Beyond Sensor Data and into the Physical Realm: How AIoT and Physical AI are Transforming Industry Automation
Factories produce enormous volumes of data. Sensors track temperature and vibration, cameras scan products, and smart machines tell us the status of various factory operations. How do you make sense of it all and use the insights to make improvements in the real world?
These two techs are enabling companies to investigate this challenge: Artificial Intelligence of Things (or AIoT) and Physical AI. As they are connected to each other, they are different. Physica I focuses on intelligent systems with ability to explore the physical world, whereas the AIoT synthesizes connected technology with AI to provide analytics.
Knowing how these technologies operate-and where they fail-can provide industrial teams with a method for developing more-realistic applications.
Understanding AIoT: Turning Connected Data Into Insights
The Internet of Things (IoT) simply describes the concept of connecting any device to the internet and to each other. In industrial settings, IoT can consist of devices such as temperature sensors, vibration monitors, cameras, programmable controllers, and manufacturing equipment.
AIoT takes the connected infrastructure and adds artificial intelligence. Rather than just reporting measurements, an AIoT system interprets data in order to find trends, flag outliers or make predictions.
For example: a production line with a motor. Sensors measure the temperature of the motor, as well as the amount of vibration. Analytics tool could take the present measures against historical data for the machine to see if there's anything higher than expected.
A standard process has four phases:
- Data collection: Sensors and connected devices gather the data.
- Data processing: The system categorizes data and monitors for incomplete or anomalous data.
- AI working? AI analysis: A model finds the structure or approximates the same event.
- Decision support: Results are shown as alarms, advice, or inputs to another system.
Not all applications need AI. Traditional monitoring may work on straightforward thresholds or pre-existing statistical models, and AI only comes into it when it can deliver better information than traditional methods.
Situating Physical AI
AIoT comes in for organizations to derive insight from their connected surroundings. Physical AI relates to systems that leverage AI to sense and interact with the physical environment including intelligent devices and robots.
For a mobile robot in a warehouse for instance, the system needs to detect surrounding objects, localize itself, plan a route and move around ensuring safety rules are followed. The surrounding environment is likely to change while the robot is in motion.
This is different than a monitoring system that just produces a misleading machine reading. Physical AI is where perception and decision are tied to physical behavior.
Yet a model's forecast should not automatically be a set of instructions to the industrial hardware. The physical gestures need suitable control algorithms, tests, operational boundaries, and protections against unlikely situations.
How AIoT and Physical AI Can Work Together
HAVE a manufacturing plant want to cut down on downtime due to machinery.
The an AIoT system gathers data on temperature, vibration, and operating-speed, the analytics part of the system detects the pattern associated with an abnormal operation and sends a message to the maintenance crew.
An intelligent mobile robot in another area of the workshop could carry tools or parts to where they are needed. The robot would navigate through the environment based on its own awareness.
They may communicate operating information where it is necessary and relevant, but not need to be directly connected. Interconnection should be dictated by the process, system design, and safety requirements.
The distinction is important:
- IoT provides connectivity and measurements.
- AIoT relies on interconnected data to facilitate analysis and decisions.
- Physical AI plays a key role in physical interaction.
- Industrial control systems support realization of desired operating behavior and safety limits.
Splitting them up could also make it easier to test, maintain, and troubleshoot.
Selecting Cloud or Edge Processing
Another major consideration in design is where the data ought to be processed.
Cloud infrastructure can enable stong storage, training and analyzing capabilities at scale across multiple facilities Edge computing allows processing of data closer to the equipment, which can reduce communication delays and decrease the volume of data transmitted.
For instance, a camera-based inspection system must detect a bad part while it is still on the manufacturing line. Online processing of images might solve this issue by satisfying timing deadlines, based on the model and hardware.
Alternatively, a cloud service might collate inspection data from different lines to provide an overall view of quality.
Which architecture you use is a factor of latency, connectivity, computing power, data sensibility, cost, and dependability. Certain applications are better served by integrating edge and cloud rather than separating the two.
Typical Difficulties with Industrial AI
There are a number of issues to be taken into account before an AI system is released.
Data quality: Corrupted sensors, lost readings, mismatched times can all cause our models to perform poorly. Data should always be checked before depending on the prediction results.
Legacy integration: Older equipment might have various communication protocols or restrict access to data. Integration planning should consider the existing controllers and software, along with maintenance procedures.
Safety and reliability: AI models can produce errors. Systems that control physical activities require safety considerations, failure detection, operational limits and controls, and human oversight.
More links may lead to increased risk of compromise or loss of control. Consider access control, network segmentation, secure updates, and monitoring when designing the system.
A technically successful prototype may still not address a significant business problem. You need to define what you want to achieve, and how you will measure success.
All of these considerations, rather, are reasons to embrace industrial AI. They are engineering requirements that will influence how we approach the problem from the outset.
A Practical Approach to Evaluating an AI Project
Stop connecting all the machines straight away. Start with one scenario that you've clearly defined.
For a predictive maintenance pilot, a team could:
- Figure out a recurrent piece of equipment and note the time it is down to normally and the cost of maintaining it.
- Verify if current sensors could help quantifying pertinent parameters.
- Determine if a straightforward rule-based or statistical approach might have been able to do the job.
- Test the equipment in a supervised setting and evaluate the agreement of the predictions with the real scenarios.
- False alarms, miss events, time to respond, cost of operation, maintenance effort.
- Only extend the project as far as the results make it worth the investment.
For example, if you have an anomaly-detection model that produces many alerts, but only a handful are accurate, the team should examine sensor quality, model thresholds, and the cost of false inspections. Prediction accuracy alone does not tell you whether you actually improve operations.
Once a project moves toward automatic physical actions, the architecture of control and safety needs to be addressed independently. It cannot be assumed that analysis of data (no matter how reliable) proves that automatic operation is safe.
Conclusion
AIoT and Physical AI complement industrial automation stages. When connected, devices allow clarity of operational metrics. AI can make sense of the data, while Physical AI can act and interact in physical space.
The question is then about how to combine these capabilities without complicating or exposing to risk. Trustworthy data, the right controls, measurable goals, and well considered pilots offer greater assurances than using a new technology just because it's a new technology.
For companies looking at industrial usage, the Aperture Venture Studio overview of Physical AI and AIoT can provide more perspective.
Finally, the better question isn't: "Can this process use AI?" It's whether or not the technology can enhance the process in a way that is quantifiable, dependable, and safe.
https://apertureventurestudio .com/
Top comments (0)