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Fatigue-Sensing Technologies for Manufacturing Materials and Machinery Parts
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Fatigue-Sensing Technologies for Manufacturing Materials and Machinery Parts

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 4709

Special Issue Editors

Dr. Zepeng Liu

E-Mail Website
Guest Editor
School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Interests: digital signal processing; tool condition monitoring; fault diagnosis; power systems analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Matthew Armstrong

E-Mail Website
Guest Editor
School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Interests: power converters; real-time digital control; electric drives; system identification\parameter estimation; hardware In the loop (HiL) emulation
Dr. Libing Cao

E-Mail Website
Guest Editor
School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Interests: electric machines and drives; electric transportation; renewable energies; wireless power transfer

Special Issue Information

Dear Colleagues,

This Special Issue on "Fatigue-Sensing Technologies for Manufacturing Materials and Machinery Parts" presents the latest scientific developments in fatigue detection and monitoring for manufacturing materials and machinery parts.

With advanced sensing technologies, including various sensor types such as strain gauges, piezoelectric sensors, fiber optic sensors, and so on, the reliability and lifespan of critical industrial components and parts can be examined and measured. These sensors are integrated into machinery parts to provide the real-time monitoring of material conditions, allowing for the early detection of fatigue-related issues before a catastrophe occurs. In addition, nondestructive testing (NDT) methods in fatigue monitoring such as ultrasonic testing, X-ray diffraction, and acoustic emission are explored for their ability to detect surface cracks and other damage without compromising the integrity of the parts. These NDT methods are essential for routine inspections and quality control in manufacturing environments.

Data analysis and machine learning techniques in fatigue monitoring can predict the onset of fatigue and estimate the remaining useful life of components. These predictive models are crucial for implementing condition-based maintenance strategies, which help prevent unexpected downtime and reduce maintenance costs.

Dr. Zepeng Liu
Dr. Matthew Armstrong
Dr. Libing Cao
Guest Editors

Deepak Makwana
Guest Editor Assistant 

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

  • fatigue sensing
  • predictive maintenance
  • machine learning
  • nondestructive testing (NDT)
  • material fatigue
  • digital manufacturing
  • structural health monitoring

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Published Papers (4 papers)

