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Predictive Maintenance for automotive Industry

Vitesco Technologies Italy has reduced machine downtime and is now able to predict pneumatic valve malfunctions 24 hours in advance.

Predictive Maintenance for automotive Industry

Vitesco Technologies Italy has reduced machine downtime and is now able to predict pneumatic valve malfunctions 24 hours in advance.

predictive maintenance

“For some time we were searching for a technology that could reduce downtime in a specific module of our assembly line. Thanks to our collaboration with Zerynth we are now able to predict pneumatic valves malfunctions within 24 hours, resulting in a tremendous reduction in downtime.”

Alessio Papucci , Chief Digital Officer (CDO) & Advanced Technology Leader - Vitesco Technologies Italy

The Challenges

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 Solution

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.

The Results

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.

“Zerynth demonstrated proficiency to manage and install IoT systems as well as performed excellently in analyzing and programming advanced systems using Artificial Intelligence algorithms. Their expert team also managed the project by applying advanced methodologies which enabled us to meet our deadlines and targets.”

Alessio Papucci , Chief Digital Officer (CDO) & Advanced Technology Leader - Vitesco Technologies Italy
predictive maintenance

“For some time we were searching for a technology that could reduce downtime in a specific module of our assembly line. Thanks to our collaboration with Zerynth we are now able to predict pneumatic valves malfunctions within 24 hours, resulting in a tremendous reduction in downtime.”

Alessio Papucci , Chief Digital Officer (CDO) & Advanced Technology Leader - Vitesco Technologies Italy

The Challenges

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 Solution

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.

The Results

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.

“Zerynth demonstrated proficiency to manage and install IoT systems as well as performed excellently in analyzing and programming advanced systems using Artificial Intelligence algorithms. Their expert team also managed the project by applying advanced methodologies which enabled us to meet our deadlines and targets.”

Alessio Papucci , Chief Digital Officer (CDO) & Advanced Technology Leader - Vitesco Technologies Italy
vitesco

COMPANY NAME:

Vitesco Technologies Italy

INDUSTRY:

Automotive

ASSETS:

Assembly line testing machines

PROFILE:

The company’s aim is to develop innovative, efficient electrification technologies for all types of vehicles.

RESULTS:

Increased Productivity
100% remote monitoring

Increased Automation
50% less manual operations

Increased Efficiency
24h malfunction prediction

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