IoT systems do a great job when it comes to data gathering. They are equipped with sensors that detect temperature, vibrations, pressure, position, energy consumption, and device status. Meanwhile, gateways send collected data and cloud or edge platforms analyze and store it.
Nevertheless, gathering data is one thing, analyzing and understanding it is another one. An intelligence layer plays a vital role here as it transforms raw IoT data into meaningful information and insights.
IoT Offers Visibility
One of the possible workflows of IoT is:
Sensor → Gateway → Network → Data Platform → Dashboard
Such an architecture answers the essential question – what is happening?
For example, sensors show that the device is working at a higher temperature than usual. In such a case, a user will be able to visualize and save that data.
Still, the exact cause behind the temperature increase, its level of importance, and further steps to take will remain unclear without artificial intelligence and analytics.
Intelligence in the Data Stream
An intelligence layer will assess the information collected from the sensors and devices and compare it to historical data.
Physical Resources
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Sensors & Devices
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Connectivity
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Data Gathering
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AI / Analytics
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Insights & Predictions
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Decision Making
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Action
The artificial intelligence can detect anomalous behavior, recognize patterns, predict future issues and suggest solutions. The aim is not to automate the process of making all decisions, but to ensure there is enough information available to make better decisions.
The Importance of Context
The raw data from the sensors usually does not give a complete picture. For example, the increased vibrations of a machine can be explained by the increased speed of operation, different loads on the machine, the noise from the sensor, natural variations, the wear and tear of the equipment or mechanical malfunction.
An intelligence layer can use a combination of information from multiple sources and historical data to provide context. It allows to understand whether every deviation from the norm requires action.
Predictive Maintenance
This is an illustrative example of the proposed architecture.
Machine
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Vibration + Temperature + Pressure
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IoT Data Pipeline
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Anomaly Detection Model
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Risk / Prediction
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Maintenance Recommendation
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Human Review
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Maintenance Action
The predictive model is not the whole story. Only by reaching the maintenance pipeline does the prediction become truly useful.
Production Intelligence Is More Than Just a Machine Learning Model
The production intelligence stack could contain many things: data preparation, feature engineering, machine learning, decision logic, integration with workflows, and monitoring.
Data needs to be cleansed, validated, and formatted for analysis. Models need to get good signals and not garbage data. Insights need to make it to the hands of the operator, maintenance crew, or business system. The performance, data quality, and operating environment also need to be monitored.
The Importance of Edge Intelligence
However, not all AI-based workloads must be executed from the cloud. Industrial use cases might demand a fast response or run without connectivity at all. In this case, edge intelligence makes sense.
Machine
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Edge Device
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Local AI Analysis
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Instant Notification / Response
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Cloud Infrastructure
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Analytics in the Long Term
The right choice of architecture depends on latency, connectivity, security, cost, and other considerations.
Going from Monitoring to Taking Action
Businesses can move along several steps of progress:
- Visibility: Understand what happens.
- Monitoring: Monitor the current situation constantly.
- Detection: Discover anomalous activity.
- Prediction: Predict future events.
- Recommendation: Recommend potential actions.
- Response: Integrate intelligence into processes.
Data quality will always remain crucial for this sequence. Old equipment, non-compliance with standards, gaps in measurement, wrong timestamps, and disintegrated systems can hinder even advanced AI algorithms.
At Aperture Venture Studio, the main goal of our work lies in helping AI and IoT startups create connections between physical systems, data, intelligence, and industry.
IoT allows us to observe the physical reality. AI makes us capable of understanding it.
The intelligence layer converts connected data into better decision-making and action.
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