Cleaning System for Automated Optical Inspection of Heads of Bearing Rollers

2013 â—½  
Vol 198 â—½  
pp. 289-294
Author(s):  
Andrzej Zbrowski
Keyword(s):  
Technological Process â—½  
Control Process â—½  
Optical Inspection â—½  
Effective Removal â—½  
Industrial Solution â—½  
Automated Optical Inspection â—½  
On Line â—½  
Automatic Removal â—½  
Total Elimination â—½  
Bearing Rollers

The paper presents problems connected with the preparation of technical objects, including bearing rollers, for the process of the in-process, on-line automatic optical inspection. The objective was to develop an industrial solution ensuring automatic removal of pollutants from the technological process, located on the controlled surface of the object examined. The developed method allows for effective removal of both liquid and solid pollutants from the surface of the roller originating from the technological process. The removal is conducted in such a way that the quality control of the tested object is possible on the basis of the digital image obtained automatically at the test stand. The control process is limited by the efficiency of the cleaning process. The critical parameter is the achievement of expected effectiveness. The basic assumption was to adjust the efficiency of the control process to the efficiency of the production line (3 items/s). The essence of the presented solution is cleaning of the heads of bearing rollers in an automated, non contact manner with total elimination of the handwork. Therefore, the method meets the requirements of the automated process of cleaning of bearing rollers' heads right before the process of automated optical inspection of an object.

Fuzzy control of coagulation reaction through streaming current monitoring

1997 â—½  
Vol 36 (4) â—½  
pp. 127-134 â—½  
Author(s):  
J. C. Liu â—½  
M. D. Wu
Keyword(s):  
Fuzzy Logic Controller â—½  
Treatment Plant â—½  
Control Process â—½  
Water Treatment Plant â—½  
Critical Parameters â—½  
Flow Mode â—½  
Raw Water â—½  
Streaming Current â—½  
On Line â—½  
Current Monitoring

A fuzzy logic controller (FLC) incorporating the streaming current detector (SDC) was utilized in the automatic control of the coagulation reaction. Kaolinite was used to prepare synthetic raw water, and ferric chloride was used as the coagulant. The control set point was decided at a streaming current (SC) of −0.05 and pH of 8.0 from jar tests, zeta potential and streaming current measurements. A bench-scale water treatment plant with rapid mix, flocculation, and sedimentation units, operated in a continuous-flow mode, was utilized to simulate the reaction. Two critical parameters affecting the coagulation reaction, i.e., pH and streaming current, were chosen as process outputs; while coagulant dose and base dose were chosen as control process inputs. They were on-line monitored and transduced through a FLC. With raw water of initial turbidity of 110 NTU, residual turbidity of lower than 10 NTU before filtration was obtained. Results show that this combination functions satisfactorily for coagulation control.

scholarly journals Novel Features and PRPD Image Denoising Method for Improved Single-Source Partial Discharges Classification in On-Line Hydro-Generators

Energies â—½  
10.3390/en14113267 â—½  
2021 â—½  
Vol 14 (11) â—½  
pp. 3267
Author(s):  
Ramon C. F. Araújo â—½  
Rodrigo M. S. de Oliveira â—½  
Fernando S. Brasil â—½  
Fabrício J. B. Barros
Keyword(s):  
Image Denoising â—½  
Recognition Performance â—½  
Partial Discharge â—½  
Classification Performance â—½  
Partial Discharges â—½  
Denoising Method â—½  
Internal Parameters â—½  
On Line â—½  
Automatic Removal â—½  
The Impact

In this paper, a novel image denoising algorithm and novel input features are proposed. The algorithm is applied to phase-resolved partial discharge (PRPD) diagrams with a single dominant partial discharge (PD) source, preparing them for automatic artificial-intelligence-based classification. It was designed to mitigate several sources of distortions often observed in PRPDs obtained from fully operational hydroelectric generators. The capabilities of the denoising algorithm are the automatic removal of sparse noise and the suppression of non-dominant discharges, including those due to crosstalk. The input features are functions of PD distributions along amplitude and phase, which are calculated in a novel way to mitigate random effects inherent to PD measurements. The impact of the proposed contributions was statistically evaluated and compared to classification performance obtained using formerly published approaches. Higher recognition rates and reduced variances were obtained using the proposed methods, statistically outperforming autonomous classification techniques seen in earlier works. The values of the algorithm’s internal parameters are also validated by comparing the recognition performance obtained with different parameter combinations. All typical PD sources described in hydro-generators PD standards are considered and can be automatically detected.

