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Sensing Technologies for Fault Diagnostics and Prognosis: 2nd Edition

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

Deadline for manuscript submissions: 30 June 2024 | Viewed by 1448

Special Issue Editor

Special Issue Information

Dear Colleagues,

Machinery and mechanical structures in the industry suffer from inevitable degradation and performance degradation during operation. By collecting and processing data using a variety of sensors, timely diagnosis of symptoms of deterioration and reliable estimation of future health conditions are essential for industrial productivity and reliability. Models consisting of sensor data measured in the past using AI technology have shown great potential for fault diagnosis and prognosis in industrial equipment. AI-powered technologies will become more important in the future as the deployment of Internet of Things and cloud-based technologies for stateful maintenance makes vast amounts of measurement data available for decision making. This Special Issue will focus on fault diagnosis and prognosis of industrial equipment and mechanical structures using a variety of sensors. Sensor-based artificial neural network technology, explainable AI solutions, objects for error diagnosis and prognosis in the context of Industry 4.0, cloud computing, cyber-physical systems, and machine-to-machine interfaces and paradigms are welcome.

Prof. Dr. Jongmyon Kim
Guest Editor

Manuscript Submission Information

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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.

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.

Keywords

  • sensor-based artificial neural network technology
  • explainable AI solutions
  • objects for error diagnosis and prognosis in the context of Industry 4.0
  • cloud computing
  • cyber-physical systems
  • machine-to-machine interfaces and paradigms

Published Papers (1 paper)

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Research

14 pages, 10508 KiB  
Article
A Reliable Pipeline Leak Detection Method Using Acoustic Emission with Time Difference of Arrival and Kolmogorov–Smirnov Test
by Duc-Thuan Nguyen, Tuan-Khai Nguyen, Zahoor Ahmad and Jong-Myon Kim
Sensors 2023, 23(23), 9296; https://doi.org/10.3390/s23239296 - 21 Nov 2023
Viewed by 1119
Abstract
This paper proposes a novel and reliable leak-detection method for pipeline systems based on acoustic emission (AE) signals. The proposed method analyzes signals from two AE sensors installed on the pipeline to detect leaks located between these two sensors. Firstly, the raw AE [...] Read more.
This paper proposes a novel and reliable leak-detection method for pipeline systems based on acoustic emission (AE) signals. The proposed method analyzes signals from two AE sensors installed on the pipeline to detect leaks located between these two sensors. Firstly, the raw AE signals are preprocessed using empirical mode decomposition. The time difference of arrival (TDOA) is then extracted as a statistical feature of the two AE signals. The state of the pipeline (leakage/normal) is determined through comparing the statistical distribution of the TDOA of the current state with the prior normal state. Specifically, the two-sample Kolmogorov–Smirnov (K–S) test is applied to compare the statistical distribution of the TDOA feature for leak and non-leak scenarios. The K–S test statistic value in this context functions as a leakage indicator. A new criterion called leak sensitivity is introduced to evaluate and compare the performance of leak detection methods. Extensive experiments were conducted using an industrial pipeline system, and the results demonstrate the excellence of the proposed method in leak detection. Compared to traditional feature-based indicators, our approach achieves a significantly higher performance in leak detection. Full article
(This article belongs to the Special Issue Sensing Technologies for Fault Diagnostics and Prognosis: 2nd Edition)
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