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Applied Sciences
Special Issues
Technical Diagnostics and Predictive Maintenance
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Technical Diagnostics and Predictive Maintenance

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

Deadline for manuscript submissions: 31 August 2024 | Viewed by 9005

Special Issue Editors

Dr. Tibor Krenicky

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Guest Editor
Department of Technical Systems Design and Monitoring, Faculty of Manufacturing Technologies with a Seat in Prešov, Technical University of Košice, Bayerova 1, 080 01 Prešov, Slovakia
Interests: technical diagnostics; virtual instrumentation; mechanical engineering; nanomaterials
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ján Piteľ

E-Mail Website
Guest Editor
Department of Industrial Engineering and Informatics, Faculty of Manufacturing Technologies with a Seat in Presov, Technical University of Kosice, 080 01 Presov, Slovakia
Interests: monitoring and control of machines; mechatronic systems
Special Issues, Collections and Topics in MDPI journals
Dr. Kamil Židek

E-Mail Website
Guest Editor
Department of Industrial Engineering and Informatics, Faculty of Manufacturing Technologies with a seat in Presov, Technical University of Kosice, 080 01 Presov, Slovakia
Interests: data acquisition; digital twins; identification technologies

Special Issue Information

Dear Colleagues,

The main aim of this Special Issue is to present state-of-the-art research on the theory, modelling, monitoring, and diagnostics of the operation of technical systems, data processing, and analysis focused on fault detection, as well as predictive maintenance theory and methods.

Objects of the research should be investigated using specific models, tools, and instruments along with their verification and evaluation of the operational states of technical systems. The knowledge, methods, technical systems, and applications presented in this Special Issue illustrate the strong potential to attract and impress researchers and other professionals, and will contribute to providing answers to outstanding questions or asking questions that have yet to be formulated.

Contributions should focus primarily on:

  • Technical diagnostic methods and systems;
  • Diagnostics of machine and technical system operational states;
  • Optimization of machinery operation and service using diagnostic methods;
  • Use of novel methods and technologies in technical diagnostics and maintenance;
  • Online monitoring, digital twins, data acquisition, and signal processing;
  • Machine learning and AI-based methods in technical diagnostics and predictive maintenance;
  • Diagnostics and maintenance utilization of virtualized systems;
  • Advanced inspection methods;
  • Diagnostics of drives (electric, pneumatic, etc.);
  • Technical systems operation quality and reliability assessment;
  • Technical system operation modelling and characterization;
  • Functional surface properties characterization;
  • Structural characterization of materials for defects identification;
  • Aspects of implementing technical diagnostics and predictive maintenance;
  • Safety and health protection aspects of diagnostics and maintenance.

Dr. Tibor Krenicky
Prof. Dr. Ján Piteľ
Dr. Kamil Židek
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. 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 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

  • technical diagnostics
  • predictive maintenance
  • machine learning
  • operational states
  • technical system reliability

Published Papers (7 papers)

