Special Issue "Condition Monitoring and Their Applications in Industry"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: 31 March 2021.

Special Issue Editors

Prof. Dr. Tat-Hean Gan
Website
Guest Editor
Brunel University London, Uxbridge, United Kingdom
Interests: non-destructive testing (NDT); structural health monitoring (SHM) and condition monitoring of rotating machinery
Prof. Dr. David Mba
Website
Guest Editor
Faculty of Computing, Engineering and Media, De Montfort University, The Gateway, Leicester, LE1 9BH, UK
Interests: Condition monitoring, machine fault diagnosis; model based prognostics; machine performance prediction
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Critical assets such as machinery and structures are essential for economic activities across most sectors. Condition monitoring of these critical assets is essential for optimal usage. Such monitoring processes involve detecting faults at an early stage, diagnosing the fault source and monitoring and predicting the fault progression. Achieving these objectives successfully for both machinery and structures requires use of a range of data analysis techniques that are typically developed for the specific application. While numerous theoretical data analysis, machine learning and signal processing techniques have evolved, this Special Edition presents only industrial-application-based papers in which the latest condition monitoring techniques are applied to machinery and structures.

Prof. Dr. Tat-Hean Gan
Prof. Dr. David Mba
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 papers will be 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. Applied Sciences 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 1800 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

  • Condition monitoring
  • Machinery
  • Structures
  • Diagnosis
  • Prognosis

Published Papers (2 papers)

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Research

Open AccessArticle
Energy-Based Prognosis of the Remaining Useful Life of the Coating Segments in Hot Rolling Mill
Appl. Sci. 2020, 10(19), 6827; https://doi.org/10.3390/app10196827 - 29 Sep 2020
Abstract
The field of prognostic maintenance aims at predicting the remaining time for a system or component to continue being used under the desired performance. This time is usually named as Remaining Useful Life (RUL). The current study proposes a novel approach for the [...] Read more.
The field of prognostic maintenance aims at predicting the remaining time for a system or component to continue being used under the desired performance. This time is usually named as Remaining Useful Life (RUL). The current study proposes a novel approach for the RUL estimation of coating segments placed on a hot rolling mill machine. A prediction method was developed, providing real-time updates of the RUL prediction during the rolling milling process. The proposed approach performs energy analysis on measurements of segment surface temperatures and hydraulic forces. It uses nonparametric statistical processes to update the predictions, within a prediction horizon/window, indicating the number of remaining products to be processed. To assess the probability of failure within the defined prediction window, Maximum Likelihood Estimation is used. The proposed methodology was implemented in a software prototype in the MATLAB environment and tested in an industrial use case coming from a steel parts manufacturer, facilitating testing and validation of the suggested approach. Real-world data were acquired from the operational machine, while the validation results support that the proposed methodology demonstrates reasonable performance and robustness against product type variations. Full article
(This article belongs to the Special Issue Condition Monitoring and Their Applications in Industry)
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Open AccessArticle
A Sparse Autoencoder-Based Unsupervised Scheme for Pump Fault Detection and Isolation
Appl. Sci. 2020, 10(19), 6789; https://doi.org/10.3390/app10196789 - 28 Sep 2020
Abstract
Pumps are one of the most critical machines in the petrochemical process. Condition monitoring of such parts and detecting faults at an early stage are crucial for reducing downtime in the production line and improving plant safety, efficiency and reliability. This paper develops [...] Read more.
Pumps are one of the most critical machines in the petrochemical process. Condition monitoring of such parts and detecting faults at an early stage are crucial for reducing downtime in the production line and improving plant safety, efficiency and reliability. This paper develops a fault detection and isolation scheme based on an unsupervised machine learning method, sparse autoencoder (SAE), and evaluates the model on industrial multivariate data. The Mahalanobis distance (MD) is employed to calculate the statistical difference of the residual outputs between monitoring and normal states and is used as a system-wide health indicator. Furthermore, fault isolation is achieved by a reconstruction-based two-dimensional contribution map, in which the variables with larger contributions are responsible for the detected fault. To demonstrate the effectiveness of the proposed scheme, two case studies are carried out based on a multivariate data set from a pump system in an oil and petrochemical factory. The classical principal component analysis (PCA) method is compared with the proposed method and results show that SAE performs better in terms of fault detection than PCA, and can effectively isolate the abnormal variables, which can hence help effectively trace the root cause of the detected fault. Full article
(This article belongs to the Special Issue Condition Monitoring and Their Applications in Industry)
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