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Special Issue "Monitoring and Fault Identification Based on Artificial Intelligence Methods"

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

Deadline for manuscript submissions: 30 November 2023 | Viewed by 3391

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Special Issue Editors

Dr. Juan Jose Saucedo-Dorantes

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Guest Editor

Engineering Faculty, Autonomous University of Queretaro, San Juan del Rio 76806, Mexico
Interests: condition monitoring; fault identification; feature extraction; feature reduction; novelty detection; vibration analysis; mcsa; artificial intelligence

Dr. Miguel Delgado-Prieto

E-Mail Website
Guest Editor

Automatic Control Department, Universitat Politècnica de Catalunya, 08222 Terrassa, Barcelona, Spain
Interests: industry 4.0 and digital transformation; condition-based monitoring; predictive maintenance and control; cyber–physical systems and digital twins
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Special Issue Information

Dear Colleagues,

Condition monitoring strategies play an important key role in the fault identification in rotating machines leading to determining the current status and the future evolution/degradation of health conditions. Currently, Artificial Intelligence (AI) allows proposing novel monitoring structures to overcome recent challenges in the field of fault diagnosis. Therefore, this Special Issue is focused on but is not limited to the following topics:

Condition monitoring;
Fault detection and identification;
Rotating machines;
Complex signal processing applied to transient and stationary regimes;
Feature calculation, feature extraction, and feature selection;
Smart sensors for fault detection in Industry 4.0;
Artificial intelligence methods.

Dr. Juan Jose Saucedo-Dorantes
Dr. Miguel Delgado-Prieto
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.

Published Papers (4 papers)

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Research

Open AccessArticle

Forecasting the Dynamic Response of Rotating Machinery under Sudden Load Changes

Juan Carlos Jauregui-Correa

Machines 2023, 11(9), 857; https://doi.org/10.3390/machines11090857 - 26 Aug 2023

Viewed by 174

Abstract

This paper analyzes vibration data that shows sudden amplitude changes due to non-stationary load conditions. The data were recorded in a wind turbine that operated under gusty winds and showed high peaks during short periods. Data were analyzed with the auto-regressive integrated moving average (ARIMA) algorithm, and the results were compared to the exponential forecasting method. Other methods have been applied for forecasting vibration data, but the simplicity of this method makes it suitable for rotating machinery with high variable loading conditions. The analysis of the method’s parameters is included in this paper, and the results showed that the optimum configuration depends on the data variations and the existence of significant trends. Forecasting vibration data is challenging; it depends on the source data quality, the preprocessing algorithms, and the deterioration of the mechanical elements. Predictions become less accurate when the machine operates under sudden changes, and evaluating damaging effects caused by the sudden event is difficult to estimate. Full article

(This article belongs to the Special Issue Monitoring and Fault Identification Based on Artificial Intelligence Methods)

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Open AccessArticle

A CNN-Based Methodology for Identifying Mechanical Faults in Induction Motors Using Thermography

Omar Trejo-Chavez

Irving A. Cruz-Albarran

Emmanuel Resendiz-Ochoa

Alejandro Salinas-Aguilar

Luis A. Morales-Hernandez

Jesus A. Basurto-Hurtado

and

Carlos A. Perez-Ramirez

Machines 2023, 11(7), 752; https://doi.org/10.3390/machines11070752 - 18 Jul 2023

Viewed by 612

Abstract

Infrared thermography (IRT) has become an interesting alternative for performing condition assessments of different types of induction motor (IM)-based equipment when it operates under harsh conditions. The reported results from state-of-the-art articles that have analyzed thermal images do not consider (1): the presence of more than one fault, and (2) the inevitable noise-corruption the images suffer. Bearing in mind these reasons, this paper presents a convolutional neural network (CNN)-based methodology that is specifically designed to deal with noise-corrupted images for detecting the failures that have the highest incidence rate: bearing and broken bar failures; moreover, rotor misalignment failure is also considered, as it can cause a further increase in electricity consumption. The presented results show that the proposal is effective in detecting healthy and failure states, as well as identifying the failure nature, as a 95% accuracy is achieved. These results allow considering the proposal as an interesting alternative for using IRT images obtained in hostile environments. Full article

