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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

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Research

19 pages, 7347 KiB

Open AccessArticle

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 1293

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 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))

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