Industrial AI spans five levels: from identifying parts to controlling processes

A painted part arrives on the conveyor, but its label can no longer be read. Workers have to consult the documentation, slowing the line down. In an example from KAMAZ's press and frame plant cited in the Habr article, a camera and recognition system were assigned the task: identify the part from an image and send the result to the production system. This is a clear entry point for AI: there is a specific delay and an action that helps eliminate it.

For developers and product managers, the most useful lesson here is the sequence of implementation. First, understand exactly what is being measured and how the process works. Then identify which decision would improve operations. Only after that should you choose a model and its degree of autonomy. The article brings together industrial examples but provides no comparable measurements of their effectiveness, so it is best read as an examination of approaches.

A sensor reports a number; context gives it meaning

Imagine a motor whose vibration has increased. You could send the operator a warning about a possible breakdown. But the motor may simply have switched to a different load under which those readings are normal. Without information about the operating mode, the system will regularly alert people, whether there is a good reason or not.

Measurements therefore need to be accompanied by the time, the equipment's operating mode and the conditions in which they were taken. Communication gaps, measurement errors and changes in the sensors themselves also need to be accounted for. For the team, this means specific work: agreeing on an event structure, checking timestamps, documenting operating modes and setting rules for handling missing data.

The article includes a telling example from SIBUR: a decline in temperature readings and their fluctuations is linked to coke buildup on reactor grids. At first glance, this might look like a measurement error. Turning the observation into a useful signal requires knowledge of the process. Simply looking for unusual values is not enough.

This preparation also determines the quality of model validation. If normal equipment ramp-up is mixed with steady-state operation, the resulting evaluation will reveal little about the system's usefulness in production.

Five levels, five different products

The first level is recognition: which part is in front of the camera, whether the equipment is running and whether a defect is visible. The second is diagnosis and prediction: what a deviation means and whether it could lead to a shutdown. The third is recommendations for the operator. The fourth is selecting process parameters within constraints. The fifth is autonomous control.

These levels are useful for defining a product's scope. A system that detects a suspicious area in an image and a system that changes a machine's operating mode require different data, interfaces and validation methods. Successful recognition alone does not establish readiness for control.

Consider an extruder used in polypropylene production. According to the source article, SIBUR's model analyzes telemetry, warns of the risk of blockage and can recommend adjusting the pressure applied by the blades. It delivers value if the operator receives the warning early enough to take appropriate action.

For this kind of product, it makes sense to measure warning lead time, the number of false alarms and the number of shutdowns that occur without a warning. A recommendation should explain which indicators triggered the alert and what changes are proposed. The operator may have information the system has not received, such as details of ongoing maintenance or equipment limitations.

How to choose the first project

A good candidate is a recurring task with available data and a verifiable outcome. One example is recognizing parts that employees currently identify manually. Before development begins, it is useful to record the baseline time required for the operation and the error rate; otherwise, it will be difficult to tell after launch whether the work has paid off.

The next step is to test the solution across different shifts, operating modes and imaging conditions. The material on monitoring aluminum production specifically mentions reflections, sun glare and vehicle warning beacons: the system must distinguish these from signs of a process disruption. Details like these often determine the quality of a deployment.

Autonomous control requires stricter criteria: limits on permitted actions, a way for people to intervene and clearly defined behavior when data is lost. The claim that "the model makes no mistakes" does not establish this without test conditions and statistics.

The practical takeaway for the team: choose one source of wasted time or one type of failure, connect the data to the conditions in which it occurs and test whether the result helps people make a decision. Expanding the model's authority makes sense after that validation.