Industrial applications have been gathering data for many years already. Temperature and pressure are measured by sensors, assets are monitored by RFID systems, devices generate telemetry data, and enterprise systems collect information about inventory, maintenance, manufacturing, and workforce.
Nevertheless, gathering data is not equal to understanding data.
The next stage of development in the field of industrial solutions is data analysis and transformation into meaningful intelligence. This is when AI comes into play along with IoT and creates AIoT solutions.
IoT Provides the Data Layer
IoT connects physical assets with digital solutions via sensors, gateways, connectivity, and data pipeline.
Physical Assets
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Sensors and Devices
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Connectivity
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Data Pipeline
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Processing
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Applications
Such architecture gives a possibility to see what is going on within industrial processes. But there is no explanation of what should be done after just displaying all the sensor data on the dashboard.
AI provides such explanation.
AI Transforms Signals Into Insights
With machine learning algorithms, signals such as equipment telemetry, maintenance data, environmental factors, production information, asset locations, and events could be analyzed.
Sensors
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Internet of Things
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Data Pipeline
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AI and Machine Learning Algorithms
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Predictions
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Decision Making
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Physical Action
The task is to shift from just logging events to making decisions that would be more informed.
The Real Problem Is Integration
Creating an industrial AI system requires not only selecting a powerful model. Quality of data, reliable timestamps, system compatibility, stability of network connection, security issues, and integration of workflows are just as critical.
A highly accurate model is useless if the insights that it provides do not affect actual operations. Industrial AI calls for knowledge in the field of hardware, connections, data pipelines, software architecture, and operations.
Edge Computing and its Importance
Some tasks require the decision-making process to be as close as possible to the equipment that provides data. Edge computing can help minimize latency, control the amount of data transferred and work regardless of network connection quality.
Device → Edge Computing → Decision
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Cloud Platform
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Historical Analytics
Different tasks may require different architectures.
From Dashboards to Decisions
The advancement of AIoT solutions can be classified into five levels:
Visibility: Know where your assets are located.
Monitoring: Sense any change.
Prediction: Assess what might happen.
Recommendation: Advise on the next move.
Action: Enable and facilitate actions.
The further a solution evolves, the more important integration becomes.
Aperture Venture Studio helps build AI & IoT startups for physical world use cases like asset visibility, inventory management, worker safety, access control, and industrial insights.
The future of industrial technology will involve physical assets, trustworthy data, intelligent processing, and human insight. AI delivers intelligence, IoT connects things, and software makes them actionable.
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