There is no shortage of data in industrial organizations. Machines, sensors, production processes, and connected devices keep generating data about industrial operations. Data collection is the easy part of the equation.
The difficult task is turning collected data into intelligence that helps people make better decisions.
What Makes Industrial AI Different?
As opposed to AI in purely digital applications, industrial AI has to operate in environments when physical aspects of reality play an important role. Equipment will behave differently under different loads, sensors can create noisy signals, legacy systems may not share compatible data formats, and certain operational decisions may impact production immediately.
This makes industrial AI more complex than developing an AI algorithm alone.
It requires integration of data, infrastructure, people, and workflows.
A Practical Pipeline for Industrial AI
It would be helpful to visualize industrial AI as a set of steps that should take place.
Physical environment → sensors → data → AI → insight → decision → action
IoT technologies create a bridge between physical equipment and digital systems. The data that can be collected by the sensors include temperature, vibrations, equipment states, energy consumption, location, production conditions, etc.
AI and data analysis algorithms can then process the collected data to find patterns, anomalies, or predictive features.
And the final stage of the pipeline is making decisions based on the insights gained.
How Industrial AI Can Be Applied
Industrial AI has many possible applications.
Predictive maintenance can analyze the behavior of the equipment and find unusual activity that may require further investigation.
Quality monitoring can process production conditions and identify patterns related to potential quality issues.
Asset visibility can help organizations know where their equipment, tools, materials, and other assets are located.
Production optimization can process operational data and find bottlenecks, lags, or inefficiencies in the resource utilization.
The specific type of application does not matter much. The only important thing is a clear connection between the technology and the operational problem.
Data is More Important Than Sophisticated AI Models
An advanced machine learning algorithm cannot do anything with bad-quality data.
Industrial organizations might have to deal with many obstacles to create useful data.
- Legacy equipment
- Absence of sensors' data
- Noisy readings
- Disconnected systems
- Inconsistent data format
- Connectivity problems
And this is one reason why AI projects need teamwork from data scientists, software engineers, IoT experts, and industrial operators.
Human Expertise is Critical
AI recognizes patterns. Humans give meaning to these patterns.
An industrial operator knows that certain readings from a machine are normal in a particular phase of the production cycle. An engineer knows the reasons why equipment behavior was changed after maintenance.
A combination of machine learning and human expertise can make industrial systems smarter and more trustworthy.
Begin With The Problem
One of the key principles of industrial AI is beginning with the problem rather than technology.
Instead of asking:
"Where can we apply AI?"
ask:
"Which operational problem would better information help solve?"
In such a case, a roadmap from technology adoption to a result becomes obvious.
For example:
Problem -> Relevant Data -> AI Analysis -> Insight -> Operational Action -> Measurable Result
Such an approach might also be helpful when trying to avoid creation of complex system which does not have much to do with your operations.
Building AI + IoT for The Physical World
This problem-first approach is essential for Aperture Venture Studio, which aims to create companies based on AI + IoT technologies for industrial purposes. This studio has a model of building AI + IoT systems which consists of the following elements: AI, IoT infrastructure, real-world deployments, industrial use cases.
Learn more about Aperture Venture Studio.
Conclusion
Industrial AI does not imply simply data gathering and more advanced AI models application.
The key point here is creation of a reliable chain:
Data -> Intelligence -> Decision -> Action
Combination of these four points in regards to a particular operational challenge allows industrial organizations to go further than just dashboards and experiments towards practical implementation of the technology.
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