scholarly journals Surface Defects Detection and Identification of Lithium Battery Pole Piece Based on Multi-feature Fusion and PSO-SVM

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Changlu Xu ◽  
Linsheng Li ◽  
Jiwei Li ◽  
Chuanbo Wen
Keyword(s):  
Lithium Battery ◽  
Surface Defects ◽  
Feature Fusion ◽  
Pole Piece ◽  
Detection And Identification

scholarly journals Multi-feature Fusion and Damage Identification of Large Generator Stator Insulation Based on Lamb Wave Detection and SVM Method

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3733
Author(s):  
Ruihua Li ◽  
Haojie Gu ◽  
Bo Hu ◽  
Zhifeng She
Keyword(s):  
Fractal Dimension ◽  
Lamb Wave ◽  
Damage Identification ◽  
Feature Fusion ◽  
Support Vector ◽  
Detection And Identification ◽  
Condition Evaluation ◽  
Power Spectral ◽  
Single Feature ◽  
Generator Stator

Due to the merits of Lamb wave to Structural Health Monitoring (SHM) of composite, the Lamb wave-based damage detection and identification technology show a potential solution for the insulation condition evaluation of large generator stator. This was performed in order to overcome the problem that it is difficult to effectively identify the stator insulation damage the using single feature of Lamb wave. In this paper, a damage identification method of stator insulation based on Lamb wave multi-feature fusion is presented. Firstly, the different damage features were extracted from time domain, frequency domain, and fractal dimension of lamb wave signals, respectively. The features of Lamb wave signals were extracted by Hilbert transform (HT), power spectral density (PSD), fast Fourier transform (FFT), and wavelet fractal dimension (WFD). Then, a machine learning method based on support vector machine (SVM) was used to fuse and reconstruct the multi-features of Lamb wave and furtherly identify damage type of stator insulation. Finally, the effect of typical stator insulation damage identification is verified by simulation and experiment.

scholarly journals Infield Apple Detection and Grading Based on Multi-Feature Fusion

Horticulturae ◽  
2021 ◽  
Vol 7 (9) ◽  
pp. 276
Author(s):  
Guangrui Hu ◽  
Enyu Zhang ◽  
Jianguo Zhou ◽  
Jian Zhao ◽  
Zening Gao ◽  
...  
Keyword(s):  
Walking Speed ◽  
Surface Defects ◽  
Feature Fusion ◽  
First Grade ◽  
Fruit Shape ◽  
Second Grade ◽  
Support Vector ◽  
Multiple Features ◽  
Detection Algorithms ◽  
Learning Framework

A field-based apple detection and grading device was developed and used to detect and grade apples in the field using a deep learning framework. Four features were selected for apple grading, namely, size, color, shape, and surface defects, and detection algorithms were designed to discriminate between the four features using machine vision and other methods. Then, the four apple features were fused, and a support vector machine (SVM) was used for infield apple grading into three grades: first-grade fruit, second-grade fruit, and other-grade fruit. The results showed that for a single index, the accuracy of detecting the apple size, the fruit shape, the color, and the surface defects, were 99.04%, 97.71%, 98%, and 95.85%. The grading accuracies for the first-grade fruit, second-grade fruit, other-grade fruit, and the average grading accuracy based on multiple features were 94.55%, 95.71%, 100%, and 95.49%, respectively. The field experiment showed that the average grading accuracy was 94.12% when the feeding interval of the apples was less than 1.5 s and the walking speed did not exceed 0.5 m/s, meeting the accuracy requirements of field-based apple grading.

scholarly journals A practical approach to tramway track condition monitoring: vertical track defects detection and identification using time-frequency processing technique

2018 ◽  
Vol 13 (s1) ◽  
pp. 135-146
Author(s):  
Péter Bocz ◽  
Ákos Vinkó ◽  
Zoltán Posgay
Keyword(s):  
Surface Defects ◽  
Detection Algorithm ◽  
Processing Technique ◽  
False Alarms ◽  
Distribution Analysis ◽  
Time Frequency ◽  
Detection And Identification ◽  
Track Condition ◽  
Automatic Detection Algorithm ◽  
Track Irregularities

Abstract This paper presents an automatic method for detecting vertical track irregularities on tramway operation using acceleration measurements on trams. For monitoring of tramway tracks, an unconventional measurement setup is developed, which records the data of 3-axes wireless accelerometers mounted on wheel discs. Accelerations are processed to obtain the vertical track irregularities to determine whether the track needs to be repaired. The automatic detection algorithm is based on time–frequency distribution analysis and determines the defect locations. Admissible limits (thresholds) are given for detecting moderate and severe defects using statistical analysis. The method was validated on frequented tram lines in Budapest and accurately detected severe defects with a hit rate of 100%, with no false alarms. The methodology is also sensitive to moderate and small rail surface defects at the low operational speed.