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Research

15 pages, 1480 KiB  
Article
Impacts of Feature Selection on Predicting Machine Failures by Machine Learning Algorithms
by Francisco Elânio Bezerra, Geraldo Cardoso de Oliveira Neto, Gabriel Magalhães Cervi, Rafaella Francesconi Mazetto, Aline Mariane de Faria, Marcos Vido, Gustavo Araujo Lima, Sidnei Alves de Araújo, Mauro Sampaio and Marlene Amorim
Appl. Sci. 2024, 14(8), 3337; https://doi.org/10.3390/app14083337 - 15 Apr 2024
Viewed by 418
Abstract
In the context of Industry 4.0, managing large amounts of data is essential to ensure informed decision-making in intelligent production environments. It enables, for example, predictive maintenance, which is essential for anticipating and identifying causes of failures in machines and equipment, optimizing processes, [...] Read more.
In the context of Industry 4.0, managing large amounts of data is essential to ensure informed decision-making in intelligent production environments. It enables, for example, predictive maintenance, which is essential for anticipating and identifying causes of failures in machines and equipment, optimizing processes, and promoting proactive management of human, financial, and material resources. However, generating accurate information for decision-making requires adopting suitable data preprocessing and analysis techniques. This study explores the identification of machine failures based on synthetic industrial data. Initially, we applied the feature selection techniques Principal Component Analysis (PCA), Minimum Redundancy Maximum Relevance (mRMR), Neighborhood Component Analysis (NCA), and Denoising Autoencoder (DAE) to the collected data and compared their results. In the sequence, a comparison among three widely known machine learning classifiers, namely Random Forest (RF), Support Vector Machine (SVM), and Multilayer Perceptron neural network (MLP), was conducted, with and without considering feature selection. The results showed that PCA and RF were superior to the other techniques, allowing the classification of failures with rates of 0.98, 0.97, and 0.98 for the accuracy, precision, and recall metrics, respectively. Thus, this work contributes by solving an industrial problem and detailing techniques to identify the most relevant variables and machine learning algorithms for predicting machine failures that negatively impact production planning. The findings provided by this study can assist industries in giving preference to employing sensors and collecting data that can contribute more effectively to machine failure predictions. Full article
(This article belongs to the Special Issue Technical Diagnostics and Predictive Maintenance)
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19 pages, 1917 KiB  
Article
Improving Deep Learning Anomaly Diagnostics with a Physics-Based Simulation Model
by Teemu Mäkiaho, Kari T. Koskinen and Jouko Laitinen
Appl. Sci. 2024, 14(2), 800; https://doi.org/10.3390/app14020800 - 17 Jan 2024
Viewed by 715
Abstract
Deep learning algorithms often struggle to accurately distinguish between healthy and anomalous states due to the scarcity of high-quality data in real-world applications. However, these data can be obtained through a physics-based simulation model. In this research, the model serves a dual purpose: [...] Read more.
Deep learning algorithms often struggle to accurately distinguish between healthy and anomalous states due to the scarcity of high-quality data in real-world applications. However, these data can be obtained through a physics-based simulation model. In this research, the model serves a dual purpose: detecting anomalies in industrial processes and replicating the machine’s operational behavior with high fidelity in terms of a simulated torque signal. When anomalous behaviors are detected, their patterns are utilized to generate anomalous events, contributing to the enhancement of deep neural network model training. This research proposes a method, named Simulation-Enhanced Anomaly Diagnostics (SEAD), to detect anomalies and further create high-quality data related to the diagnosed faults in the machine’s operation. The findings of this study suggest that employing a physics-based simulation model as a synthetic-anomaly signal generator can significantly improve the classification accuracy of identified anomalous states, thereby enhancing the deep learning model’s ability to recognize deviating behavior at an earlier stage when more high-quality data of the identified anomaly has been available for the learning process. This research measures the classification capability of a Long Short-Term Memory (LSTM) autoencoder to classify anomalous behavior in different SEAD stages. The validated results clearly demonstrate that simulated data can contribute to the LSTM autoencoder’s ability to classify anomalies in a peripheral milling machine. The SEAD method is employed to test its effectiveness in detecting and replicating a failure in the support element of the peripheral milling machine. Full article
(This article belongs to the Special Issue Technical Diagnostics and Predictive Maintenance)
