Data is one of the key elements of industrial environmental monitoring. There could be many thousands of readings collected from sensors, monitoring equipment, and associated technologies, but the use of large amounts of data becomes possible only with the help of high-quality data.
Low-quality data results in misinterpretation, false alerts, and difficulties in compliance reporting. This is why data quality should be regarded as an integral component of any industrial monitoring strategy.
Sources of Data Quality Issues in Industrial Monitoring
Industrial environmental monitoring is done under tough conditions. The following factors might affect sensors' readings:
- Calibration drift
- Interference of electrical nature
- Sensor deterioration
- Network disruption
- Loss of readings
- Timestamp error
- Special operational modes
Even a minor data quality problem can influence interpretation of environmental trends.
Importance of Validation
Before being used in analysis and reporting, environmental monitoring systems can use validation procedures.
It means identifying the sudden appearance of values outside a certain operating range or gaps in the data sequence from a certain sensor.
It does not automatically mean that the value is wrong; it allows operators to check whether the value indicates any changes in the environment or a problem with the monitoring procedure.
Use of Industrial IoT
Industrial IoT can enable a connection between sensors, edge devices, databases, and monitoring dashboards.
Edge devices can perform some simple data processing at the local level, such as filtering noise, checking the status of sensors, and temporary data storage in case there are no connections available.
After the processing, the data can be sent to cloud platforms for further storage, visualization, and analysis.
Use of AI
AI can provide additional analytics opportunities for environmental monitoring.
For example, machine learning models can process both historical and current data to identify patterns which cannot be identified by means of simple threshold checking. The unusual combination of sensor readings can indicate potential problems with equipment or abnormal operation of processes.
AI is supposed to be a supplementary technology in the monitoring process.
Better Data, Better Decisions
With the help of reliable data, businesses can track their emissions trends, study anomalies, report, and improve their processes.
The goal is not just getting more sensor data points. The goal is to make sure this data is reliable, consistent, up-to-date, and useful.
Looking Forward
With more and more factories using connected sensors and analytics with machine learning algorithms, data quality will only become more and more crucial.
The right environmental monitoring infrastructure involves reliable sensors, validation procedures, reliable communication channels, safe storage of data, and smart analytics.
All these elements working together can allow one to turn data points into useful information.
To learn more about emissions monitoring at industrial sites, Industrial IoT, machine learning, and other environmental technologies, go to Emissions and Stack.
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