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Smart Sensing for Failure Diagnosis in Structures and Machine Components
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Smart Sensing for Failure Diagnosis in Structures and Machine Components

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (25 October 2023) | Viewed by 11950

Special Issue Editors

Prof. Dr. Andrew G Starr

E-Mail Website
Guest Editor
School of Aerospace, Transport and Manufacturing, Building 50, Cranfield University, College Road, Cranfield MK43 0AL, UK
Interests: maintenance of machine systems; data fusion; fault diagnostics and prognostics
Special Issues, Collections and Topics in MDPI journals
Dr. Muhammad Khan

E-Mail Website
Guest Editor
Centre for Life-Cycle Engineering and Management, Cranfield University, College Road, Cranfield MK43 0AL, UK
Interests: damage mechanics; maintenance engineering; asset management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Catastropic performance failures in structures and machine components are always considered high risk during their working. In the past, conventional sensing tools were used to diagnose the failure prior to any significant physical  damage or reduction in performance. Failure diagnosis is now being performed in real time and in an effective manner due to the evolution in digital technologies, data communication rate, cloud storage, intelligent algorithms for big data, and virtual and augmented reality-based assessment. Sensing frameworks are now becoming smart with desired automation.

This Special Issue will provide research focusing on failure diagnosis in structures and machine components with the help of smart sensing elements and technologies. Research using both invasive and non-invasive sensing for failure diagnosis are invited.

Prof. Dr. Andrew G Starr
Dr. Muhammad Khan
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. Sensors 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 2600 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

  • failure diagnosis
  • smart sensing
  • structure health monitoring
  • near real time diagnosis
  • invasive and non invasive sensing
  • big data
  • intelligent algorithms

Published Papers (6 papers)

