Machine Health Diagnosis & Prognosis by Advanced Sensing and Data Driven Techniques

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: closed (15 July 2023) | Viewed by 4460

Special Issue Editors


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Guest Editor
School of Civil Aviation, Northwestern Polytechnical University, Xi'an 710072, China
Interests: predictive maintenance; digital twin; signal processing; machine learning; system reliability analysis; remaining useful life prediction; time–frequency analysis
Special Issues, Collections and Topics in MDPI journals
College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China
Interests: condition monitoring; fault diagnosis; fault prognosis; vibration analysis; signal processing; machine learning
Special Issues, Collections and Topics in MDPI journals
Industrial and Systems Engineering, Slippery Rock University of Pennsylvania, Slippery Rock, PA 16057, USA
Interests: reliability assessment; process improvement; bayesian inference; machine learning; accelerated test design; failure analysis; industrial safety; stochastic degradation modelling

Special Issue Information

Dear Colleagues,

We are pleased to invite you to submit your papers to the Special Issue of Machines on “Machine Health Diagnosis and Prognosis by Advanced Sensing and Data Driven Techniques”. Over the last few decades, researchers have paid close attention to machine diagnosis and prognosis. A multifaceted strategy in terms of diagnostic and prognostic research themes can greatly increase the operational dependability of machine maintenance. In terms of fundamental and engineering applications, modern technologies such as as smart sensing, sophisticated data collection, and intelligent algorithms have made this subject more appealing to researchers. Machine health diagnosis and prognosis are widely used in industries such as aerospace, manufacturing, automotive, infrastructure, and transportation sectors. Given the present state-of-the-art methods in this fast-evolving research field, we are soliciting papers in all areas of machine-health diagnostic and prognostic operations. A comprehensive range of issues in the field of machine diagnostics and prognostics will be discussed, including novel theories, techniques, data gathering processes, optimization algorithms, sensor design, modelling, and so on.

Topics include but are not limited to the following:

(1) Development of dynamic modelling regarding fault inception mechanisms and the degradation of the machine health;

(2)  Signal processing relative to measured data;

(3) Remaining useful life prediction;

(4) Artificial intelligence-based algorithms for machine-health diagnosis and prognosis;

(5) Hybrid fault diagnosis techniques;

(6) Machine-health monitoring under non-stationary time-varying speed conditions;

(7) Structural health monitoring (SHM);

(8) Feature extraction from machine data;

(9) Non-destructive testing (NDT);

(10) Machine learning;

(11) Deep learning.

Dr. Khandaker Noman
Dr. Anil Kumar
Dr. Shah Limon
Dr. Yongbo Li
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. Machines is an international peer-reviewed open access monthly 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 2400 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 (CM)
  • prognostic and health management (PHM)
  • structural health monitoring (SHM)
  • reliability analysis
  • non-destructive evaluation
  • data-driven techniques
  • sensing technology

Published Papers (2 papers)

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Research

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22 pages, 26353 KiB  
Article
A Knowledge Discovery Process Extended to Experimental Data for the Identification of Motor Misalignment Patterns
by Sebastian Bold and Sven Urschel
Machines 2023, 11(8), 827; https://doi.org/10.3390/machines11080827 - 11 Aug 2023
Viewed by 859
Abstract
The diagnosis of misalignment plays a crucial role in the area of maintenance and repair since misalignment can lead to expensive downtime. To address this issue, several solutions have been developed, and both offline and online approaches are available. However, online strategies using [...] Read more.
The diagnosis of misalignment plays a crucial role in the area of maintenance and repair since misalignment can lead to expensive downtime. To address this issue, several solutions have been developed, and both offline and online approaches are available. However, online strategies using a small number of sensors show a higher false positive rate than other approaches. The problem is a lack of knowledge regarding the interrelations of a fault, disturbances during the diagnosis process, and capable features and feature vectors. Knowledge discovery in database is a framework that allows extracting the missing knowledge. For technical systems, optimal results were achieved by aligning (partially) automated experiments with a data mining strategy, in this case classification. The results yield a greater understanding of the interrelations regarding parallel misalignment, i.e., feature vectors that show good results also with varying load and realistic fault levels. Moreover, the test data confirm a specificity (range 0 to 1) for classification between 0.87 and 1 with the found feature vectors. For angular misalignment, potential vectors were identified, but these need further validation with a modified experiment in future work. For the study, two induction motors with 1.1 kW and 7.5 kW were considered. Furthermore, the findings were compared with additional motors of the same rated power. The findings of this work can help to improve the implementation of sensorless diagnostics on machines and advance the research in this field. Full article
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Review

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22 pages, 4490 KiB  
Review
Fiber Optic Fiber Bragg Grating Sensing for Monitoring and Testing of Electric Machinery: Current State of the Art and Outlook
by Asep Andi Suryandi, Nur Sarma, Anees Mohammed, Vidyadhar Peesapati and Siniša Djurović
Machines 2022, 10(11), 1103; https://doi.org/10.3390/machines10111103 - 21 Nov 2022
Cited by 10 | Viewed by 2619
Abstract
This paper presents a review of the recent trends and the current state of the art in the application of fiber optic fiber Bragg gratings (FBG) sensing technology to condition the monitoring (CM) and testing of practical electric machinery and the associated power [...] Read more.
This paper presents a review of the recent trends and the current state of the art in the application of fiber optic fiber Bragg gratings (FBG) sensing technology to condition the monitoring (CM) and testing of practical electric machinery and the associated power equipment. FBG technology has received considerable interest in this field in recent years, with research demonstrating that the flexible, multi-physical, and electromagnetic interference (EMI) immune in situ sensing of a multitude of physical measurands of CM interest is possible and cannot be obtained through conventional sensing means. The unique FBG sensing ability has the potential to unlock many of the electric machine CM and design validation restrictions imposed by the limitations of conventional sensing techniques but needs further research to attain wider adoption. This paper first presents the fundamental principles of FBG sensing. This is followed by a description of recent FBG sensing techniques proposed for electric machinery and associated power equipment and a discussion of their individual benefits and limitations. Finally, an outlook for the further application of this technique is presented. The underlying intention is for the review to provide an up-to-date overview of the state of the art in this area and inform future developments in FBG sensing in electric machinery. Full article
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