Not all applications have the same degree of tolerance for system delays. Some systems can tolerate batch processing, others - such as in safety-critical environments - require less latency or local processing capabilities.
The requirements should be clearly defined, and appropriate infrastructure selected based on the consequences of failures or delays.
Security and access control
Adding connectivity to physical assets and systems introduces additional security considerations.
Implementations need to account for device security, authentication, segmentation, access control and monitoring. Critical safety functions should have appropriate control and oversight.
- Start With a Measurable Use Case
A common pitfall when building industrial intelligence systems is designing a generic platform without addressing a specific application.
A useful starting point is to identify a bounded use-case and define its success criteria.
For example, a team investigating equipment downtime may need to:
Decide which equipment and events need to be monitored.
Establish which sensor readings and maintenance logs are relevant.
Set a baseline using historical or observed data.
Evaluate the usefulness of the proposed analytics model.
Assess results against relevant operational indicators.
Evaluate the requirements for integration, reliability and deployments before scaling.
Possible measures may include the time required to investigate an incident, or the quality of event records. A useful application may focus on improving an operational metric that the project is intended to address.
The selected measure should reflect the problem the application is trying to solve, rather than a generic measure such as the amount of data processed or number of events analysed.
- Design for Expansion Without Overengineering
With a useful use-case in place teams can now think about its expansion. How can the application be reused in other equipment, production lines or facilities?
Some common design considerations include reusable data formats, consistent equipment identifiers, interfaces, observability, configuration and component ownership.
At the same time, scalability should not drive the initial implementation. The application should address the current requirements, and have the flexibility to evolve without requiring a complete redesign.
Teams working on industrial AIoT products can learn more about connected systems, AI and the intersection of industrial venture development at [Aperture Venture Studio].
Conclusion
Practical industrial AIoT engineering requires a reliable end-to-end process that turns physical observations into actionable insights.
Sensors and connectivity can be seen as a starting point, but data integrity, integration, analytic approaches, security and operations are often just as important in building an effective system.
The most successful architecture is not necessarily the most complex one, but the one that addresses a specific need, is easily measurable, and can evolve as requirements become clearer.
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