Predictive Maintenance for automotive Industry

In an era of rapid technological development, the success and market position of a company are largely determined by the optimization of its processes. For Vitesco Technologies Italy, a company that manufactures automotive components, the quality of automation processes plays a pivotal role in their operations.

Vitesco Technologies Italy, therefore, engaged Zerynth for expert assistance. Vitesco Technologies Italy operates several automatic assembly lines to produce fuel injectors. Each line includes a test module that controls the previous assembly steps through a leak test. If the test is successful, the fuel module proceeds along the line, otherwise, it is rejected. It may happen that one of the elements of the module is not working properly due to wear. Prior to detecting a line malfunction, it would generate a significant number of false scraps. Therefore, it was crucial to minimize the occurrence of false scraps caused by module damage.

Furthermore, the replacement of worn components entails significant downtime lasting several hours. Therefore, Vitesco Technologies Italy sought the assistance of Zerynth to reduce manual diagnostics, as well as remotely monitor the status of the test module for each assembly line.

The Zerynth team provided Vitesco with an Industrial IoT & AI platform for real-time monitoring of machinery and predictive maintenance, thanks to the development of an Artificial Intelligence algorithm.

Zerynth’s Edge Devices, known as 4ZeroBox, are industrial control units directly connected to the cloud. They extract data directly from PLCs and machine sensors, integrating it with information acquired from additional sensors applied non-invasively at critical points of the machinery.

The signals are then processed, displayed on dedicated dashboards, and analyzed for the immediate detection of operational anomalies in pneumatic valves and other sealing elements in the high-pressure line of the machinery. Data analysis algorithms and predictive maintenance measures have been implemented directly on the IoT devices to enable the prediction of machinery failures.

Through remote monitoring of the status of each test machine in the assembly line, Vitesco Technologies gained a comprehensive and real-time view of their performance and any potential anomalies. This allowed for timely intervention and the prevention of failures or errors related to valve/sealing elements with a 24 hour advance notice, significantly reducing machine downtime and associated costs.

By implementing predictive maintenance techniques and minimizing manual diagnostics and interventions, the machines have achieved enhanced efficiency and prolonged operational lifespan. Thanks to error prediction and procedure optimization, the number of false negatives has been minimized, ensuring greater accuracy and reliability throughout the testing and assembly process.