Machine learning based prognostics of on-board electromechanical actuators

Edmondo Minisci, Matteo D.L. Dalla Vedova, Parid Alimhillaj, Leonardo Baldo, Paolo Maggiore

Research output: Chapter in Book/Report/Conference proceedingConference contribution book


This paper presents a novel machine learning-based prognostic approach for on-board electromechanical actuators. The study is centered around overcoming the limitations of model-based prognostic frameworks that rely on expensive optimization processes. Machine learning techniques were employed to map system signal characteristics directly into parameters related to fault simulation. A first test, utilizing only five of eight implemented fault types, demonstrates a highly promising potential of artificial neural networks to predict and detect faults with minimal error. A second test expands the investigation to include all fault types and provides an analysis of the model’s robustness, error rates, and computational costs. The practical outcome of the work is a viable real-time solution for fault detection and characterization in electromechanical actuators, highlighting the efficiency and effectiveness of machine learning techniques for industrial applications.
Original languageEnglish
Title of host publicationProceedings of the Joint International Conference
Subtitle of host publication10th Textile Conference and 4th Conference on Engineering and Entrepreneurship
EditorsGenti Guxho, Tatjana Kosova Spahiu, Valma Prifti, Ardit Gjeta, Eralda Xhafka, Anis Sulejmani
Place of PublicationCham
Number of pages12
ISBN (Electronic)9783031489334
ISBN (Print)9783031489327, 9783031489358
Publication statusPublished - 10 Jan 2024
EventJoint International Conference: 10th Textile Conference & 4th Conference on Engineering and Entrepreneurship 2023 - Tirana, Albania
Duration: 19 Oct 202320 Oct 2023

Publication series

NameLecture Notes on Multidisciplinary Industrial Engineering
VolumePart F2090
ISSN (Print)2522-5022
ISSN (Electronic)2522-5030


ConferenceJoint International Conference


  • machine learning
  • prognostics
  • electro-mechanical actuators
  • reliability
  • fault implementation
  • fault detection and diagnosis (FDD)
  • failure analysis


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