Research on recognition method of wear debris based on YOLO V5S network

Shi, Xinfa; Cui, Ce; He, Shiwen; Xie, Xue‐Jun; Sun, Ying; Qin, Chudong

doi:10.1108/ilt-08-2021-0334

Cited by 5 publications

(1 citation statement)

References 18 publications

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“…In engineering applications, it is usually necessary to install multiple sensors at the key cross-section of the system to collect multi-channel information and extract statistical features from the vibration signals collected from each channel, but the increase in the number of features will undoubtedly produce the problem of "dimensional catastrophe" [27,19,15,14,10]. crucial for developing machine intelligence fault diagnosis and decision-making techniques for industrial big data [23,2,20].…”

Section: Introductionmentioning

confidence: 99%

Dimensionality reduction of rotor fault dataset based on joint embedding of multi-class graphs

Zhang,

Wang,

Zhao

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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Traditional dimensionality reduction techniques usually rely on a single or a limited number of similar graphs for graph embedding, which limits their ability to extract more information about the internal structure of the data. To address this problem, this study proposes a rotor fault dataset dimensionality reduction algorithm based on multi-class graph joint embedding (MCGJE). The algorithm first overcomes the defect that the traditional feature space cannot take both local and global information into account by constructing local and global median feature line graphs; secondly, based on the graph embedding framework, the algorithm also constructs a hypergraph structure for inscribing complex multivariate relationships between high-dimensional data in the feature space, which in turn enables it to contain more fault information. Finally, we conducted two different rotor fault simulation experiments. The results show that the MCGJE-based algorithm has robust dimensionality reduction capability and can significantly improve the accuracy of fault identification.

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Section: Introductionmentioning

confidence: 99%

Dimensionality reduction of rotor fault dataset based on joint embedding of multi-class graphs

Zhang,

Wang,

Zhao

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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An improved YOLOv8-CGA-ASF-DBB method for multi-class wear debris recognition of online visual ferrograph image

Fan,

Wang,

Feng

et al. 2024

Meas. Sci. Technol.

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The analysis of wear based on on-line visual ferrograph provides crucial insights for the analysis of wear faults in mechanical equipment.However, online ferrograph analysis has been greatly limited by the low imaging quality and recognition accuracy of particle chains and high hubbles when analyzing lubricant oils in practical applications. To address this issue,this paper proposes an enhanced OLVF wear image detection model based on YOLOv8 and applies it to the multi-class intelligent recognition of ferrograph images .The cascade group attention module is introduced to enhance the diversity of features and improve computational efficiency. The attentional scale sequence fusion module is introduced to achieve precise and rapid recognition of small targets. This diverse branch block module is introduced efficiently to extracts features without compromising reasoning speed during training. For verification, a test of the bridge transmission box was conducted based on OLVF and 992 ferrograph images were collected. Experimental results reveal that the improved algorithm achieves an accuracy of 94.53% on the dataset of bridge transmission box ferrograph wear debris images collected through OLVF. This represents a 5.2% increase in recognition accuracy compared to the original algorithm while maintaining a processing time of only 0.69 ms per image. These findings provide compelling evidence for significant enhancements in both recognition accuracy and processing speed achieved by the improved algorithm, thereby establishing its considerable value for engineering applications.

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Instance segmentation of on-line wear debris using deep convolutional neural network with transfer learning

Li,

Chen

2024

ILT

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Purpose This study aims to apply deep convolutional neural network Mask-R-CNN algorithm based on transfer learning to realize the segmentation of online wear fragments. Design/methodology/approach Wear debris analysis is considered to be one of the most effective methods to maintain the condition of mechanical equipment. In this paper, the friction and wear testing machine was used to design pin-disk rotation, pin-disk reciprocation and four-ball test to produce cutting, sliding, laminar and fatigue debris. A semi-online sampling system was designed to collect ferrographic images containing various fragments. The images were rotated and flipped to augment the data and enhance the generalization ability of the model. The data set required for data analysis is established. Using COCO pre-trained Mask R-CNN data set as a benchmark, the region proposal network (RPN) is trained with labeled wear debris images to enhance the ability of RPN to recognize background and wear debris. Two transfer learning scenarios are tested in the network head of the Mask R-CNN. Findings The results show that the deep convolutional neural network is suitable for the automatic classification and detection of wear fragments. Through transfer learning and proper training configuration, the ferrographic image recognition based on Mask R-CNN achieves high accuracy. Originality/value The results show that the deep convolutional neural network is suitable for the automatic classification and detection of wear fragments. Through transfer learning and proper training configuration, the ferrographic image recognition based on Mask R-CNN achieves high accuracy. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0182/

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Research on recognition method of wear debris based on YOLO V5S network

Cited by 5 publications

References 18 publications

Dimensionality reduction of rotor fault dataset based on joint embedding of multi-class graphs

Dimensionality reduction of rotor fault dataset based on joint embedding of multi-class graphs

An improved YOLOv8-CGA-ASF-DBB method for multi-class wear debris recognition of online visual ferrograph image

Instance segmentation of on-line wear debris using deep convolutional neural network with transfer learning

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