Optimization of Lithium Battery Pole Piece Thickness Control System Based on GA-BP Neural Network

2019 ◽  
Vol 14 (7) ◽  
pp. 978-986 ◽  
Author(s):  
Yun-Xin Xu ◽  
Li-Chao Niu ◽  
Huan Yang ◽  
Yan-Chun Xiao ◽  
Yan-Jun Xiao
Keyword(s):  
Neural Network ◽  
Control System ◽  
Bp Neural Network ◽  
Lithium Battery ◽  
Pole Piece ◽  
Thickness Control

Research on detection algorithm of lithium battery surface defects based on embedded machine vision

2021 ◽  
pp. 1-9
Author(s):  
Yonggang Chen ◽  
Yufeng Shu ◽  
Xiaomian Li ◽  
Changwei Xiong ◽  
Shenyi Cao ◽  
...  
Keyword(s):  
Lithium Battery ◽  
Surface Defects ◽  
Visual Detection ◽  
Rapid Development ◽  
Detection Algorithm ◽  
Large Error ◽  
Detection Algorithms ◽  
Manual Inspection ◽  
Low Efficiency ◽  
Vision Detection

In the production process of lithium battery, the quality inspection requirements of lithium battery are very high. At present, most of the work is done manually. Aiming at the problem of large manual inspection workload and large error, the robot visual inspection technology is applied to the production of lithium battery. In recent years, with the rapid development and progress of science and technology, the rapid development of visual detection hardware and algorithms, making it possible to screen defective products through visual detection algorithms. This paper takes lithium battery as the research object, and studies its vision detection algorithm. As a common commodity, the quality of lithium battery is the key for users to choose. With the increasing requirements of users for battery quality, how to produce high-quality battery is the key problem to be solved by manufacturers. However, at present, the defects of battery surface are mostly carried out manually. There are low efficiency and low detection rate in the process of manual detection. In this paper, the 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 lithium battery; use different lighting schemes to design different battery positioning and extraction algorithms; use Hough detection method to locate the battery surface, and design the battery defect algorithm for this, and compare the algorithm through experiments.

Research on the On-Line Monitoring and Control System of Lithium Battery Pole Piece Surface Density

2014 ◽  
Vol 889-890 ◽  
pp. 691-694
Author(s):  
Guo Dong Zhu ◽  
Xuan Sun ◽  
Chang Sheng Ai
Keyword(s):  
Control System ◽  
Surface Density ◽  
Lithium Battery ◽  
Capacitive Sensor ◽  
Pole Piece ◽  
Monitoring And Control ◽  
Monitoring And Control System ◽  
Voltage Signal ◽  
On Line ◽  
And Control

For the question of surface density controlled by manual measurement in production process and low quality and inefficiency, we designed the on-line monitoring and control system based on capacitive sensor. The principle is capacitive sensor can convert the surface density variation to the capacitance variation, after operational amplifier, rectifier circuit and filter circuit we can get the voltage signal related to surface density of lithium battery pole piece. The voltage signal through the A/D input module into the PLC for data processing, according to the results we can adjust the coat blade degree real-time. This will achieve the purpose of surface density uniformity. Using this system to replace traditional methods, the lithium battery pole piece surface density can be measured in intelligent, controlled in real-time and non-contact mode with high precision. And this system can improve the quality of lithium battery and the production efficiency, also with great reliability and feasibility.

scholarly journals Trend Review Related To Defects Detection from Fruits and Vegetables

2021 ◽  
Author(s):  
Anjali N ◽  
Rajkumar Kannan ◽  
Frederic Andres ◽  
Georgeghita Ghinea
Keyword(s):  
Defect Detection ◽  
Fruits And Vegetables ◽  
Surface Defects ◽  
Morphological Characteristics ◽  
Local Market ◽  
Indian Agriculture ◽  
Detection And Identification ◽  
Rgb Images ◽  
Vegetables And Fruits

Defect detection and identification from fruits and vegetables are particularly challenging for Indian agriculture. Defect Detection is a process to identify the defects or damages in vegetables and fruits, based on the shapes, colors and textures. The local market finds it difficult to cope with the defects and other infections in fruits and vegetables as quality evaluations and classification of vegetables and fruits have become tedious process. Recently, several approaches based on Image processing, Machine Learning and Artificial Intelligence methods have been proposed for the purpose of defect detection. On the basis of classifying the types of defects, related pathogens, and physical and morphological characteristics descriptors, we review the different approaches based on a corpus of 57 articles between 2016 and 2021. In the process of describing the defect analysis, steps from the target articles, algorithms, and methods including qualitative and quantitative evaluation are mainly summarized. The aim of this current review work is to present-day novel images and collects recent defective area calculation methods to detect surface defects of fruits and vegetables using RGB images and to classify whether the fruit is defected or fresh. A rigorous evaluation of many new algorithms provided for quality assurance by researcher’s probes of vegetables and fruits have been conducted in this work. This review work conveys that using the recent identification features will help to decrease the disadvantages in fruit storeroom owing to storage of the affected vegetables and fruits, ie. Preventing the spread of defects and other infections from the infected fruits and vegetables to the fresh ones.

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