Industries use numerous sensors that provide large volumes of data. Signals that describe the temperature, vibrations, pressure, energy consumption, equipment status, and other parameters can be constantly changing while the machines are operating.
The problem is that all anomalies do not mean a trouble.
It is natural that the temperature readings fluctuate. It can be natural that the vibration pattern has changed due to different operation conditions. Also, the signal can be noisy or incomplete.
That is why it is possible to apply AI algorithms for detecting anomalies in the data.
What is Anomaly Detection?
Anomaly detection is the identification of data points that have distinct behavior compared to the patterns.
In an industrial setting, an anomaly can be a sign of:
- Abnormal behavior of the equipment
- Mechanical issues
- Sensor problems
- Unusual operation conditions
- Production performance issues
Traditional ways include setting up the threshold for detecting the anomaly, but with machine learning models, it is possible to analyze the historic data and detect deviations from the typical patterns.
Why Context is Important
A simple way to detect an anomaly can be expressed as:
“Alert if the temperature is higher than 80°”.
However, in most cases, it is not enough.
The same temperature can be abnormal in one set of operating conditions, but normal in the other. That is why it is important for AI models to consider multiple signals.
For example:
Temperature + vibration + machine speed + history of operations
can give more information than temperature alone.
This approach will allow reducing false positive alerts and helping to detect those patterns that need further analysis.
Where Edge AI can Help
In many cases, it is not necessary for the sensor readings to be transferred to the cloud platform to start the analysis.
In cases where prompt reaction is required, it is possible to perform anomaly detection at the edge level.
The simplified schema can be as follows:
Sensor → Edge device → AI model → anomaly detection → alert/action
The cloud platform can still be used for storing the data, performing the model training and fleet-level analysis.
Good Data Is Important
AI does not make the problem of data disappear.
Industrial sensor data may have missing values, noise, inconsistent sampling rates, as well as changes that occur due to maintenance or equipment replacement.
Thus, a good anomaly-detection system has to take into account the features of the data and the environment.
Model performance has to be tested using real-life data, not just lab data.
From Detection to Action
Anomaly detection is just one step.
However, the main question is:
What will happen after the anomaly is detected?
Depending on the case, the alert can start inspection procedure, maintenance procedures, new data collection process or some other predefined actions.
In this way, AI turns into a part of an industrial intelligence system, and not just an isolated machine-learning model.
Aperture Venture Studio specializes in developing AI + IoT ventures for physical-world applications, including industrial intelligence, asset visibility, inventory optimization, worker safety, and operations systems.
Learn more about its vision here: Aperture Venture Studio
Conclusion
Industrial anomaly detection is not about detecting unusual numbers.
Its true purpose lies in detecting what changes are important, why they are important, and what kind of action has to be taken.
As technologies like AI, IoT, and edge computing advance, industrial anomaly detection may play an important role in industrial systems becoming more intelligent.
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