End-to-End Methodology for Predictive Maintenance Based on Fingerprint Routines and Anomaly Detection for Machine Tool Rotary Components

This work introduces an end-to-end methodology, from data gathering to fault notification, for the predictive maintenance of rotary components of machine tools. This is done through fingerprint routines; that is, processes that are executed periodically under the same no-load conditions to obtain a...

Full description

Saved in:
Bibliographic Details
Main Authors: Amaia Arregi, Aitor Barrutia, Iñigo Bediaga
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Journal of Manufacturing and Materials Processing
Subjects:
Online Access:https://www.mdpi.com/2504-4494/9/1/12
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1832588280991842304
author Amaia Arregi
Aitor Barrutia
Iñigo Bediaga
author_facet Amaia Arregi
Aitor Barrutia
Iñigo Bediaga
author_sort Amaia Arregi
collection DOAJ
description This work introduces an end-to-end methodology, from data gathering to fault notification, for the predictive maintenance of rotary components of machine tools. This is done through fingerprint routines; that is, processes that are executed periodically under the same no-load conditions to obtain a snapshot of the machine condition. High-frequency vibration data gathered during these routines combined with knowledge about the machine structure and its components are used to obtain failure-specific features. These features are then introduced to an anomaly and paradigm shifts detection algorithm. The method is evaluated through three distinct scenarios. First, we use synthetically generated data to test its ability to detect controlled variations and edge cases. Second, we use with publicly available data obtained from bearing run-to-failure tests under normal load conditions on a specially designed test rig. Finally, the methodology is validated using real-world data collected from a spindle bearing installed in a machine tool. The novelty of this work lies in performing anomaly detection using failure-specific features derived from fingerprint routines, ensuring stability over time and enabling precise identification of machine conditions with minimal data requirements.
format Article
id doaj-art-7319df2c474141fdadf9c69d9bfb4f30
institution Kabale University
issn 2504-4494
language English
publishDate 2025-01-01
publisher MDPI AG
record_format Article
series Journal of Manufacturing and Materials Processing
spelling doaj-art-7319df2c474141fdadf9c69d9bfb4f302025-01-24T13:36:26ZengMDPI AGJournal of Manufacturing and Materials Processing2504-44942025-01-01911210.3390/jmmp9010012End-to-End Methodology for Predictive Maintenance Based on Fingerprint Routines and Anomaly Detection for Machine Tool Rotary ComponentsAmaia Arregi0Aitor Barrutia1Iñigo Bediaga2Ideko—Basque Research and Technology Alliance (BRTA), Arriaga Kalea, 2, 20870 Elgoibar, Gipuzkoa, SpainIdeko—Basque Research and Technology Alliance (BRTA), Arriaga Kalea, 2, 20870 Elgoibar, Gipuzkoa, SpainIdeko—Basque Research and Technology Alliance (BRTA), Arriaga Kalea, 2, 20870 Elgoibar, Gipuzkoa, SpainThis work introduces an end-to-end methodology, from data gathering to fault notification, for the predictive maintenance of rotary components of machine tools. This is done through fingerprint routines; that is, processes that are executed periodically under the same no-load conditions to obtain a snapshot of the machine condition. High-frequency vibration data gathered during these routines combined with knowledge about the machine structure and its components are used to obtain failure-specific features. These features are then introduced to an anomaly and paradigm shifts detection algorithm. The method is evaluated through three distinct scenarios. First, we use synthetically generated data to test its ability to detect controlled variations and edge cases. Second, we use with publicly available data obtained from bearing run-to-failure tests under normal load conditions on a specially designed test rig. Finally, the methodology is validated using real-world data collected from a spindle bearing installed in a machine tool. The novelty of this work lies in performing anomaly detection using failure-specific features derived from fingerprint routines, ensuring stability over time and enabling precise identification of machine conditions with minimal data requirements.https://www.mdpi.com/2504-4494/9/1/12machine toolspredictive maintenancefingerprint routinesanomaly detectionconcept driftIndustry 4.0
spellingShingle Amaia Arregi
Aitor Barrutia
Iñigo Bediaga
End-to-End Methodology for Predictive Maintenance Based on Fingerprint Routines and Anomaly Detection for Machine Tool Rotary Components
Journal of Manufacturing and Materials Processing
machine tools
predictive maintenance
fingerprint routines
anomaly detection
concept drift
Industry 4.0
title End-to-End Methodology for Predictive Maintenance Based on Fingerprint Routines and Anomaly Detection for Machine Tool Rotary Components
title_full End-to-End Methodology for Predictive Maintenance Based on Fingerprint Routines and Anomaly Detection for Machine Tool Rotary Components
title_fullStr End-to-End Methodology for Predictive Maintenance Based on Fingerprint Routines and Anomaly Detection for Machine Tool Rotary Components
title_full_unstemmed End-to-End Methodology for Predictive Maintenance Based on Fingerprint Routines and Anomaly Detection for Machine Tool Rotary Components
title_short End-to-End Methodology for Predictive Maintenance Based on Fingerprint Routines and Anomaly Detection for Machine Tool Rotary Components
title_sort end to end methodology for predictive maintenance based on fingerprint routines and anomaly detection for machine tool rotary components
topic machine tools
predictive maintenance
fingerprint routines
anomaly detection
concept drift
Industry 4.0
url https://www.mdpi.com/2504-4494/9/1/12
work_keys_str_mv AT amaiaarregi endtoendmethodologyforpredictivemaintenancebasedonfingerprintroutinesandanomalydetectionformachinetoolrotarycomponents
AT aitorbarrutia endtoendmethodologyforpredictivemaintenancebasedonfingerprintroutinesandanomalydetectionformachinetoolrotarycomponents
AT inigobediaga endtoendmethodologyforpredictivemaintenancebasedonfingerprintroutinesandanomalydetectionformachinetoolrotarycomponents