The DETR model is often affected by noise information such as complex backgrounds in the application of defect detection tasks, resulting in detection of some targets is ignored. In this paper, AIA DETR model is proposed by adding AIA (attention in attention) module into transformer encoder part, which makes the model pay more attention to correct defect …
Learn MoreThis paper presents an automatic flaw inspection scheme for online real-time detection of the defects on the surface of lithium-ion battery electrode (LIBE) in actual industrial production. Firstly, based on the conventional methods of region extraction, ROI (region of LIBE) could be extracted from the captured LIBE original image. Secondly, in order to …
Learn MoreWhen manufacturing battery cells, various defects can occur that require detection so the product can be removed before shipping. Microscopic cracks can occur in the electrode materials or the separator, potentially leading to reduced performance and safety concerns. Inconsistent coating on electrodes can lead to short circuits or reduced …
Learn MoreDeveloped methods for battery early fault diagnosis concentrate on short-term data to analyze the deviation of external features without considering the long-term …
Learn MoreAccording to QYResearch, a global market research firm, the global market size of secondary batteries is growing at an average annual rate of 8.1%, but fires and casualties continue to occur due to the lack of quality and reliability of secondary batteries. Therefore, improving the quality of secondary batteries is a major factor in …
Learn MoreThe results show that the optimization algorithm can improve the accuracy and speed of the lithium battery and achieves a 92.7% detection accuracy, surpassing the original network by 2.1%. For the traditional algorithm to detect lithium battery defects, the missing rate is high and the speed is slow, an improved YOLOv7 algorithm was …
Learn MoreThe global market research firm QYResearch forecasts that the global market for lithium-ion battery lead taps will grow at an average annual rate of 8.1% from USD 75.6 billion in 2022 to 1.33 billion by 2029 [] addition, the growth of the EV market is expected to accelerate from 2023 due to increasing EV purchase subsidies under the …
Learn MoreTargeting the issue that the traditional target detection method has a high missing rate of minor target defects in the lithium battery electrode defect detection, this paper proposes an improved and optimized battery electrode defect detection model based on YOLOv8. Firstly, the lightweight GhostCony is used to replace the standard …
Learn MoreThe experimental results indicate that the improved YOLOv5s model can accurately and quickly detect three types of defects on the bottom surface of lithium batteries and can meet the real-time detection requirements. Defect detection of lithium batteries is a crucial step in lithium battery production. However, traditional detection …
Learn MoreIndustrial CT offers engineers a powerful tool to diagnose problems and discover hidden flaws in batteries. This webinar hosted by Battery Technology and Lumafield delves into applications in battery construction, manufacturing, and inspection to ease detection and inspection for many critical issues. These include internal short …
Learn MoreThis paper presents a novel fault diagnosis method for battery systems in electric vehicles based on big data statistical methods. According to machine learning algorithm and 3σ multi-level screening strategy (3σ-MSS), the abnormal changes of cell terminal voltages in a battery pack can be detected and calculated in the form of …
Learn MoreDOI: 10.1109/ICMSP58539.2023.10170926 Corpus ID: 259835564; An end-to-end Lithium Battery Defect Detection Method Based on Detection Transformer @article{Yang2023AnEL, title={An end-to-end Lithium Battery Defect Detection Method Based on Detection Transformer}, author={Kun Yang and Lixin Zheng}, journal={2023 …
Learn More1. Introduction. Electric vehicles (EVs) have been widely recognized as an integral part of efficient and green transportation. Battery systems are a key component of EVs that largely defines their performance and cost-effectiveness [1], [2], [3].With the eye-catching development of advanced lithium-ion batteries, they have been established as …
Learn MoreDue to the non-contact, high accuracy, and flexibility of implementation, inspection technology based on machine vision has been extensively studied [3], and it has been widely used in various inspection scenarios, e.g., the inspection of mechanical parts [4, 5], steel products [6], [7], [8], textiles [9, 10], agricultural products [11, 12 ...
Learn MoreHundreds of electric vehicle (EV) battery thermal runaway accidents resulting from untreated defects restrict further development of EV industry. Battery defect detection based on the abnormality of external parameters is a promising way to reduce this kind of thermal runaway accidents and protect EV consumers from fire danger. However, …
Learn MoreDOI: 10.1784/insi.2024.66.5.305 Corpus ID: 269679222; Resolving data imbalance in alkaline battery defect detection: a voting-based deep learning approach @article{Xu2024ResolvingDI, title={Resolving data imbalance in alkaline battery defect detection: a voting-based deep learning approach}, author={Zhenying Xu and Bangguo …
Learn MoreIn this paper, data analysis methods for solar cell defect detection are categorised into two forms: 1) IBTs, which depend on analysing the deviations of optical properties, thermal patterns, or other visual features in images, and 2) ETTs, which depend on comparing the deviations of the module''s measured electrical parameters from the …
Learn MoreThe multi-exposure-based structured light method is introduced to reconstruct the 3D shape of the lithium battery using the MiniImageNet datasets as the source domain to pretrain the Cross-Domain Few-Shot Learning (CD-FSL) model. Detecting the surface defects in a lithium battery with an aluminium/steel shell is a difficult task. The effect of reflectivity, …
Learn MoreIn order to realize the automatic detection of surface defects of lithium battery pole piece, a method for detection and identification of surface defects of lithium battery pole piece based on multi-feature fusion and PSO-SVM was proposed in this paper. Firstly, image subtraction and contrast adjustment were used to preprocess the defect …
Learn MoreThis research addresses the critical challenge of classifying surface defects in lithium electronic components, crucial for ensuring the reliability and safety of lithium batteries. …
Learn MoreWhen and why does a rechargeable battery lose capacity or go bad? ... A 3D model system, ... Schauerman, C.M. et al. Rechargeable lithium-ion cell state of charge and defect detection by in-situ ...
