Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
Artificial Intelligence—Robotics for Prognostics and Health Management (PHM)
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Artificial Intelligence—Robotics for Prognostics and Health Management (PHM)

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: closed (20 September 2024) | Viewed by 3073

Special Issue Editors

Dr. Ki-yong Oh

E-Mail Website
Guest Editor
School of Mechanical Engineering, Hanyang University, Seoul 04763, Republic of Korea
Interests: artificial intelligence; prognostics and health management; AI-PHM; diagnostics; fault detection; degradation; reliability
Special Issues, Collections and Topics in MDPI journals
Dr. Woochul Nam

E-Mail Website
Guest Editor
School of Mechanical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
Interests: human-machine interface; estimation of gait phase; analysis of bio-signal with machine learning; autonomous control of UAV; indoor positioning with vision; prediction of remaining useful life for structures with deep learning; dynamics of floating structures; domain-adaptation; data synthesis
Dr. TaeWon Seo

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Hanyang University, Seoul 04763, Republic of Korea
Interests: service robot design and control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Robots have become widespread in various fields of manufacturing, medical treatment, service, agriculture, patrol inspection, and even entertainment applications. This progress is synergistically stimulated by the cross-cutting, transformational effects of artificial intelligence (AI) together with the 4th industrial evolution. In particular, artificial intelligence (AI) shows huge enhancement in image/pattern recognition, computer games, and natural language processing, which are useful for prognostics and health management. These surprising capabilities of intelligent robotics allow researchers to make advances in mobility toward PHM. Specifically, intelligent robots are effective for patrol and previse inspection of complex industrial systems at extreme operational conditions. Hence, AI robotics has garnered attention for proactive operation and maintenance together with novel sensors and the Internet of Things.

This Special Issue aims to collect a variety of studies on robotics (and robotics) enhanced by AI in the field of PHM. Novel approaches in robotic inspection systems, robot perception for fault diagnosis, path planning and tracking for patrol inspection, and self-learning with AI for autonomous inspection schemes are welcome, as well as the smart design of inspection robots using AI-generative models. Theoretical contributions, model development, and performance improvements through technology fusion in the aforementioned fields are invited.

Topics of interest include (but are not limited to):

  • Extreme robotics for PHM;
  • Deep learning in grasping and manipulation for inspection robotics;
  • Patrol and precise inspection with mobile robotics;
  • Object detection, segmentation, and categorization;
  • Path planning and tracking with reinforcement learning;
  • Physical human–robot interaction;
  • Generative design of inspection robots with AI;
  • AI based object detection for fault diagnostics and autonomous inspection;
  • Model-based control with deep neural network;
  • Autonomous flight/driving with self-learning;
  • Collision avoidance with novel sensors and AI.

We look forward to receiving your submissions in this Special Issue.

Dr. Ki-yong Oh
Dr. Woochul Nam
Dr. TaeWon Seo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 7347 KiB  
Article
A New Deep Learning Framework for Imbalance Detection of a Rotating Shaft
by Muhammad Wisal and Ki-Yong Oh
Sensors 2023, 23(16), 7141; https://doi.org/10.3390/s23167141 - 12 Aug 2023
Cited by 4 | Viewed by 2102
Abstract
Rotor unbalance is the most common cause of vibration in industrial machines. The unbalance can result in efficiency losses and decreased lifetime of bearings and other components, leading to system failure and significant safety risk. Many complex analytical techniques and specific classifiers algorithms [...] Read more.
Rotor unbalance is the most common cause of vibration in industrial machines. The unbalance can result in efficiency losses and decreased lifetime of bearings and other components, leading to system failure and significant safety risk. Many complex analytical techniques and specific classifiers algorithms have been developed to study rotor imbalance. The classifier algorithms, though simple to use, lack the flexibility to be used efficiently for both low and high numbers of classes. Therefore, a robust multiclass prediction algorithm is needed to efficiently classify the rotor imbalance problem during runtime and avoid the problem’s escalation to failure. In this work, a new deep learning (DL) algorithm was developed for detecting the unbalance of a rotating shaft for both binary and multiclass identification. The model was developed by utilizing the depth and efficacy of ResNet and the feature extraction property of Convolutional Neural Network (CNN). The new algorithm outperforms both ResNet and CNN. Accelerometer data collected by a vibration sensor were used to train the algorithm. This time series data were preprocessed to extract important vibration signatures such as Fast Fourier Transform (FFT) and Short-Time Fourier Transform (STFT). STFT, being a feature-rich characteristic, performs better on our model. Two types of analyses were carried out: (i) balanced vs. unbalanced case detection (two output classes) and (ii) the level of unbalance detection (five output classes). The developed model gave a testing accuracy of 99.23% for the two-class classification and 95.15% for the multilevel unbalance classification. The results suggest that the proposed deep learning framework is robust for both binary and multiclass classification problems. This study provides a robust framework for detecting shaft unbalance of rotating machinery and can serve as a real-time fault detection mechanism in industrial applications. Full article
(This article belongs to the Special Issue Artificial Intelligence—Robotics for Prognostics and Health Management (PHM))
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop