| 1. |
- Li, Xin, et al.
(författare)
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Intelligent fault diagnosis of bevel gearboxes using semi-supervised probability support matrix machine and infrared imaging
- 2023
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Ingår i: Reliability Engineering & System Safety. - : Elsevier. - 0951-8320 .- 1879-0836. ; 230
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Tidskriftsartikel (refereegranskat)abstract
- Fault diagnosis is of great significance to ensure the reliability and safety of complex bevel gearbox systems. Most existing intelligent fault diagnosis approaches of bevel gearboxes are designed with vibration monitoring. However, the collected vibration data are vulnerable to noise pollution and machinery operating conditions. Besides, traditional fault diagnosis models highly rely on numerous labeled samples, and neglect the high cost of label annotation in real-world applications. Therefore, a novel fault diagnosis approach based on semi-supervised probability support matrix machine (SPSMM) and infrared imaging is proposed for bevel gearboxes in this paper, which has the following properties. Firstly, SPSMM classifies 2D matrix data directly without vectorization, thus fully utilizing the spatial information in infrared images. Secondly, a probability output strategy is designed for SPSMM to calculate the posterior class probability estimation of matrix inputs, and consequently enhance the diagnostic accuracy and robustness of the model. Thirdly, a semi-supervised learning (SSL) framework is proposed for SPSMM to carry out sample transfer from the unlabeled sample pool to the labeled sample pool, which can effectively alleviate the problem of insufficient labeled samples. The superiority of the proposed diagnosis approach is demonstrated with an infrared imaging dataset of a bevel gearbox.
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| 2. |
- Shao, Mingjiao, et al.
(författare)
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High-Performance Biodegradable Energy Storage Devices Enabled by Heterostructured MoO3-MoS2 Composites
- 2023
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Ingår i: Small. - : Wiley-VCH Verlagsgesellschaft. - 1613-6810 .- 1613-6829. ; 19:10
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Tidskriftsartikel (refereegranskat)abstract
- Biodegradable implantable devices are of growing interest in biosensors and bioelectronics. One of the key unresolved challenges is the availability of power supply. To enable biodegradable energy-storage devices, herein, 2D heterostructured MoO3–MoS2 nanosheet arrays are synthesized on water-soluble Mo foil, showing a high areal capacitance of 164.38 mF cm−2 (at 0.5 mA cm−2). Employing the MoO3–MoS2 composite as electrodes of a symmetric supercapacitor, an asymmetric Zn-ion hybrid supercapacitor, and an Mg primary battery are demonstrated. Benefiting from the advantages of MoO3–MoS2 heterostructure, the Zn-ion hybrid supercapacitors deliver a high areal capacitance (181.86 mF cm−2 at 0.5 mA cm−2) and energy density (30.56 µWh cm−2), and the Mg primary batteries provide a stable high output voltage (≈1.6 V) and a long working life in air/liquid environment. All of the used materials exhibit desirable biocompatibility, and these fabricated devices are also fully biodegradable. Demonstration experiments display their potential applications as biodegradable power sources for various electronic devices.
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| 5. |
- Zhang, Liangwei, et al.
(författare)
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An unsupervised end-to-end approach to fault detection in delta 3D printers using deep support vector data description
- 2024
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Ingår i: Journal of manufacturing systems. - : Elsevier. - 0278-6125 .- 1878-6642. ; 72, s. 214-228
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Tidskriftsartikel (refereegranskat)abstract
- Fault detection in 3D printers is crucial for safety and quality assurance, emphasizing proactive prediction over reactive rectification based on manufacturing factors. Presently, most detection techniques rely on shallow models with limited representational capabilities, necessitating manual feature extraction from the captured signals. This manual process is not only cumbersome and potentially costly but often requires intricate domain-specific knowledge. Additionally, these handcrafted features might not optimally distinguish between normal and faulty samples, potentially reducing prediction accuracy. In this study, we introduce an end-to-end approach using the Deep Support Vector Data Description model for fault detection in 3D printers. This design inherently facilitates automatic feature learning, where the features are synergistically optimized for fault detection. Our experiments leverage magnetic field signals for fault detection in 3D printers, using 1D convolutional layers to discern temporal signal patterns and wide kernels in the initial layer to mitigate high-frequency noise. Furthermore, our model can be easily adapted to integrate multi-channel signals for enhanced accuracy. Evaluations on real-world data from a delta 3D printer underscore the superiority of our method compared to existing alternatives.
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| 6. |
- Zhang, Liangwei, et al.
(författare)
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Wave-ConvNeXt : An Efficient and Precise Fault Diagnosis Method for IIoT Leveraging Tailored ConvNeXt and Wavelet Transform
- 2024
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Ingår i: IEEE Internet of Things Journal. - 2327-4662. ; , s. 1-1
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Tidskriftsartikel (refereegranskat)abstract
- The burgeoning field of the Industrial Internet of Things (IIoT) necessitates advanced fault diagnosis methods capable of navigating the dual challenges of high predictive accuracy and the constraints of edge computing environments. Our study introduces Wave-ConvNeXt, a novel fault diagnosis model that seamlessly integrates the state-of-the-art ConvNeXt architecture with Wavelet Transform. This innovative model stands out for its lightweight design yet delivers exceptional accuracy in fault diagnosis. In Wave-ConvNeXt, we re-engineer the ConvNeXt model for IIoT applications by adopting onedimensional convolution, tailored for processing high-frequency, non-periodic inputs. This adaptation is complemented by replacing the traditional “patchify” layer with a Wavelet transform layer, which simplifies input signals into sub-signals, thereby easing learning complexities and diminishing the dependence on elaborate deep architectures. Further enhancing this model, we incorporate a squeeze-and-excitation module, enriching its ability to prioritize channel-wise feature relevance, akin to self-attention mechanisms. This integration is rigorously validated through an ablation study. Wave-ConvNeXt epitomizes a holistic approach, enabling an end-to-end optimization of feature learning and fault classification. Our empirical analysis on two real-world IIoT datasets demonstrates Wave-ConvNeXt’s superiority over existing models. It not only elevates prediction accuracy but also significantly curtails computational complexity. Additionally, our exploration into the impact of various mother wavelets reveals the effectiveness of using wavelet basis functions with smaller support, bolstering diagnostic precision. The source code of Wave-ConvNeXt is available at https://github.com/leviszhang/waveConvNeXt.
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