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Intelligent Sensing and Automatic Device for Industrial Process

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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 4893

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Special Issue Editors

Dr. Can Zhou

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Guest Editor

School of Automation, Central South University, Changsha 410083, China
Interests: online sensing of industrial parameters; spectral characteristics analysis; intelligent sensing and automatic device

Prof. Dr. Chunhua Yang

E-Mail Website
Guest Editor

School of Automation, Central South University, Changsha 410083, China
Interests: intelligent sensing and automatic device; process modeling and optimal control; industrial big data analysis and deep learning; smart manufacturing for process industry
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Special Issue Information

Dear Colleagues,

Sensing is critical when enabling the industrial monitoring and control system to gain access to internal information about operating statuses and state trajectories. However, the harsh industrial environment, multi-phase field strong coupling, and complex material composition make a bottleneck in obtaining and analyzing vital information in the industrial process. With Industry 4.0 presenting the notions of digitalization and communication, intelligent sensing and automatic device offer a wide range of possibilities for accurate extraction of key features and intelligent analysis of information in industrial application scenarios. To date, the space for in-depth investigations of intelligent sensing-related topics remains open.

We are seeking papers on intelligent sensing and automatic devices for industrial application scenarios, to extract informative features from complex high-dimensional data, and describe complex operating conditions and accurate perception of the target index. This Special Issue will cover the latest original sensing technology and automatic devices, including spectral analysis, radiation detection, visual perception, etc. Intelligent sensing theoretical and experimental works are also welcome, including (but not limited to) sensing principles, sensing methods, and sensing modeling. Both critical reviews and surveys of the cutting-edge practice will be considered.

Dr. Can Zhou
Prof. Dr. Chunhua Yang
Guest Editors

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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.

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Keywords

spectral analysis
radiation detection
visual perception
particle measurement
feature extraction and fusion
working condition characterization
portable instrument

Published Papers (3 papers)

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Research

23 pages, 4908 KiB

Open AccessArticle

Time-Distributed Framework for 3D Reconstruction Integrating Fringe Projection with Deep Learning

by Andrew-Hieu Nguyen and Zhaoyang Wang

Sensors 2023, 23(16), 7284; https://doi.org/10.3390/s23167284 - 20 Aug 2023

Cited by 1 | Viewed by 1282

Abstract

In recent years, integrating structured light with deep learning has gained considerable attention in three-dimensional (3D) shape reconstruction due to its high precision and suitability for dynamic applications. While previous techniques primarily focus on processing in the spatial domain, this paper proposes a novel time-distributed approach for temporal structured-light 3D shape reconstruction using deep learning. The proposed approach utilizes an autoencoder network and time-distributed wrapper to convert multiple temporal fringe patterns into their corresponding numerators and denominators of the arctangent functions. Fringe projection profilometry (FPP), a well-known temporal structured-light technique, is employed to prepare high-quality ground truth and depict the 3D reconstruction process. Our experimental findings show that the time-distributed 3D reconstruction technique achieves comparable outcomes with the dual-frequency dataset (

p

= 0.014) and higher accuracy than the triple-frequency dataset (

p

= 1.029 ×

10^{- 9}

), according to non-parametric statistical tests. Moreover, the proposed approach’s straightforward implementation of a single training network for multiple converters makes it more practical for scientific research and industrial applications. Full article

(This article belongs to the Special Issue Intelligent Sensing and Automatic Device for Industrial Process)

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14 pages, 2670 KiB

Open AccessArticle

Failure Severity Prediction for Protective-Coating Disbondment via the Classification of Acoustic Emission Signals

by Noor A’in A. Rahman, Zazilah May, Rabeea Jaffari and Mehwish Hanif

Sensors 2023, 23(15), 6833; https://doi.org/10.3390/s23156833 - 31 Jul 2023

Viewed by 757

Abstract

Structural health monitoring is a popular inspection method that utilizes acoustic emission (AE) signals for fault detection in engineering infrastructures. Diagnosis based on the propagation of AE signals along any surface material offers an attractive solution for fault identification. However, the classification of AE signals originating from failure events, especially coating failure (coating disbondment), is a challenging task given the AE signature of each material. Thus, different experimental settings and analyses of AE signals are required to classify the various types of coating failures, and they are time-consuming and expensive. Hence, to address these issues, we utilized machine learning (ML) classification models in this work to evaluate epoxy-based-protective-coating disbondment based on the AE principle. A coating disbondment experiment consisting of coated carbon steel test panels for the collection of AE signals was implemented. The obtained AE signals were then processed to construct the final dataset to train various state-of-the-art ML classification models to divide the failure severity of coating disbondment into three classes. Consequently, methods for the extraction of useful features, the handling of data imbalance, and a reduction in the bias of ML models were also effectively utilized in this study. Evaluations of state-of-the-art ML classification models on the AE signal dataset in terms of standard metrics revealed that the decision forest classification model outperformed the other state-of-the-art models, with accuracy, precision, recall, and F1 score values of 99.48%, 98.76%, 97.58%, and 98.17%, respectively. These results demonstrate the effectiveness of utilizing ML classification models for the failure severity prediction of protective-coating defects via AE signals. Full article

(This article belongs to the Special Issue Intelligent Sensing and Automatic Device for Industrial Process)

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17 pages, 2523 KiB

Open AccessArticle

Inferential Composition Control of a Distillation Column Using Active Disturbance Rejection Control with Soft Sensors

by Fahad Al Kalbani and Jie Zhang

Sensors 2023, 23(2), 1019; https://doi.org/10.3390/s23021019 - 16 Jan 2023

Viewed by 2020

Abstract

This paper presents the integration of active disturbance rejection control (ADRC) with soft sensors for enhancing the composition control performance in a distillation column. Static and dynamic soft sensors are developed to estimate the top and bottom product compositions using multiple tray temperatures. In order to cope with the collinearity issues in tray temperature measurements, static and dynamic principal component regression is used in developing the soft sensors. The soft sensor outputs are introduced as the feedback signals to ADRC. This control scheme is termed as “inferential ADRC control”. Static control offsets are eliminated through mean updating in the soft-sensor models. The effectiveness of the proposed control scheme is demonstrated on a benchmark simulated methanol-water distillation column. Full article

(This article belongs to the Special Issue Intelligent Sensing and Automatic Device for Industrial Process)

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