Surface Quality Assurance Method for Lithium-Ion Battery Electrode Using Concentration Compensation and Partiality Decision Rules

This paper presents a novel method for lithium-ion battery electrode (LIBE) surface quality assurance. First, based on machine vision, an automatic optical inspection system is developed to check defects on LIBE. In addition, a background normalization algorithm is put forward to preprocess the large-scale LIBE with inhomogeneous thickness in uneven illumination. With the help of the auto-concentration compensation algorithm, flaws can be extracted precisely. Moreover, after characterizing the defects, features machine applied partiality parameter automatic adjustment method and partiality decision rules are exerted for defects accurate classification, which provides near-optimal performance and reduces the complexity of tuning parameters. The proposed method is computationally efficient and satisfies real-time online inspection requirement. Experimental results verify the effectiveness and performance of the proposed method according to the inspection speed and recognition rate.

[1]  Jianbin Xiong,et al.  Fault Diagnosis of Rotation Machinery Based on Support Vector Machine Optimized by Quantum Genetic Algorithm , 2018, IEEE Access.

[2]  Robert X. Gao,et al.  Deep learning and its applications to machine health monitoring , 2019, Mechanical Systems and Signal Processing.

[3]  Jian Feng,et al.  Window Feature-Based Two-Stage Defect Identification Using Magnetic Flux Leakage Measurements , 2018, IEEE Transactions on Instrumentation and Measurement.

[4]  De Xu,et al.  A Novel and Effective Surface Flaw Inspection Instrument for Large-Aperture Optical Elements , 2015, IEEE Transactions on Instrumentation and Measurement.

[5]  Xie Hongwei,et al.  Solder Joint Inspection Method for Chip Component Using Improved AdaBoost and Decision Tree , 2011, IEEE Transactions on Components, Packaging and Manufacturing Technology.

[6]  Ruqiang Yan,et al.  Bearing Degradation Evaluation Using Improved Cross Recurrence Quantification Analysis and Nonlinear Auto-Regressive Neural Network , 2019, IEEE Access.

[7]  Bernd Eichberger,et al.  Detecting Defects in Photovoltaic Cells and Panels and Evaluating the Impact on Output Performances , 2016, IEEE Transactions on Instrumentation and Measurement.

[8]  Jordi-Roger Riba Ruiz,et al.  A Genetic-Algorithm-Optimized Fractal Model to Predict the Constriction Resistance From Surface Roughness Measurements , 2017, IEEE Transactions on Instrumentation and Measurement.

[9]  Christina G. Siontorou,et al.  A Knowledge-Based Approach to Online Fault Diagnosis of FET Biosensors , 2010, IEEE Transactions on Instrumentation and Measurement.

[10]  James M. Caruthers,et al.  Lithium-Ion Battery Electrode Inspection Using Flash Thermography , 2014 .

[11]  Reinhold Ludwig,et al.  An Optical Surface Inspection and Automatic Classification Technique Using the Rotated Wavelet Transform , 2018, IEEE Transactions on Instrumentation and Measurement.

[12]  Valentin Roscher,et al.  Method and Measurement Setup for Battery State Determination Using Optical Effects in the Electrode Material , 2018, IEEE Transactions on Instrumentation and Measurement.

[13]  Giovanna Castellano,et al.  Using Adaptive Thresholding and Skewness Correction to Detect Gray Areas in Melanoma In Situ Images , 2012, IEEE Transactions on Instrumentation and Measurement.

[14]  Ruqiang Yan,et al.  Convolutional Discriminative Feature Learning for Induction Motor Fault Diagnosis , 2017, IEEE Transactions on Industrial Informatics.

[15]  Nabil Zerrouki,et al.  Vision-based fall detection system for improving safety of elderly people , 2017, IEEE Instrumentation & Measurement Magazine.

[16]  Christina G. Siontorou,et al.  Determining the Sources of Measurement Uncertainty in Environmental Cell-Based Biosensing , 2014, IEEE Transactions on Instrumentation and Measurement.

[17]  H. Xie,et al.  Adaptive online solder joint inspection algorithm based on incremental clustering , 2011 .

[18]  Ruqiang Yan,et al.  Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning , 2019, IEEE Transactions on Industrial Informatics.

[19]  Beena Sukumaran,et al.  Measurement of Porosity in Granular Particle Distributions Using Adaptive Thresholding , 2010, IEEE Transactions on Instrumentation and Measurement.

[20]  Nigel P. Brandon,et al.  Multi Length Scale Microstructural Investigations of a Commercially Available Li-Ion Battery Electrode , 2012 .

[21]  Bernd Eichberger,et al.  Detecting Defects in Photovoltaic Panels With the Help of Synchronized Thermography , 2018, IEEE Transactions on Instrumentation and Measurement.

[22]  Günther Schuh,et al.  Future Trends in Production Engineering , 2013 .

[23]  Gabriel Thomas,et al.  Detection of Ice on Power Cables Based on Image Texture Features , 2018, IEEE Transactions on Instrumentation and Measurement.

[24]  Fei Li,et al.  Detection of Suspicious Lesions by Adaptive Thresholding Based on Multiresolution Analysis in Mammograms , 2011, IEEE Transactions on Instrumentation and Measurement.

[25]  Xin Wang,et al.  Fabric Texture Analysis Using Computer Vision Techniques , 2011, IEEE Transactions on Instrumentation and Measurement.

[26]  Xinhua Chen,et al.  Surface Defect Detection Algorithm Based on Local Neighborhood Analysis , 2015, ITITS.

[27]  Alessandro Ferrero,et al.  Camera as the instrument: the rising trend of vision based measurement , 2014, IEEE Instrumentation & Measurement Magazine.

[28]  Liu Liu,et al.  Automated Visual Inspection System for Bogie Block Key Under Complex Freight Train Environment , 2016, IEEE Transactions on Instrumentation and Measurement.

[29]  Gunther Reinhart,et al.  Production of large-area lithium-ion cells – Preconditioning, cell stacking and quality assurance , 2012 .

[30]  Du-Ming Tsai,et al.  A fast regularity measure for surface defect detection , 2011, Machine Vision and Applications.

[31]  Marco Parvis,et al.  Exposure-Tolerant Imaging Solution for Cultural Heritage Monitoring , 2011, IEEE Transactions on Instrumentation and Measurement.

[32]  Anirban Mukherjee,et al.  Automatic Defect Detection on Hot-Rolled Flat Steel Products , 2013, IEEE Transactions on Instrumentation and Measurement.

[33]  Shang-Liang Chen,et al.  A Machine Vision Based Automatic Optical Inspection System for Measuring Drilling Quality of Printed Circuit Boards , 2017, IEEE Access.