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Research

25 pages, 11691 KiB  
Article
Enhancement Methods of Hydropower Unit Monitoring Data Quality Based on the Hierarchical Density-Based Spatial Clustering of Applications with a Noise–Wasserstein Slim Generative Adversarial Imputation Network with a Gradient Penalty
by Fangqing Zhang, Jiang Guo, Fang Yuan, Yuanfeng Qiu, Pei Wang, Fangjuan Cheng and Yifeng Gu
Sensors 2024, 24(1), 118; https://doi.org/10.3390/s24010118 - 25 Dec 2023
Viewed by 888
Abstract
In order to solve low-quality problems such as data anomalies and missing data in the condition monitoring data of hydropower units, this paper proposes a monitoring data quality enhancement method based on HDBSCAN-WSGAIN-GP, which improves the quality and usability of the condition monitoring [...] Read more.
In order to solve low-quality problems such as data anomalies and missing data in the condition monitoring data of hydropower units, this paper proposes a monitoring data quality enhancement method based on HDBSCAN-WSGAIN-GP, which improves the quality and usability of the condition monitoring data of hydropower units by combining the advantages of density clustering and a generative adversarial network. First, the monitoring data are grouped according to the density level by the HDBSCAN clustering method in combination with the working conditions, and the anomalies in this dataset are detected, recognized adaptively and cleaned. Further combining the superiority of the WSGAIN-GP model in data filling, the missing values in the cleaned data are automatically generated by the unsupervised learning of the features and the distribution of real monitoring data. The validation analysis is carried out by the online monitoring dataset of the actual operating units, and the comparison experiments show that the clustering contour coefficient (SCI) of the HDBSCAN-based anomaly detection model reaches 0.4935, which is higher than that of the other comparative models, indicating that the proposed model has superiority in distinguishing between the valid samples and anomalous samples. The probability density distribution of the data filling model based on WSGAIN-GP is similar to that of the measured data, and the KL dispersion, JS dispersion and Hellinger’s distance of the distribution between the filled data and the original data are close to 0. Compared with the filling methods such as SGAIN, GAIN, KNN, etc., the effect of data filling with different missing rates is verified, and the RMSE error of data filling with WSGAIN-GP is lower than that of other comparative models. The WSGAIN-GP method has the lowest RMSE error under different missing rates, which proves that the proposed filling model has good accuracy and generalization, and the research results in this paper provide a high-quality data basis for the subsequent trend prediction and state warning. Full article
(This article belongs to the Special Issue Smart Sensing for Failure Diagnosis in Structures and Machine Components)
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19 pages, 1876 KiB  
Article
A Multiplier-Free Convolution Neural Network Hardware Accelerator for Real-Time Bearing Condition Detection of CNC Machinery
by Yu-Pei Liang, Ming-You Hung and Ching-Che Chung
Sensors 2023, 23(23), 9437; https://doi.org/10.3390/s23239437 - 27 Nov 2023
Viewed by 566
Abstract
In various industrial domains, machinery plays a pivotal role, with bearing failure standing out as the most prevalent cause of malfunction, contributing to approximately 41% to 44% of all operational breakdowns. To address this issue, this research employs a lightweight neural network, boasting [...] Read more.
In various industrial domains, machinery plays a pivotal role, with bearing failure standing out as the most prevalent cause of malfunction, contributing to approximately 41% to 44% of all operational breakdowns. To address this issue, this research employs a lightweight neural network, boasting a mere 8.69 K parameters, tailored for implementation on an FPGA (field-programmable gate array). By integrating an incremental network quantization approach and fixed-point operation techniques, substantial memory savings amounting to 63.49% are realized compared to conventional 32-bit floating-point operations. Moreover, when executed on an FPGA, this work facilitates real-time bearing condition detection at an impressive rate of 48,000 samples per second while operating on a minimal power budget of just 342 mW. Remarkably, this system achieves an accuracy level of 95.12%, showcasing its effectiveness in predictive maintenance and the prevention of costly machinery failures. Full article
(This article belongs to the Special Issue Smart Sensing for Failure Diagnosis in Structures and Machine Components)
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26 pages, 4103 KiB  
Article
Suitability Analysis of Machine Learning Algorithms for Crack Growth Prediction Based on Dynamic Response Data
by Intisar Omar, Muhammad Khan and Andrew Starr
Sensors 2023, 23(3), 1074; https://doi.org/10.3390/s23031074 - 17 Jan 2023
Cited by 3 | Viewed by 2688
Abstract
Machine learning has the potential to enhance damage detection and prediction in materials science. Machine learning also has the ability to produce highly reliable and accurate representations, which can improve the detection and prediction of damage compared to the traditional knowledge-based approaches. These [...] Read more.
Machine learning has the potential to enhance damage detection and prediction in materials science. Machine learning also has the ability to produce highly reliable and accurate representations, which can improve the detection and prediction of damage compared to the traditional knowledge-based approaches. These approaches can be used for a wide range of applications, including material design; predicting material properties; identifying hidden relationships; and classifying microstructures, defects, and damage. However, researchers must carefully consider the appropriateness of various machine learning algorithms, based on the available data, material being studied, and desired knowledge outcomes. In addition, the interpretability of certain machine learning models can be a limitation in materials science, as it may be difficult to understand the reasoning behind predictions. This paper aims to make novel contributions to the field of material engineering by analyzing the compatibility of dynamic response data from various material structures with prominent machine learning approaches. The purpose of this is to help researchers choose models that are both effective and understandable, while also enhancing their understanding of the model’s predictions. To achieve this, this paper analyzed the requirements and characteristics of commonly used machine learning algorithms for crack propagation in materials. This analysis assisted the authors in selecting machine learning algorithms (K nearest neighbor, Ridge, and Lasso regression) to evaluate the dynamic response of aluminum and ABS materials, using experimental data from previous studies to train the models. The results showed that natural frequency was the most significant predictor for ABS material, while temperature, natural frequency, and amplitude were the most important predictors for aluminum. Crack location along samples had no significant impact on either material. Future work could involve applying the discussed techniques to a wider range of materials under dynamic loading conditions. Full article