(This article belongs to the Special Issue Monitoring and Fault Identification Based on Artificial Intelligence Methods)

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Open AccessArticle

Statistical Machine Learning Strategy and Data Fusion for Detecting Incipient ITSC Faults in IM

Arturo Yosimar Jaen-Cuellar

David Alejandro Elvira-Ortiz

and

Juan Jose Saucedo-Dorantes

Machines 2023, 11(7), 720; https://doi.org/10.3390/machines11070720 - 07 Jul 2023

Viewed by 719

Abstract

The new technological developments have allowed the evolution of the industrial process to this new concept called Industry 4.0, which integrates power machines, robotics, smart sensors, communication systems, and the Internet of Things to have more reliable automation systems. However, electrical rotating machines like the Induction Motor (IM) are still widely used in several industrial applications because of their robust elements, high efficiency, and versatility in industrial applications. Nevertheless, the occurrence of faults in IMs is inherent to their operating conditions; hence, Inter-turn short-circuit (ITSC) is one of the most common failures that affect IMs, and its appearance is due to electrical stresses leading to the degradation of the stator winding insulation. In this regard, this work proposes a diagnosis methodology capable of performing the assessment and automatic detection of incipient electric faults like ITSC in IMs; the proposed method is supported through the processing of different physical magnitudes such as vibration, stator currents and magnetic stray-flux and their fusion of information. Certainly, the novelty and contribution include the characterization of different physical magnitudes by estimating a set of statistical time domain features, as well as their fusion following a feature-level fusion approach and their reduction through the Linear discriminant Analysis technique. Furthermore, the fusion and reduction of information from different physical magnitudes lead to performing automatic fault detection and identification by a simple Neural-Network (NN) structure since all considered conditions can be represented in a 2D plane. The proposed method is evaluated under a complete set of experimental data, and the obtained results demonstrate that the fusion of information from different sources (physical magnitudes) can lead to achieving a global classification ratio of up to 99.4% during the detection of ITSC in IMs and an improvement higher than 30% in comparison with classical approaches that consider the analysis of a unique physical magnitude. Additionally, the results make this proposal feasible to be incorporated as a part of condition-based maintenance programs in the industry. Full article

(This article belongs to the Special Issue Monitoring and Fault Identification Based on Artificial Intelligence Methods)

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Open AccessArticle

Expert System Based on Autoencoders for Detection of Broken Rotor Bars in Induction Motors Employing Start-Up and Steady-State Regimes

Martin Valtierra-Rodriguez

Jesus Rooney Rivera-Guillen

J. Jesus De Santiago-Perez

Gerardo Israel Perez-Soto

and

Juan Pablo Amezquita-Sanchez

Machines 2023, 11(2), 156; https://doi.org/10.3390/machines11020156 - 23 Jan 2023

Cited by 2 | Viewed by 980

Abstract

Induction motors are indispensable, robust, and reliable machines for industry; however, as with any machine, they are susceptible to diverse faults. Among the faults that a motor can suffer, broken rotor bars (BRBs) have become one of the most studied ones because the motor under this fault condition can continue operating with apparent normality, yet the fault severity can quickly increase and, consequently, generate the whole collapse of the motor, raising repair costs and the risk to people or other machines around it. This work proposes an expert system to detect BRB early, i.e., half-BRB, 1-BRB, and 2-BRB, from the current signal analysis by considering the following two operating regimes: start-up transient and steady-state. The method can diagnose the BRB condition by using either one regime or both regimes, where the objective is to somehow increase the reliability of the result. Regarding the proposed expert system, it consists of the application of two autoencoders, i.e., one per regime, to diagnose the BRB condition. To automatically separate the regimes of analysis and obtain the envelope of the current signal, the Hilbert transform is applied. Then, the particle swarm optimization method is implemented to compute the separation point of both regimes in the current signal. Once the signal is separated, the two autoencoders and a simple set of if-else rules are employed to automatically determine the BRB condition. The proposed expert system proved to be an effective tool, with 100% accuracy in diagnosing all BRB conditions. Full article

(This article belongs to the Special Issue Monitoring and Fault Identification Based on Artificial Intelligence Methods)

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