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ELINV_24

PROJECT

ELINV_24

Smart high-efficiency servo-drives for textile machinery
PROJECT LEAD

Salvatore Pirozzi

CONCEPT

This project focuses on optimizing servo-drives used in textile machinery to maximize efficiency and improve performance. One of the key challenges addressed is the design of high-efficiency, high-dynamic inverters tailored to the specific needs of these industrial applications. A central aspect is the integration of silicon carbide (SiC) devices into the DC/AC converter architectures.

The project aims to develop modulation techniques that minimize both switching and conduction losses, ensuring efficient inverter operation. To further enhance reliability and reduce downtime, advanced diagnostics and predictive maintenance techniques will be implemented through machine learning.

CONTEXT

The project operates in the context of energy efficiency and operational reliability improvements in the textile machinery industry. Its goals include:

  • Energy efficiency: By optimizing inverters and integrating SiC devices, energy losses in electrical conversion can be reduced, thus lowering the environmental impact of energy consumption.
  • Reliability and lifespan: Implementing advanced diagnostic and predictive maintenance techniques reduces machine downtime and extends operational life.

Beneficiari

OBJECTIVES AND EXPECTED RESULTS

The project aims to deliver tangible innovations capable of transforming the Italian textile sector. Specifically:

  • Selection of optimal power devices: Identify the most efficient and cost-effective components for inverters, allowing textile companies to adopt modular and distributed motion architectures without increasing costs.
  • Development of a modular servo-drive: Design a converter model tailored to the textile sector, but modular and adaptable to different production needs.
  • Implementation of advanced algorithms: Introduce sophisticated control algorithms to ensure maximum energy efficiency and unmatched dynamic performance.
  • Use of machine learning: Employ ML techniques for predictive diagnostics, changing how companies manage maintenance and prevent faults.
  • Comprehensive validation: Rigorously test and validate the proposed solutions — both numerically and experimentally — to ensure they are reliable and effective in real-world applications.

KEY FIGURES

8

RESEARCHERS INVOLVED

12

PROJECT DURATION

3

NUMBER OF WORK PACKAGES (WPS)

4

FINAL TRL

Contact us

Project n.: PE_00000004
MADE IN ITALY CIRCOLARE E SOSTENIBILE
Tax Code. 97931690156

Contact

Registered headquarters Piazza Leonardo da Vinci, 32, 20133 Milano
Operative headquarters Via Copernico, 38, 20125 Milano | Edificio C – Piano 1
Southern office Corso Nicolangelo Protopisani, 70, 80146 Napoli | Laboratorio Ricreami CESMA – Università degli Studi di Napoli Federico II