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Editorial

Fault Diagnosis of Rotating Machine

by
Grzegorz Królczyk
1,
Zhixiong Li
2,* and
Jose Alfonso Antonino Daviu
3
1
Department of Manufacturing Engineering and Production Automation, Opole University of Technology, 45-758 Opole, Poland
2
School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong, Wollongong, NSW 2522, Australia
3
School of Industrial Engineering, Universitat de València, 46010 València, Spain
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(6), 1961; https://doi.org/10.3390/app10061961
Submission received: 24 February 2020 / Accepted: 25 February 2020 / Published: 13 March 2020
(This article belongs to the Special Issue Fault Diagnosis of Rotating Machine)
Versions Notes

1. Introduction

Rotating machines have been used in a wide variety of industries, such as manufacturing tools [1,2,3,4,5,6,7], electric motors [8,9,10,11,12,13,14,15,16,17], wind turbines [18,19,20,21], aero-engines [22,23], mining machines [24,25,26,27], marine propulsions [28,29,30,31] and autonomous vehicles [32,33,34,35,36]. A tiny defect or a simple crack may result in severe damages to rotating machines [37]. As a result, it is crucial to monitor the health condition of rotating machines through active diagnostic and prognostic technologies [38]. In recent years, many detection techniques have been developed. These technologies include vibration analysis [18], oil analysis [30], thermography analysis [17], acoustic analysis [9], flux analysis [16], etc. This Special Issue invites original research papers that report on the state of the art and recent advancements in the fault diagnosis of rotating machines, so as to further promote practical applications of diagnostic and prognostic technologies.

2. Content

There are 18 papers selected for this Special Issue, representing the latest progress and advances in the diagnostics and prognostics of rotating machines. Regarding these 18 research papers, the following four research directions are specifically addressed:
Bearing elements fault detection: This Special Issue includes eight excellent examples of improving bearing fault detection. In the first paper, Duan et al. [39] improved the fault detection rate of a general rolling bearing by combining the local mean decomposition (LMD) and the ratio correction methods. Then, Cui et al. [40] discussed the diagnosis of multiple defects in a rolling bearing via vibration analysis; Shi et al. [41] reported a frequency matching linear transform technique for bearing fault detection under variable rotating speeds; moreover, Yin et al. [42] proposed a Huffman coding technique to identify bearing defect severity. By what follows, artificially intelligent techniques, including ensemble learning [43] and deep learning [44,45] were developed to detect bearing faults. In the last paper, the authors presented the application of acoustic emission analysis for bearing fault diagnosis [46].
Motor fault detection: Another four excellent papers have addressed motor fault detection. Goh and Kim [47] investigated the short circuit problem in an induction motor. Ishikawa and Igarashi [48] analyzed the demagnetization of a permanent magnet synchronous motor using finite element analysis. Glowacz [48] applied the acoustics analysis and Lee et al. [49] adopted the deep learning for motor failure detection.
Machine-level fault detection: This Special Issue includes five interesting papers for machine-level modelling and condition monitoring. Song et al. [50] diagnosed a superconducting rotating machine; Dineva et al. [51] applied fault pattern recognition for a rotating electrical Machine; Ding et al. [52] developed a vibration-based method to monitor a mining machine; Lv et al. [53] analyzed the multiple faults of the Apollo manned lunar landing system; and Xie et al. [54] modelled a marine platform based on temperature measurements.
Rotor fault detection: The last paper contained in this Special Issue presents a crack detection method for a rotor system [55].

3. Summary

Diagnostic and prognostic technologies for rotating machines are growing every month. The described work in this Special Issue has made a good contribution to the fault diagnosis of rotating machines. This collection of 18 papers is highly recommended and is believed to benefit readers in various aspects.

Author Contributions

All the authors contributed equally to the conception of the idea, implementing and analyzing the experimental results, and writing the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Australia Research Council under grant number DE190100931.

Acknowledgments

The Guest Editors would like to thank all authors, reviewers and the editorial board of the MDPI Applied Science journal, and especially Xiaoyan Chen (Assistant Managing Editor) for their valuable contributions to this Special Issue.

Conflicts of Interest

The author declares no conflict of interest.

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Share and Cite

MDPI and ACS Style

Królczyk, G.; Li, Z.; Antonino Daviu, J.A. Fault Diagnosis of Rotating Machine. Appl. Sci. 2020, 10, 1961. https://doi.org/10.3390/app10061961

AMA Style

Królczyk G, Li Z, Antonino Daviu JA. Fault Diagnosis of Rotating Machine. Applied Sciences. 2020; 10(6):1961. https://doi.org/10.3390/app10061961

Chicago/Turabian Style

Królczyk, Grzegorz, Zhixiong Li, and Jose Alfonso Antonino Daviu. 2020. "Fault Diagnosis of Rotating Machine" Applied Sciences 10, no. 6: 1961. https://doi.org/10.3390/app10061961

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