Learn MoreA new model based on the improved YOLOv5 algorithm, which improved the average detection rate for small targets on the MS COCO dataset by 2.4%, showing that it can effectively detect small target defects. Focus on the requirement for detecting laser welding defects of lithium battery pole, a new model based on the improved …
Learn MoreThe experimental results show that the proposed method in this paper can effectively detect surface multiple types defects of lithium battery pole piece, and the average recognition rate of defects reaches 98.3%, which is an effective and feasible automatic defect detection and identification method.
Learn MoreAccurate detection of early faults in lithium-ion (Li-ion) battery packs plays an important role in preventing safety accidents and reducing property damage. At …
Learn MoreHere, we develop a realistic deep-learning framework for electric vehicle (EV) LiB anomaly detection. It features a dynamical autoencoder tailored for dynamical …
Learn MoreAutomated inspection technology based on computer vision is now widely used in the manufacturing industry with high speed and accuracy. However, metal parts always appear in high gloss or shadow on the surface, resulting in the overexposure of the captured images. It is necessary to adjust the light direction and view to keep defects out …
Learn MoreConsidering the influence of soc. on battery characteristics, we propose a AIEM-SOC to dynamically extract the effective soc. interval for battery defect detection. (2) GDP-DLCSS is proposed for battery defect detection, the parameters of which are driven by data to avoid the subjectivity of manually defined thresholds. (3)
Learn MoreThe visual detection algorithm is studied to detect the defects such as pits, rust marks and broken skin on the surface of lithium battery, specifically to design the imaging experimental platform of lithiumattery, and studies its vision detection algorithm. In the production process of lithium battery, the quality inspection requirements of lithium …
Learn MoreThe battery system, as the core energy storage device of new energy vehicles, faces increasing safety issues and threats. An accurate and robust fault diagnosis technique is crucial to guarantee the safe, reliable, and robust operation of lithium-ion batteries. However, in battery systems, various faults are difficult to diagnose and …
Learn MoreThe management of product quality is a crucial process in factory manufacturing. However, this approach still has some limitations, e.g., depending on the expertise of the engineer in evaluating products …
Learn MoreAs an essential component of the new energy vehicle battery, current collectors affect the performance of battery and are crucial to the safety of passengers. The significant differences in shape and scale among defect types make it challenging for the model detection of current collector defects. In order to reduce application costs and …
Learn MoreThus, the goal of this study was to realize reliable automatic quality inspection of secondary battery lead taps. There are three primary types of defects in lead tabs, i.e., those in the material …
Learn MoreFor the traditional algorithm to detect lithium battery defects, the missing rate is high and the speed is slow, an improved YOLOv7 algorithm was proposed. Firstly, CBAM attention mechanism is added to feature extraction part, which can enhance network''s representation ability. Secondly, in the feature fusion part, ConvNeXt …
Learn Morereveal the power of deep learning for EV battery fault detection with large-scale publicly available EV charging datasets, nor do they dis- cover how practical factors should …
Learn MoreThe system is able to learn meaningful features on its own, such as the presence of foreign particles or a deformed collector layer to indicate the presence of defects. ... Badmos, O., Kopp, A., Bernthaler, T. et al. Image-based defect detection in lithium-ion battery electrode using convolutional neural networks. J Intell Manuf 31, …
Learn MoreThe comprehensive intelligent development of the manufacturing industry puts forward new requirements for the quality inspection of industrial products. This paper summarizes the current research status of machine learning methods in surface defect detection, a key part in the quality inspection of industrial products. First, according to …
Learn MoreGDP-DLCSS is proposed for battery defect detection, the parameters of which are driven by data to avoid the subjectivity of manually defined thresholds. (3) The whole method is driven by real-world EV data, which considers the actual working …
Learn MoreAdvanced Fault Diagnosis for Li-Ion Battery Systems: A Review of Fault Mechanisms, Fault Features, and Diagnosis Procedures. This study delves into the inner …
Learn MoreAs an essential component of the new energy vehicle battery, current collectors affect the performance of battery and are crucial to the safety of passengers. The significant differences in shape and scale among defect types make it challenging for the model detection of current collector defects. In order to reduce application costs and conduct …
Learn MoreThis paper presents a plastic cap defect detection model. Plastic caps play a crucial role in industrial production, but they are susceptible to various defects caused by factors such as raw materials and manufacturing processes. Traditional defect detection methods rely on complex feature engineering and classifiers, leading to limited …
Learn MoreThis paper presents a novel fault diagnosis method for battery systems in electric vehicles based on big data statistical methods. According to machine learning …
Learn MoreGiven the increasing use of lithium-ion batteries, which is driven in particular by electromobility, the characterization of cells in production and application plays a decisive role in quality assurance. The …
Learn MoreContact Us