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16 pages, 10021 KiB  
Article
Design and Testing of a Measurement Device for High-Speed Bearing Evaluation
by Michal Duhancik, Tibor Krenicky and Tomas Coranic
Appl. Sci. 2024, 14(2), 508; https://doi.org/10.3390/app14020508 - 06 Jan 2024
Viewed by 681
Abstract
The submitted article focuses on the proposal of a testing device for researching alternative methods of lubricating technical systems, specifically high-speed rolling bearings using lubricants containing nanoparticles. The aim of this research is to verify the functionality of the proposed technical device, whose [...] Read more.
The submitted article focuses on the proposal of a testing device for researching alternative methods of lubricating technical systems, specifically high-speed rolling bearings using lubricants containing nanoparticles. The aim of this research is to verify the functionality of the proposed technical device, whose main task is to ensure the measurement of the functional and operational characteristics of high-speed rolling bearings. The proposed technical device allows us to carry out a series of measurements, primarily for the purpose of selecting specific bearings and secondarily for the purpose of conducting technical diagnostic measurements. The results of these measurements are significant in the selection of suitable nano-particle additives for the lubrication of the tested bearings. The tests was carried out using speeds reaching 110,000 rpm. Methods of monitoring vibrational and acoustic diagnostics were chosen for the analysis of the operational processes of loaded technical systems. Full article
(This article belongs to the Special Issue Technical Diagnostics and Predictive Maintenance)
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20 pages, 5349 KiB  
Article
Research Method for Ship Engine Fault Diagnosis Based on Multi-Head Graph Attention Feature Fusion
by Zeren Ai, Hui Cao, Jihui Wang, Zhichao Cui, Longde Wang and Kuo Jiang
Appl. Sci. 2023, 13(22), 12421; https://doi.org/10.3390/app132212421 - 16 Nov 2023
Viewed by 798
Abstract
At present, there are problems such as low fault data, insufficient labeling information, and poor fault diagnosis in the field of ship engine diagnosis. To address the above problems, this paper proposes a fault diagnosis method based on probabilistic similarity and rank-order similarity [...] Read more.
At present, there are problems such as low fault data, insufficient labeling information, and poor fault diagnosis in the field of ship engine diagnosis. To address the above problems, this paper proposes a fault diagnosis method based on probabilistic similarity and rank-order similarity of multi-head graph attention neural networks (MPGANN) models. Firstly, the ship engine dataset is used to explore the similarity between the data using the probabilistic similarity of T_SNE and the rank order similarity of Spearman’s correlation coefficient to define the neighbor relationship between the samples, and then the appropriate weights are selected for the early fusion of the two graph structures to fuse the feature information of the two scales. Finally, the graph attention neural networks (GANN) incorporating the multi-head attention mechanism are utilized to complete the fault diagnosis. In this paper, comparative experiments such as graph construction and algorithm performance are carried out based on the simulated ship engine dataset, and the experimental results show that the MPGANN outperforms the comparative methods in terms of accuracy, F1 score, and total elapsed time, with an accuracy rate of 97.58%. The experimental results show that the model proposed in this paper can still fulfill the ship engine fault diagnosis task well under unfavorable conditions such as small samples and insufficient label information, which is of practical significance in the field of intelligent ship cabins and fault diagnosis. Full article
(This article belongs to the Special Issue Technical Diagnostics and Predictive Maintenance)
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23 pages, 5831 KiB  
Article
Anomalistic Symptom Judgment Algorithm for Predictive Maintenance of Ship Propulsion Engine Using Machine Learning
by Jinkyu Park and Jungmo Oh
Appl. Sci. 2023, 13(21), 11818; https://doi.org/10.3390/app132111818 - 29 Oct 2023
Viewed by 725
Abstract
Ships serve as crucial transporters of cargo and passengers in substantial volumes and operate for a long time; therefore, an efficient maintenance system is essential for economical and stable vessel operation. In this study, a machine learning based approach was developed that considers [...] Read more.
