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Machines
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Deep Learning and Machine Health Monitoring
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Deep Learning and Machine Health Monitoring

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

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 10705

Special Issue Editors

Dr. Amare Desalegn Fentaye

E-Mail Website
Guest Editor
School of Business Society and Engineering, Division of Automation in Energy and Environmental Engineering, Mälardalen University, 72123 Vasteras, Sweden
Interests: predictive maintenance; fault diagnostics; prognostics; intelligent decision support; deep learning; explainable AI; transfer learning
Prof. Dr. Konstantinos Kyprianidis

E-Mail Website
Guest Editor
School of Business Society and Engineering, Division of Automation in Energy and Environmental Engineering, Mälardalen University, 72123 Vasteras, Sweden
Interests: energy and environment; process control; system analysis; design optimization; mechanical and gas turbine engineering; aerospace; defense
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

One way to increase the profitability of machines is to reduce operational and maintenance expenses by improving reliability and availability through effective predictive maintenance technologies. Recent advancements in computational intelligence and machine learning methods enable us to apply more sophisticated diagnostic and prognostic techniques.

This Special Issue desires state-of-the art and original research articles focusing on advances in all aspects of machinery diagnostics and prognostics. We invite researchers in academia and industry to contribute papers that demonstrate novel research ideas and findings, present new predictive maintenance systems and real-time applications, demonstrate successful deep-learning-based diagnostic and prognostic algorithms, and state-of-the-art review articles that summarize recent advancements in machinery health management technologies, challenges, opportunities, and the way forward.

Dr. Amare Desalegn Fentaye
Prof. Dr. Konstantinos Kyprianidis
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

  • health monitoring
  • predictive maintenance
  • fault diagnostics and prognostics
  • remaining useful life prediction
  • AI-enabled diagnostics
  • deep learning
  • transfer learning
  • explainable AI