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Research

20 pages, 11784 KiB  
Article
An Optimized Maximum Second-Order Cyclostationary Blind Deconvolution and Bidirectional Long Short-Term Memory Network Model for Rolling Bearing Fault Diagnosis
by Jixin Liu, Liwei Deng, Yue Cao, Chenglin Wen, Zhihuan Song, Mei Liu and Xiaowei Cui
Sensors 2025, 25(5), 1495; https://doi.org/10.3390/s25051495 - 28 Feb 2025
Viewed by 412
Abstract
To address the challenge of extracting fault features and accurately identifying bearing fault conditions under strong noisy environments, a rolling bearing failure diagnostic technique is presented that utilizes parameter-optimized maximum second-order cyclostationary blind deconvolution (CYCBD) and bidirectional long short-term memory (BiLSTM) networks. Initially, [...] Read more.
To address the challenge of extracting fault features and accurately identifying bearing fault conditions under strong noisy environments, a rolling bearing failure diagnostic technique is presented that utilizes parameter-optimized maximum second-order cyclostationary blind deconvolution (CYCBD) and bidirectional long short-term memory (BiLSTM) networks. Initially, an adaptive golden jackal optimization (GJO) algorithm is employed to refine important CYCBD parameters. Subsequently, the rolling bearing failure signals are filtered and denoised using the optimized CYCBD, producing a denoised signal. Ultimately, the noise-reduced signal is fed into the BiLSTM model to realize the classification of faults. The experimental findings demonstrate the suggested approach’s strong noise reduction performance and high diagnostic accuracy. The optimized CYCBD–BiLSTM improves the accuracy by approximately 9.89% compared with other methods when the signal-to-noise ratio (SNR) reaches −9 dB, and it can be effectively used for diagnosing rolling bearing faults under noisy backgrounds. Full article
(This article belongs to the Special Issue Fatigue-Sensing Technologies for Manufacturing Materials and Machinery Parts)
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16 pages, 7912 KiB  
Article
An Indicator Based on Spatial Coordinate Information for Assessing the Capability for Dynamic Machining Performance of Five-Axis Flank Milling
by Chenglin Yao, Gaiyun He, Yicun Sang, Chen Yue, Yichen Yan and Sitong Wang
Sensors 2024, 24(22), 7229; https://doi.org/10.3390/s24227229 - 12 Nov 2024
Viewed by 681
Abstract
As a spatial coordinate sensor, the touch-trigger on-machine probe is a key equipment in manufacturing that ensures machining quality, and it has played an important role in five-axis flank milling. However, in flank milling, the utilization of the deviation as a conventional indicator [...] Read more.
As a spatial coordinate sensor, the touch-trigger on-machine probe is a key equipment in manufacturing that ensures machining quality, and it has played an important role in five-axis flank milling. However, in flank milling, the utilization of the deviation as a conventional indicator for quality assessment of the machining performance is incomprehensive without considering the characteristics of the machining method. In this paper, the error mutual moment is introduced as an indicator to assess the capability for dynamic machining performance of the machine tool in flank milling based on the spatial coordinate information of the touch-trigger on-machine probe considering the characteristic of the error distribution of the flank milling. Experiments are carried out to validate the advantages of the error mutual moment to assess the capability for dynamic machining performance compared with the deviation. Results show that the error mutual moment shows more significant discrepancies than the deviation in assessing the capability for dynamic machining performance of flank milling. The error mutual moment has the potential to be applied as a quality assessment sensor. Full article
(This article belongs to the Special Issue Fatigue-Sensing Technologies for Manufacturing Materials and Machinery Parts)
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21 pages, 12816 KiB  
Article
KAN-HyperMP: An Enhanced Fault Diagnosis Model for Rolling Bearings in Noisy Environments
by Jun Wang, Zhilin Dong and Shuang Zhang
Sensors 2024, 24(19), 6448; https://doi.org/10.3390/s24196448 - 5 Oct 2024
Cited by 3 | Viewed by 1499
Abstract
Rolling bearings often produce non-stationary signals that are easily obscured by noise, particularly in high-noise environments, making fault detection a challenging task. To address this challenge, a novel fault diagnosis approach based on the Kolmogorov–Arnold Network-based Hypergraph Message Passing (KAN-HyperMP) model is proposed. [...] Read more.
Rolling bearings often produce non-stationary signals that are easily obscured by noise, particularly in high-noise environments, making fault detection a challenging task. To address this challenge, a novel fault diagnosis approach based on the Kolmogorov–Arnold Network-based Hypergraph Message Passing (KAN-HyperMP) model is proposed. The KAN-HyperMP model is composed of three key components: a neighbor feature aggregation block, a feature fusion block, and a KANLinear block. Firstly, the neighbor feature aggregation block leverages hypergraph theory to integrate information from more distant neighbors, aiding in the reduction of noise impact, even when nearby neighbors are severely affected. Subsequently, the feature fusion block combines the features of these higher-order neighbors with the target node’s own features, enabling the model to capture the complete structure of the hypergraph. Finally, the smoothness properties of B-spline functions within the Kolmogorov–Arnold Network (KAN) are employed to extract critical diagnostic features from noisy signals. The proposed model is trained and evaluated on the Southeast University (SEU) and Jiangnan University (JNU) Datasets, achieving accuracy rates of 99.70% and 99.10%, respectively, demonstrating its effectiveness in fault diagnosis under both noise-free and noisy conditions. Full article
(This article belongs to the Special Issue Fatigue-Sensing Technologies for Manufacturing Materials and Machinery Parts)
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22 pages, 11803 KiB  
Article
SSG-Net: A Multi-Branch Fault Diagnosis Method for Scroll Compressors Using Swin Transformer Sliding Window, Shallow ResNet, and Global Attention Mechanism (GAM)
by Zhiwei Xu, Tao Liu, Zezhou Xia, Yanan Fan, Min Yan and Xu Dang
Sensors 2024, 24(19), 6237; https://doi.org/10.3390/s24196237 - 26 Sep 2024
Cited by 1 | Viewed by 1275
Abstract
The reliable operation of scroll compressors is crucial for the efficiency of rotating machinery and refrigeration systems. To address the need for efficient and accurate fault diagnosis in scroll compressor technology under varying operating states, diverse failure modes, and different operating conditions, a [...] Read more.
The reliable operation of scroll compressors is crucial for the efficiency of rotating machinery and refrigeration systems. To address the need for efficient and accurate fault diagnosis in scroll compressor technology under varying operating states, diverse failure modes, and different operating conditions, a multi-branch convolutional neural network fault diagnosis method (SSG-Net) has been developed. This method is based on the Swin Transformer, the Global Attention Mechanism (GAM), and the ResNet architecture. Initially, the one-dimensional time-series signal is converted into a two-dimensional image using the Short-Time Fourier Transform, thereby enriching the feature set for deep learning analysis. Subsequently, the method integrates the window attention mechanism of the Swin Transformer, the 2D convolution of GAM attention, and the shallow ResNet’s two-dimensional convolution feature extraction branch network. This integration further optimizes the feature extraction process, enhancing the accuracy of fault feature recognition and sensitivity to data variability. Consequently, by combining the global and local features extracted from these three branch networks, the model significantly improves feature representation capability and robustness. Finally, experimental results on scroll compressor datasets and the CWRU dataset demonstrate diagnostic accuracies of 97.44% and 99.78%, respectively. These results surpass existing comparative models and confirm the model’s superior recognition precision and rapid convergence capabilities in complex fault environments. Full article
(This article belongs to the Special Issue Fatigue-Sensing Technologies for Manufacturing Materials and Machinery Parts)
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