Next Article in Journal
Renewable Energy Technology: Transient 3D CFD and Experimental Electrical Evaluation of a Cycloidal-Enhanced Rotor Versus a Savonius and Gorlov-Savonius Rotor with Blade Rotation Angle
Previous Article in Journal
Graph Attention Network with Mutual k-Nearest Neighbor Strategy for Predictive Maintenance in Nuclear Power Plants
Previous Article in Special Issue
AUV Intelligent Decision-Making System Empowered by Deep Learning: Evolution, Challenges and Future Prospects
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Technologies
Volume 14
Issue 1
10.3390/technologies14010025
technologies-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Intelligent Fault Detection in the Mechanical Structure of a Wheeled Mobile Robot

by
Viorel Ionuț Gheorghe
1,
Laurențiu Adrian Cartal
1,*,
Constantin Daniel Comeagă
1,
Bogdan-Costel Mocanu
2,
Alexandra Rotaru
3,
Mircea-Iulian Nistor
1,
Mihai-Vlad Vartic
1 and
Ștefana Arina Tăbușcă
4
1
Faculty of Mechanical Engineering and Mechatronics, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania
2
Faculty of Automatic Control and Computers, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania
3
Faculty of Industrial Engineering and Robotics, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania
4
Interdisciplinary School of Doctoral Studies, Faculty of Interdisciplinary Studies, University of Bucharest, 050107 Bucharest, Romania
*
Author to whom correspondence should be addressed.
Technologies 2026, 14(1), 25; https://doi.org/10.3390/technologies14010025 (registering DOI)
Submission received: 16 November 2025 / Revised: 18 December 2025 / Accepted: 27 December 2025 / Published: 1 January 2026
(This article belongs to the Special Issue Emerging Paradigms in AI, Autonomous Systems, and Intelligent Technologies)
Versions Notes

Abstract

This paper establishes an integrated framework combining self-induced vibration measurements with deep learning for vibration-based remaining useful life (RUL) prediction of mechanical frame structures in mobile robots. The main innovations comprise (1) a self-induced vibration excitation system that utilizes the robot’s drive wheels to generate controlled mechanical oscillations, using a five-sensor micro-electro-mechanical system (MEMS) accelerometer array to capture non-uniform vibration mode shapes across the robot’s structure, and (2) a processing pipeline for RUL prediction using accelerometer data and early feature fusion in two machine-learning models (long short-term memory (LSTM) and a convolutional neural network (CNN)). Our research methodology includes (i) modal analysis to identify the robot’s natural frequencies, (ii) verification platform evaluation, comparing low-cost MEMS accelerometers against a reference integrated electronic piezoelectric (IEPE) accelerometer, demonstrating industrial-grade measurement quality (coherence > 98%, uncertainty 4.79–7.21%), and (iii) data-driven validation using real data from the mechanical frame, showing that the LSTM model outperforms the CNN with a 2.61× root-mean-square error (RMSE) improvement (R² = 0.99). Our solution demonstrates that early feature fusion provides sufficient information to model degradation and detect faults early at a lower cost, offering a feasible alternative to classical maintenance procedures through combined hardware validation and lightweight software suitable for Industrial Internet-of-Things (IIoT) deployment.
Keywords: mobile robotics; fault detection; vibration; machine learning; predictive maintenance; anomaly detection; mechatronics mobile robotics; fault detection; vibration; machine learning; predictive maintenance; anomaly detection; mechatronics

Share and Cite

MDPI and ACS Style

Gheorghe, V.I.; Cartal, L.A.; Comeagă, C.D.; Mocanu, B.-C.; Rotaru, A.; Nistor, M.-I.; Vartic, M.-V.; Tăbușcă, Ș.A. Intelligent Fault Detection in the Mechanical Structure of a Wheeled Mobile Robot. Technologies 2026, 14, 25. https://doi.org/10.3390/technologies14010025

AMA Style

Gheorghe VI, Cartal LA, Comeagă CD, Mocanu B-C, Rotaru A, Nistor M-I, Vartic M-V, Tăbușcă ȘA. Intelligent Fault Detection in the Mechanical Structure of a Wheeled Mobile Robot. Technologies. 2026; 14(1):25. https://doi.org/10.3390/technologies14010025

Chicago/Turabian Style

Gheorghe, Viorel Ionuț, Laurențiu Adrian Cartal, Constantin Daniel Comeagă, Bogdan-Costel Mocanu, Alexandra Rotaru, Mircea-Iulian Nistor, Mihai-Vlad Vartic, and Ștefana Arina Tăbușcă. 2026. "Intelligent Fault Detection in the Mechanical Structure of a Wheeled Mobile Robot" Technologies 14, no. 1: 25. https://doi.org/10.3390/technologies14010025

APA Style

Gheorghe, V. I., Cartal, L. A., Comeagă, C. D., Mocanu, B.-C., Rotaru, A., Nistor, M.-I., Vartic, M.-V., & Tăbușcă, Ș. A. (2026). Intelligent Fault Detection in the Mechanical Structure of a Wheeled Mobile Robot. Technologies, 14(1), 25. https://doi.org/10.3390/technologies14010025

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Article metric data becomes available approximately 24 hours after publication online.
Back to TopTop