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

19 pages, 5737 KiB  
Article
Adaptive Dynamic Threshold Graph Neural Network: A Novel Deep Learning Framework for Cross-Condition Bearing Fault Diagnosis
by Linjie Zheng, Yonghua Jiang, Hongkui Jiang, Chao Tang, Weidong Jiao, Zhuoqi Shi and Attiq Ur Rehman
Machines 2024, 12(1), 18; https://doi.org/10.3390/machines12010018 - 28 Dec 2023
Viewed by 1676
Abstract
Recently, bearing fault diagnosis methods based on deep learning have achieved significant success. However, in practical engineering applications, the limited labeled data and various working conditions severely constrain the widespread application of most deep-learning-based fault diagnosis methods. Additionally, many methods focus solely on [...] Read more.
Recently, bearing fault diagnosis methods based on deep learning have achieved significant success. However, in practical engineering applications, the limited labeled data and various working conditions severely constrain the widespread application of most deep-learning-based fault diagnosis methods. Additionally, many methods focus solely on the amplitude information of samples, neglecting the rich relational information between samples. To address these issues, this paper proposes a novel cross-condition few-shot fault diagnosis method based on an adaptive dynamic threshold graph neural network (ADTGNN). The aim of the proposed method is to rapidly identify fault types after they occur only a few times or even once. The adaptive threshold computation module (ATCM) in ADTGNN dynamically assigns thresholds to each edge based on edge confidence, optimizing the graph structure and effectively alleviating the over-smoothing issue. Furthermore, a dynamic threshold adjustment strategy (DTAS) is introduced to gradually increase the threshold with the training iterations, preventing the model from prematurely discarding crucial edges due to insufficient performance. The proposed model’s effectiveness is demonstrated using three bearing datasets. The experimental results indicate that the proposed approach significantly outperforms other comparison methods in cross-condition bearing fault diagnosis. Full article
(This article belongs to the Special Issue Deep Learning and Machine Health Monitoring)
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22 pages, 4213 KiB  
Article
Holistic Approach Promotes Failure Prevention of Smart Mining Machines Based on Bayesian Networks
by Madeleine Martinsen, Amare Desalegn Fentaye, Erik Dahlquist and Yuanye Zhou
Machines 2023, 11(10), 940; https://doi.org/10.3390/machines11100940 - 2 Oct 2023
Viewed by 2285
Abstract
In the forthcoming era of fully autonomous mining, spanning from drilling operations to port logistics, novel approaches will be essential to pre-empt hazardous situations in the absence of human intervention. The progression towards complete autonomy in mining operations must have meticulous approaches and [...] Read more.
In the forthcoming era of fully autonomous mining, spanning from drilling operations to port logistics, novel approaches will be essential to pre-empt hazardous situations in the absence of human intervention. The progression towards complete autonomy in mining operations must have meticulous approaches and uncompromised security. By ensuring a secure transition, the mining industry can navigate the transformative shift towards autonomy while upholding the highest standards of safety and operational reliability. Experiments involving autonomous pathways for mining machinery that utilize AI for route optimization demonstrate a higher speed capacity than manually operated approaches; this translates to enhanced productivity, subsequently fostering increased production capacity to meet the rising demand for metals. Nonetheless, accelerated wear on crucial elements like tires, brakes, and bearings on mining machines has been observed. Autonomous mining processes will require smarter machines without humans that guide and support actions prior to a hazardous situation occurring. This paper will delve into a comprehensive perspective on the safety of autonomous mining machines by using Bayesian networks (BN) to detect possible hazard fires. The BN is tuned with a combination of empirical field data and laboratory data. Various faults have been recognized, and their correlation with the measurements has been established. Full article
(This article belongs to the Special Issue Deep Learning and Machine Health Monitoring)
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17 pages, 2856 KiB  
Article
Deep Learning for Enhanced Fault Diagnosis of Monoblock Centrifugal Pumps: Spectrogram-Based Analysis
by Prasshanth Chennai Viswanathan, Sridharan Naveen Venkatesh, Seshathiri Dhanasekaran, Tapan Kumar Mahanta, Vaithiyanathan Sugumaran, Natrayan Lakshmaiya, Prabhu Paramasivam and Sakthivel Nanjagoundenpalayam Ramasamy
Machines 2023, 11(9), 874; https://doi.org/10.3390/machines11090874 - 31 Aug 2023
Cited by 34 | Viewed by 2398
Abstract
The reliable operation of monoblock centrifugal pumps (MCP) is crucial in various industrial applications. Achieving optimal performance and minimizing costly downtime requires effectively detecting and diagnosing faults in critical pump components. This study proposes an innovative approach that leverages deep transfer learning techniques. [...] Read more.
