IIoT systems require one key ingredient: data.
Manufacturing facilities may use hundreds or even thousands of sensors to measure equipment performance, process parameters, energy usage, emissions, and environment. Yet having many sensors doesn't always equate to having good insights.
Poor quality underlying data can render even the most sophisticated AI and analytics algorithms useless.
How Industrial Sensor Data Can Become Poor-Quality
Industrial conditions are usually hard. Sensors may work under heat, vibrations, dust, humidity, chemicals, and other challenging conditions.
Sensor data quality may be affected by such factors as:
- Sensors' calibration problems
- Degradation of equipment
- Network failures
- Data gaps
- Electromagnetic interference
- Improper installation of sensors
- Inconsistent timestamps
These challenges can make it difficult to differentiate between the actual changes in working conditions and faults in a monitoring system.
Data Validation on the Edge
Edge computing provides an additional security measure.
Instead of immediately transferring all sensors' readings to the cloud, the edge gateway may perform some preliminary checks. It can recognize missing values, abnormal readings, eliminate particular types of noise, and temporarily keep the information in case of network interruptions.
This approach can reduce unnecessary data transfer while increasing the reliability of the data flow.
What Role AI May Play
When the data is sufficiently accurate, AI and machine learning algorithms may add value to the analysis.
They may study historical sensor data and find unusual combinations of temperature, pressure, vibrations, or emissions.
Such capabilities can be used to develop solutions like:
- Predictive maintenance
- Anomaly detection in equipment operation
- Process optimization
- Environment monitoring
- Energy management
- Analysis of emissions
However, AI and machine learning shouldn't be seen as compensation for inadequate monitoring practices.
Environmental Monitoring With Sensor Data Quality Guaranteed
The importance of data quality becomes especially noticeable during environmental monitoring.
Systems providing continuous monitoring can collect information about emissions, air quality, temperature, particles, and many other environmental parameters. Quality data helps companies analyze trends, research abnormal situations, and make environmental reports.
When such systems are interconnected through Industrial IoT platforms, the data can be collected in dashboards and databases.
Creating a Robust IIoT System
A good Industrial IoT structure comprises many elements:
Sensors → Edge Processing → Secure Connectivity → Data Storage → Analytics → Dashboard
All of them make the whole structure more reliable.
The idea here is not just to increase the amount of data but to make sure that the data sent to operators and analytics systems is reliable, up-to-date, secure, and applicable.
The Future Is Here
With more factories integrating AI and IoT systems, the data quality becomes increasingly crucial.
With better sensors, edge processing capabilities, smart analytics, and reliable connections, you can turn basic metrics into real-time insights.
If your industry needs to monitor equipment and environmental metrics, reliable data will be the basis for making smarter decisions.
To see more information about Industrial IoT, emissions monitoring, AI, and environmental solutions, check out Emissions and Stack
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