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AI monitoring boosts quality and decreases inspections

Case Description

As an important enterprise in the chemical industry, China Steel Carbon (China Carbon) is highly dependent on the stable operation of high-temperature equipment. How to predict abnormalities before equipment failure and help the maintenance team take early action to effectively avoid downtime and losses is the key to maintenance work.

However, the past maintenance model could only repair equipment when it malfunctioned, causing the production line to face downtime. This not only reduced production efficiency, but also required waiting for the equipment to slow down before parts could be replaced, incurring huge costs loss.

In order to improve this problem, we cooperated with China Carbon to build a predictive maintenance model through the DeepFlow platform, combined with a light alarm mechanism to help make on-site maintenance work more systematic.

Solution

Work with partners to install sensors on clients to collect data: establish a machine network, collect machine data and record it to the enterprise cloud situation room.
Establish an early warning model through InventAl’s self-developed DeepFlow product, integrate the central control room and set up an alarm mechanism to improve the problem of replacing the production line only when abnormalities occur.
The system construction method adopts cloud, on-premises or hybrid architecture, which can be determined based on data characteristics or case conditions.

Benefit

Interpret the operating status of the equipment based on AI, provide early warning or alarm, and detect faults early for repair and replacement.
Indirectly test the quality of parts from different suppliers.
Reduce manual inspection of equipment.