The reliable operation of monoblock centrifugal pumps (MCP) is crucial in various industrial applications. Achieving optimal performance and minimizing costly downtime requires effectively detecting and diagnosing faults in critical pump components. This study proposes an innovative approach that leverages deep transfer learning techniques. An accelerometer was adopted to capture vibration signals emitted by the pump. These signals are then converted into spectrogram images which serve as the input for a sophisticated classification system based on deep learning. This enables the accurate identification and diagnosis of pump faults. To evaluate the effectiveness of the proposed methodology, 15 pre-trained networks including ResNet-50, InceptionV3, GoogLeNet, DenseNet-201, ShuffleNet, VGG-19, MobileNet-v2, InceptionResNetV2, VGG-16, NasNetmobile, EfficientNetb0, AlexNet, ResNet-18, Xception, ResNet101 and ResNet-18 were employed. The experimental results demonstrate the efficacy of the proposed approach with AlexNet exhibiting the highest level of accuracy among the pre-trained networks. Additionally, a meticulous evaluation of the execution time of the classification process was performed. AlexNet achieved 100.00% accuracy with an impressive execution (training) time of 17 s. This research provides invaluable insights into applying deep transfer learning for fault detection and diagnosis in MCP. Using pre-trained networks offers an efficient and precise solution for this task. The findings of this study have the potential to significantly enhance the reliability and maintenance practices of MCP in various industrial settings. Full article
(This article belongs to the Special Issue Deep Learning and Machine Health Monitoring)
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42 pages, 11226 KiB  
Article
Simultaneous Fault Diagnostics for Three-Shaft Industrial Gas Turbine
by Waleligne Molla Salilew, Syed Ihtsham Gilani, Tamiru Alemu Lemma, Amare Desalegn Fentaye and Konstantinos G. Kyprianidis
Machines 2023, 11(8), 832; https://doi.org/10.3390/machines11080832 - 16 Aug 2023
Cited by 1 | Viewed by 1869
Abstract
The study focused on the development of -gas turbine full- and part-load operation diagnostics. The gas turbine performance model was developed using commercial software and validated using the engine manufacturer data. Upon the validation, fouling, erosion, and variable inlet guide vane drift were [...] Read more.
The study focused on the development of -gas turbine full- and part-load operation diagnostics. The gas turbine performance model was developed using commercial software and validated using the engine manufacturer data. Upon the validation, fouling, erosion, and variable inlet guide vane drift were simulated to generate faulty data for the diagnostics development. Because the data from the model was noise-free, sensor noise was added to each of the diagnostic set parameters to reflect the actual scenario of the field operation. The data was normalized. In total, 13 single, and 61 double, classes, including 1 clean class, were prepared and used as input. The number of observations for single faults diagnostics were 1092, which was 84 for each class, and 20,496 for double faults diagnostics, which was 336 for each class. Twenty-eight machine learning techniques were investigated to select the one which outperformed the others, and further investigations were conducted with it. The diagnostics results show that the neural network group exhibited better diagnostic accuracy at both full- and part-load operations. The test results and its comparison with literature results demonstrated that the proposed method has a satisfactory and reliable accuracy in diagnosing the considered fault scenarios. The results are discussed, following the plots. Full article
(This article belongs to the Special Issue Deep Learning and Machine Health Monitoring)
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20 pages, 3818 KiB  
Article
Synergistic Effect of Physical Faults and Variable Inlet Guide Vane Drift on Gas Turbine Engine
by Waleligne Molla Salilew, Syed Ihtsham Gilani, Tamiru Alemu Lemma, Amare Desalegn Fentaye and Konstantinos G. Kyprianidis
Machines 2023, 11(8), 789; https://doi.org/10.3390/machines11080789 - 1 Aug 2023
Cited by 1 | Viewed by 1599
Abstract
This study presents a comprehensive analysis of the impact of variable inlet guide vanes and physical faults on the performance of a three-shaft gas turbine engine operating at full load. By utilizing the input data provided by the engine manufacturer, the performance models [...] Read more.
This study presents a comprehensive analysis of the impact of variable inlet guide vanes and physical faults on the performance of a three-shaft gas turbine engine operating at full load. By utilizing the input data provided by the engine manufacturer, the performance models for both the design point and off-design scenarios have been developed. To ensure the accuracy of our models, validation was conducted using the manufacturer’s data. Once the models were successfully validated, various degradation conditions, such as variable inlet guide vane drift, fouling, and erosion, were simulated. Three scenarios that cause gas turbine degradation have been considered and simulated: First, how would the variable inlet guide vane drift affect the gas turbine performance? Second, how would the combined effect of fouling and variable inlet guide vane drift cause the degradation of the engine performance? Third, how would the combined effect of erosion and variable inlet guide vane drift cause the degradation of the engine performance? The results revealed that up-VIGV drift, which is combined fouling and erosion, shows a small deviation because of offsetting the isentropic efficiency drop caused by fouling and erosion. It is clearly observed that fouling affects more upstream components, whereas erosion affects more downstream components. Furthermore, the deviation of performance and output parameters due to the combined faults has been discussed. Full article
(This article belongs to the Special Issue Deep Learning and Machine Health Monitoring)
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