Ships serve as crucial transporters of cargo and passengers in substantial volumes and operate for a long time; therefore, an efficient maintenance system is essential for economical and stable vessel operation. In this study, a machine learning based approach was developed that considers the rapidly changing load fluctuations on ships and large variability in normal operation data to apply predictive maintenance to the propulsion engines of ships. After acquiring propulsion engine data from the alarm monitoring system of a ship, data and maintenance items were analyzed to select the data that could determine the anomalistic symptoms of the propulsion engine. Further, the main engine condition criterion value was defined as the factor for anomalistic symptom prediction. An engine anomalistic symptom judgment algorithm that can be practically used for ship maintenance prediction was developed and verified using machine learning. Full article
(This article belongs to the Special Issue Technical Diagnostics and Predictive Maintenance)
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17 pages, 2544 KiB  
Article
Identification of Selected Failures in a Pipe Conveyor’s Operation with the Use of the Discrimination Method Based on Continuous Measurement
by Vieroslav Molnár, Gabriel Fedorko, Beáta Stehlíková, Peter Michalik and Daniel Koštial
Appl. Sci. 2023, 13(12), 6864; https://doi.org/10.3390/app13126864 - 06 Jun 2023
Cited by 1 | Viewed by 942
Abstract
This paper deals with research on the operational process monitoring of a pipe conveyor for the needs of online diagnostics. The aim of this research is to verify the possibility of identifying the selected pipe conveyor’s failures in its straight section during operation [...] Read more.
This paper deals with research on the operational process monitoring of a pipe conveyor for the needs of online diagnostics. The aim of this research is to verify the possibility of identifying the selected pipe conveyor’s failures in its straight section during operation (a missing roller in the idler housing, absent material on the conveyor belt) with the use of a discrimination method. This is an attempt to implement digital transformation with the aim of verifying its possibilities and limitations. The basis for discrimination is a continuous measurement and evaluation of measured values of contact forces in certain rollers’ positions in the hexagonal idler housing. Within this research, eight different measurement regimes were implemented. The use of the method was verified with simulated data using the trace table. We aimed to create prerequisites for online monitoring, which, based on digital transformation, will be deployed to control a transport system. The measurement was realized with the maximum tension force of 28,000 N. From the measurements, a decision-making algorithm was proposed to identify selected failures in the pipe conveyor operation with the use of the discrimination method. Within the algorithm, classifying criteria were determined, in the range of 57 N ÷ 251 N. The results confirm the method’s suitability for its practical assurance of pipe conveyors’ failure-free operation, as the failures were always identified sufficiently in advance, thanks to which, in practice, there was no further damage to the diagnosed devices. Full article
(This article belongs to the Special Issue Technical Diagnostics and Predictive Maintenance)
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17 pages, 1643 KiB  
Article
Decision Framework for Predictive Maintenance Method Selection
by Wieger Tiddens, Jan Braaksma and Tiedo Tinga
Appl. Sci. 2023, 13(3), 2021; https://doi.org/10.3390/app13032021 - 03 Feb 2023
Cited by 2 | Viewed by 3585
Abstract
Many asset owners and maintainers have the ambition to better predict the future state of their equipment to make timely and better-informed maintenance decisions. Although many methods to support high-level maintenance policy selection are available, practitioners still often follow a costly trial-and-error process [...] Read more.
Many asset owners and maintainers have the ambition to better predict the future state of their equipment to make timely and better-informed maintenance decisions. Although many methods to support high-level maintenance policy selection are available, practitioners still often follow a costly trial-and-error process in selecting the most suitable predictive maintenance method. To address the lack of decision support in this process, this paper proposes a framework to support asset owners in selecting the optimal predictive maintenance method for their situation. The selection framework is developed using a design science process. After exploring common difficulties, a set of solutions is proposed for these identified problems, including a classification of the various maintenance methods, a guideline for defining the ambition level for the maintenance process, and a classification of the available data types. These elements are then integrated into a framework that assists practitioners in selecting the optimal maintenance approach. Finally, the proposed framework is successfully tested and demonstrated using four industrial case studies. It can be concluded that the proposed classifications of ambition levels, data types and types of predictive maintenance methods clarify and accelerate the complex selection process considerably. Full article
(This article belongs to the Special Issue Technical Diagnostics and Predictive Maintenance)
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