(This article belongs to the Special Issue Smart Sensing for Failure Diagnosis in Structures and Machine Components)
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24 pages, 7564 KiB  
Article
Diagnosis of Multiple Faults in Rotating Machinery Using Ensemble Learning
by Udeme Ibanga Inyang, Ivan Petrunin and Ian Jennions
Sensors 2023, 23(2), 1005; https://doi.org/10.3390/s23021005 - 15 Jan 2023
Cited by 4 | Viewed by 2913
Abstract
Fault diagnosis of rotating machines is an important task to prevent machinery downtime, and provide verifiable support for condition-based maintenance (CBM) decision-making. Deep learning-enabled fault diagnosis operations have become increasingly popular because features are extracted and selected automatically. However, it is challenging for [...] Read more.
Fault diagnosis of rotating machines is an important task to prevent machinery downtime, and provide verifiable support for condition-based maintenance (CBM) decision-making. Deep learning-enabled fault diagnosis operations have become increasingly popular because features are extracted and selected automatically. However, it is challenging for these models to give superior results with rotating machine components of different scales, single and multiple faults across different rotating components, diverse operating speeds, and diverse load conditions. To address these challenges, this paper proposes a comprehensive learning approach with optimized signal processing transforms for single as well as multiple faults diagnosis across dissimilar rotating machine components: gearbox, bearing, and shaft. The optimized bicoherence, spectral kurtosis and cyclic spectral coherence feature spaces, and deep blending ensemble learning are explored for multiple faults diagnosis of these components. The performance analysis of the proposed approach has been demonstrated through a single joint training of the entire framework on a compound dataset containing multiple faults derived from three public repositories. A comparison with the state-of-the-art approaches that used these datasets, shows that our method gives improved results with different components and faults with nominal retraining. Full article
(This article belongs to the Special Issue Smart Sensing for Failure Diagnosis in Structures and Machine Components)
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19 pages, 4143 KiB  
Article
Displacement Estimation Using 3D-Printed RFID Arrays for Structural Health Monitoring
by Metin Pekgor, Reza Arablouei, Mostafa Nikzad and Syed Masood
Sensors 2022, 22(22), 8811; https://doi.org/10.3390/s22228811 - 15 Nov 2022
Cited by 2 | Viewed by 1738
Abstract
Radio frequency identification (RFID) tags are small, low-cost, wearable, and wireless sensors that can detect movement in structures, humans, or robots. In this paper, we use passive RFID tags for structural health monitoring by detecting displacements. We employ a novel process of using [...] Read more.
Radio frequency identification (RFID) tags are small, low-cost, wearable, and wireless sensors that can detect movement in structures, humans, or robots. In this paper, we use passive RFID tags for structural health monitoring by detecting displacements. We employ a novel process of using 3D printable embedded passive RFID tags within uniform linear arrays together with the multiple signal classification algorithm to estimate the direction of arrival using only the phase of the backscattered signals. We validate our proposed approach via data collected from real-world experiments using a unipolar RFID reader antenna and both narrowband and wideband measurements. Full article
(This article belongs to the Special Issue Smart Sensing for Failure Diagnosis in Structures and Machine Components)
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17 pages, 4329 KiB  
Article
Optical Rail Surface Crack Detection Method Based on Semantic Segmentation Replacement for Magnetic Particle Inspection
by Lei Kou, Mykola Sysyn, Szabolcs Fischer, Jianxing Liu and Olga Nabochenko
Sensors 2022, 22(21), 8214; https://doi.org/10.3390/s22218214 - 26 Oct 2022
Cited by 11 | Viewed by 2131
Abstract
Railway damage detection is of great significance in ensuring railway safety. The cracks on the rail surface play a key role in studying the formation and development process of rail damage, predicting the occurrence of rail defects, and then improving the service life [...] Read more.
Railway damage detection is of great significance in ensuring railway safety. The cracks on the rail surface play a key role in studying the formation and development process of rail damage, predicting the occurrence of rail defects, and then improving the service life of the rail. However, due to the small shape of the cracks, the typical detection method is relatively complicated, and the speed is quite slow. Although traditional magnetic particle inspection technology is fairly accurate at detection, it is costly and inconvenient to carry and install, while also limiting the detection speed and affecting the system’s operation. In this paper, a semantic segmentation detection method is developed by using various collected rail surface crack data and deep learning through a neural network. By comparing the inspection of the same rail surface with magnetic particle inspection technology, only inexpensive cameras are used and the inspection speed is increased while maintaining relatively high accuracy. In addition, the method can achieve fast detection speeds if it is extended to be combined with high-frequency cameras. It is an economical, efficient, and environmentally friendly method for future rail surface detection. Full article
(This article belongs to the Special Issue Smart Sensing for Failure Diagnosis in Structures and Machine Components)
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