Across industrial environments there can be massive amounts of data produced. This could be through status updates from machines, sensors measuring temperature and vibrations and asset tracking systems.
However it is getting this data and using it to make decisions that can be tricky.
But combining AI and IoT can help.
What are AI and IoT?
IoT means that physical devices can be connected to a network to gather and share data. This could be industrial sensors, smart machines, asset tracking and environmental monitoring.
This data is then analysed using AI to spot trends, identify anomalies and make decisions.
Within an industrial environment this could be:
Collect data – Sensors are used to measure things like temperature, vibrations, energy usage etc from machines.
Send data – IoT devices send this data to an edge device/cloud.
Analyze data – The AI model processes this to spot trends or issues.
Action – The system can alert the operator or suggest next steps.
The architecture will depend on your environment and data requirements.
Predictive Maintenance
Unplanned breakdowns can cause disruption and increase maintenance costs.
Often traditional methods of maintenance are scheduled or reactive. However, predictive maintenance can help.
Using vibration and temperature sensors, we can monitor machines continuously within an industrial environment. This data can then be analysed using an AI model to spot anomalies.
When an issue is detected you can alert your maintenance team to investigate.
However, this doesn’t mean there will be an issue with the equipment. You need to ensure you have accurate historical data, proper validation and human oversight.
Better Asset & Inventory Oversight
With lots of equipment, tools and materials in large facilities sometimes it can be hard to keep track of what’s where.
IoT assets can be used to keep track of where your assets are within a business. AI can then be used to analyse how the assets are being used, spot any anomalies or predict how much inventory you may need.
A warehouse could use the data from asset tracking and historical usage data to see what equipment is most commonly used.
This can help you gain better visibility and make more informed decisions.
Energy Efficiency
Industrial environments can often be very energy intensive. It can be tricky to know where you are wasting energy.
Using smart metering and sensors, you can gather data on how much energy is being used, how long machines are running for and environmental factors.
This can be analysed using AI to spot trends and identify any inefficiencies.
You may find that certain machines are using a lot more energy than expected when they are idle, or you could change the schedule.
This gives the facility managers a good starting point to look into and reduce waste.
Right Architecture
To get the most out of your AIoT system there are some factors you need to think about as a developer or engineer.
Data quality – If the data coming in from the sensors isn’t accurate, you won’t get a reliable model.
Latency – If you have an application that needs to be fast you may want to use edge computing.
Connectivity – Industrial environments can sometimes have unreliable connections.
Security – You need to make sure you have proper security in place for your connected devices and data flow.
Integration – Your new systems need to integrate with your industrial equipment and software.
Human oversight – Your operators need to understand the alerts and have a process in place.
Edge computing can be a great option for systems that need to be fast or if there could be connectivity issues. However, using cloud computing can help provide centralised analytics, data and model management.
Many industrial applications will use a hybrid approach, with edge and cloud.
Starting With A Problem
A common mistake is adding AI into your business because it is trendy.
Instead start with something that has a clear goal.
Identify an issue that comes up regularly.
What data can help you understand this?
Is there data available and is it accurate?
Build a small prototype using one machine/process/facility.
Measure the results.
Improve the system and scale.
Useful metrics could be unplanned downtime, maintenance response times, inventory errors, energy usage and false alerts.
It will depend on your issue. Don’t get too fancy with your model if it isn’t helping improve a process within your business.
Final Thoughts
Using AI and IoT together can make your industrial operations more informed, reactive and efficient. IoT can give visibility of your physical processes and AI can help you understand the data.
Getting the most out of this is by linking it to your operations. Having accurate data, the right architecture, security and human oversight is very important.
Find out more about using AI in industry, connected technology and venture development here: Aperture Venture Studio
Discussion
If you were building an AIoT solution, which of the problems would you try to solve first? Predictive maintenance, asset tracking or energy optimisation? What technologies would you use and why?
[SENSORS] -> [PROCESSING] -> [AI MODEL] -> [ACTION]
collect clean and interpret trigger
raw data organize and predict a response
^ |
|___________ feedback loop _________________|
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