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Signals
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Machines and Industrial Equipment Fault Diagnosis Based on Signal Analysis
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Machines and Industrial Equipment Fault Diagnosis Based on Signal Analysis

A special issue of Signals (ISSN 2624-6120).

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 7480

Special Issue Editors

Dr. Viorel Paleu

E-Mail Website
Guest Editor
Mechanical Engineering, Mechatronics and Robotics Department, Mechanical Engineering Faculty, Gheorghe Asachi Technical University of Iasi, 700050 Iași, Romania
Interests: tribology; friction; wear; machine parts; signal processing; data acquisition; automotive; mechanical transmissions; mechanical engineering; lubrication; lubricants; coatings; materials; rolling bearings; diagnosis
Dr. Shubrajit Bhaumik

E-Mail Website
Guest Editor
Tribology and Surface Interaction Research Laboratory, Department of Mechanical Engineering, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu 603203, India
Interests: tribology
Prof. Dr. Viorel Goanţă

E-Mail Website
Guest Editor
Mechanical Engineering, Mechatronics and Robotics Department, “Gheorghe Asachi” Technical University of Iasi, 700050 Iasi, Romania
Interests: fatigue; fracture mechanics; materials strength; sensors; defectoscopy; diagnosis; dynamics; statically analysis; strain measurements; reliability; mechanical testing; expertise in mechanical engineering

Special Issue Information

Dear Colleagues,

Preventive diagnosis of machines and industrial equipment eliminates the risks of catastrophic failures, hence the undesired out-of-service periods of these machines and the risks of accidents and human loss. The vibroacoustic signals are generated each time a fault is manifesting in a certain mechanism or equipment (rolling bearing, gear, electrical motor, compressor, belt transmission, etc.). The diagnosis can be realized online, or by post processing of collected data regarding the monitoring of signals generated by various mechanisms and equipment faults. There are mainly three methods of signal processing and diagnosis, based on processing of the acquired signals in time, frequency, or time-frequency domains. The main problem the researchers and industrial maintenance engineers are facing is represented by the fact that the acquired signal containing the fault features signature is non-stationary. Furthermore, the fault signal is usually of small amplitude and is drowned in lot of noise. The noise is transmitted from the surrounding environment through the machine bed, but it can be also generated by mounting errors, deviations from perfect form of different machine parts composing the assembly, and mainly by worn and faulty machine elements.

Consequently, improvements to the existing diagnosis methods or new methods proposals are welcome, including monitoring, signal decomposition, evaluation and analysis, diagnosis (establishment of failure types and root causes), smart decision and optimized techniques (automatic features recognition, expert system, neural networks, fuzzy logic), application of feedback actions, and final actions (maintenance required or replacement).

Dr. Viorel Paleu
Dr. Shubrajit Bhaumik
Prof. Dr. Viorel Goanţă
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. Signals is an international peer-reviewed open access quarterly 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 1000 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

  • signals
  • vibroacoustic
  • monitoring
  • machines diagnosis
  • industrial equipment diagnosis
  • data acquisition
  • signal decomposition
  • signal processing
  • defectoscopy
  • automatic features recognition technique
  • neural networks
  • expert systems
  • fuzzy logic

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Published Papers (2 papers)

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Research

17 pages, 6144 KiB  
Article
Hybrid Wavelet–CNN Fault Diagnosis Method for Ships’ Power Systems
by Dimitrios Paraskevopoulos, Christos Spandonidis and Fotis Giannopoulos
Signals 2023, 4(1), 150-166; https://doi.org/10.3390/signals4010008 - 8 Feb 2023
Cited by 9 | Viewed by 2364
Abstract
Three-phase induction motors (IMs) are considered an essential part of electromechanical systems. Despite the fact that IMs operate efficiently under harsh environments, there are many cases where they indicate deterioration. A crucial type of fault that must be diagnosed early is stator winding [...] Read more.
Three-phase induction motors (IMs) are considered an essential part of electromechanical systems. Despite the fact that IMs operate efficiently under harsh environments, there are many cases where they indicate deterioration. A crucial type of fault that must be diagnosed early is stator winding faults as a consequence of short circuits. Motor current signature analysis is a promising method for the failure diagnosis of power systems. Wavelets are ideal for both time- and frequency-domain analyses of the electrical current of nonstationary signals. In this paper, the signal data are obtained from simulations of an induction motor for various stator winding fault conditions and one normal operating condition. Our main contribution is the presentation of a fault diagnostic system based on a hybrid discrete wavelet–CNN method. First, the time series of the currents are processed with discrete wavelet analysis. In this way, the harmonic frequencies of the faults are successfully captured, and features can be extracted that comprise valuable information. Next, the features are fed into a convolutional neural network (CNN) model that achieves competitive accuracy and needs significantly reduced training time. The motivations for integrating CNNs into wavelet analysis results for fault diagnosis are as follows: (1) the monitoring is automated, as no human operators are needed to examine the results; (2) deep learning algorithms have the potential to identify even more indistinguishable and complex faults than those that human eyes could. Full article
(This article belongs to the Special Issue Machines and Industrial Equipment Fault Diagnosis Based on Signal Analysis)
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15 pages, 7329 KiB  
Article
Transmission Line Fault Classification of Multi-Dataset Using CatBoost Classifier
by Vincent Nsed Ogar, Sajjad Hussain and Kelum A. A. Gamage
Signals 2022, 3(3), 468-482; https://doi.org/10.3390/signals3030027 - 5 Jul 2022
Cited by 14 | Viewed by 4053
Abstract
Transmission line fault classification forms the basis of fault protection management in power systems. Because faults have adverse effects on transmission lines, adequate measures must be implemented to avoid power outages. This paper focuses on using the categorical boosting (CatBoost) algorithm classifier to [...] Read more.
Transmission line fault classification forms the basis of fault protection management in power systems. Because faults have adverse effects on transmission lines, adequate measures must be implemented to avoid power outages. This paper focuses on using the categorical boosting (CatBoost) algorithm classifier to analyse and train multiple voltage and current data from a 330 kV and 500 km-long simulated faulty transmission line model designed using Matlab/Simulink. From it, 93,340 fault data sizes were extracted. The CatBoost classifier was employed to classify the faults after different machine learning algorithms were used to train the same data with different parameters. The trainer achieved the best accuracy of 99.54%, with an error of 0.46% for 748 iterations out of 1000. The algorithm was selected for its high performance in classifying faults based on accuracy, precision and speed. In addition, it is easy to use and handles multiple data-sets. In contrast, a support vector machine and an artificial neural network each has a longer training time than the proposed method’s 58.5 s. Proper fault classification techniques assist in the effective fault management and planning of power system control thereby preventing energy waste and providing high performance. Full article
(This article belongs to the Special Issue Machines and Industrial Equipment Fault Diagnosis Based on Signal Analysis)
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