en.Wedoany.com Reported - Vecoplan AG has introduced the Vecoplan Intelligent Detect (VID) system at the K 2025 trade fair, designed to address extreme wear caused by inherent vibrations in shredders. VID is a self-learning system that monitors vibrations using high-frequency vibration sensors, enabling measures to be taken before machine damage occurs, including directly shutting down the machine. Vecoplan states that this technology can reduce the risk of machine damage by up to 60% and decrease machine downtime by up to 70%.

VID is a new module of the Vecoplan Smart Center (VSC) digital platform, which functions to network machines and processes. Both VID and VSC are available on new shredders and can also be retrofitted to existing machines.

The system uses high-frequency vibration sensors to detect subtle vibrations, with data transmitted to an evaluation unit. The evaluation unit compares the signals against thresholds specific to the material, determining whether intervention in machine operation is necessary. When foreign objects enter the shredder, the system displays their precise location, allowing operators to take corrective actions.

When setting up a new batch of material, VID analyzes vibration values and can create thresholds for that material within minutes. Vecoplan indicates that the system can identify differences in a wide range of materials, from lightweight films to hard plastics. Additionally, VID considers the shredder's speed and impact values and features a recipe function to help operators develop programs for rapid material changeovers. The system can also adapt to rotor replacements or adjustments in shredder geometry.

Patrick Pfeiffer, Sales Director at Vecoplan AG based in Bad Marienberg, Germany, stated that VID can detect problems long before bearing failure occurs, providing a new level of shredder reliability. He emphasized that VID is a key component for making processes transparent and controllable while actively protecting the shredder from damage. Traditionally, shredder operators have relied on predictive maintenance procedures to handle machine failures.

This article is compiled by Wedoany. All AI citations must indicate the source as "Wedoany". If there is any infringement or other issues, please notify us promptly, and we will modify or delete it accordingly. Email: news@wedoany.com