Today’s industrial environment is producing more data than ever before. Each machine, sensor, PLC, and monitoring device produces thousands of data points per minute. However, despite all the information gathered, most companies still find it hard to make use of it.
Data collection is not an issue anymore; making sense of it is.
This is how AI starts changing the way industrial monitoring works.
Why Conventional Industrial Monitoring Fails
The best thing about conventional industrial monitoring systems is that they do a really good job at showing you the data.
In most cases, operators can check temperature, pressure, vibrations, emissions, energy usage, and other important information via the dashboard.
However, the dashboard relies on the operator spotting changes in those readings.
AI Provides Context to Data
AI doesn't substitute industrial monitoring. It complements it.
While a mere display of values is not what industrial monitoring is about, AI constantly processes incoming data in order to detect patterns and abnormalities and provide context.
For instance, AI can tell when vibration, temperature, and energy consumption start to change simultaneously even though each individual value is within the normal operational range. This combination of values may serve as an early warning sign of developing problems with machinery that might go unnoticed otherwise.
Practical Applications
AI is used by modern industrial facilities in many different ways.
Predictive Maintenance
Instead of servicing equipment according to pre-defined time frames, AI processes data from sensors to predict when the service will actually be required.
This prevents the equipment from experiencing unexpected downtime and at the same time saves resources on unnecessary services.
Environmental Monitoring
Environmental monitoring becomes an important tool in operations of many industrial facilities.
AI can detect abnormal emission patterns, make a comparison with historical data, and alert the operator when operating parameters deviate from normal performance.
Energy Optimization
Energy is an important expense component for many factories.
AI is capable of analyzing production performance and energy consumption and identifying inefficiencies and potential improvements without compromising production volume.
Quality Control
Machine learning algorithms may help to discover correlations between production conditions and quality of the products made and assist in detecting process variables that affect quality.
AI Needs Industrial Internet of Things
AI relies heavily on high-quality data.
IoT technology supplies such data by continuously collecting information about production using various sensors and controllers.
Combined with edge computing capabilities, AI is able to process important information near production facilities and synchronize historical data with cloud platforms.
Implementation Challenges
Implementation of AI is more than just purchasing a software.
One should pay attention to the following aspects:
- Accurate sensor data
- Correct equipment calibration
- Secure network infrastructure
- Compatibility with existing systems
- Defined goals
- Training staff to make use of information provided by AI systems
AI works best when it augments skills and experience of engineers and operators instead of trying to replace them.
Conclusion
With more industrial facilities going through the process of digital transformation, AI-driven monitoring seems set to become a standard aspect of modern facilities.
These systems will not only detect abnormal situations but provide recommendations for how to fix them, manage the prioritization of maintenance activities, and optimize processes using the operational data.
Smart, safe, and efficient industrial facilities are just around the corner.
Final Thoughts
Modern industrial monitoring moves past data gathering and becomes a tool for intelligent decision-making thanks to AI, Industrial IoT, connected sensors, and advanced analytics.
Those interested in Industrial IoT, emissions monitoring, and other connected industrial solutions should check out Emissions and Stack blog posts that provide useful information about modern industrial monitoring solutions.
How is your organization leveraging AI in its industrial operations? What are you focusing on—predictive maintenance, environmental monitoring, energy optimization, or some other application? Let me know your experience in the comments below!
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