scholarly journals Ceramic Cracks Segmentation with Deep Learning

Applied Sciences â—½  
10.3390/app11136017 â—½  
2021 â—½  
Vol 11 (13) â—½  
pp. 6017
Author(s):  
Gerivan Santos Junior â—½  
Janderson Ferreira â—½  
Cristian Millán-Arias â—½  
Ramiro Daniel â—½  
Alberto Casado Junior â—½  
...  
Keyword(s):  
Deep Learning â—½  
Ceramic Plate â—½  
Ceramic Tiles â—½  
Entire Area â—½  
Manual Inspection â—½  
Optical Inspection â—½  
Proposed Model â—½  
Processing Step â—½  
Long Time â—½  
Automated Optical Inspection

Cracks are pathologies whose appearance in ceramic tiles can cause various damages due to the coating system losing water tightness and impermeability functions. Besides, the detachment of a ceramic plate, exposing the building structure, can still reach people who move around the building. Manual inspection is the most common method for addressing this problem. However, it depends on the knowledge and experience of those who perform the analysis and demands a long time and a high cost to map the entire area. This work focuses on automated optical inspection to find faults in ceramic tiles performing the segmentation of cracks in ceramic images using deep learning to segment these defects. We propose an architecture for segmenting cracks in facades with Deep Learning that includes an image pre-processing step. We also propose the Ceramic Crack Database, a set of images to segment defects in ceramic tiles. The proposed model can adequately identify the crack even when it is close to or within the grout.

Automated optical inspection system for surface mount device light emitting diodes

2016 â—½  
Vol 30 (2) â—½  
pp. 641-655 â—½  
Author(s):  
Chung-Feng Jeffrey Kuo â—½  
Tz-ying Fang â—½  
Chi-Lung Lee â—½  
Han-Cheng Wu
Keyword(s):  
Light Emitting Diodes â—½  
Inspection System â—½  
Light Emitting â—½  
Surface Mount â—½  
Optical Inspection â—½  
Automated Optical Inspection â—½  
Optical Inspection System

Design of Robust Approach for Failure Detection in Dynamic Control Systems

2013 â—½  
pp. 237-259
Author(s):  
Gomaa Zaki El-Far
Keyword(s):  
Nuclear Power â—½  
Dynamic Control â—½  
Control Process â—½  
Failure Detection â—½  
Global Optimum â—½  
Sampling Time â—½  
Nuclear Power Reactor â—½  
Control Actions â—½  
On Line â—½  
Simulation Results

This paper presents a robust instrument fault detection (IFD) scheme based on modified immune mechanism based evolutionary algorithm (MIMEA) that determines on line the optimal control actions, detects faults quickly in the control process, and reconfigures the controller structure. To ensure the capability of the proposed MIMEA, repeating cycles of crossover, mutation, and clonally selection are included through the sampling time. This increases the ability of the proposed algorithm to reach the global optimum performance and optimize the controller parameters through a few generations. A fault diagnosis logic system is created based on the proposed algorithm, nonlinear decision functions, and its derivatives with respect to time. Threshold limits are implied to improve the system dynamics and sensitivity of the IFD scheme to the faults. The proposed algorithm is able to reconfigure the control law safely in all the situations. The presented false alarm rates are also clearly indicated. To illustrate the performance of the proposed MIMEA, it is applied successfully to tune and optimize the controller parameters of the nonlinear nuclear power reactor such that a robust behavior is obtained. Simulation results show the effectiveness of the proposed IFD scheme based MIMEA in detecting and isolating the dynamic system faults.

Synthesizing images using parameterized models for automated optical inspection (AOI)

2015 â—½  
Vol 82 (5) â—½  
Author(s):  
Max-Gerd Retzlaff â—½  
Josua Stabenow â—½  
Jürgen Beyerer â—½  
Carsten Dachsbacher
Keyword(s):  
Physical Simulation â—½  
Rigid Bodies â—½  
Systematic Evaluation â—½  
Virtual Objects â—½  
Optical Inspection â—½  
Physically Based â—½  
Automated Optical Inspection â—½  
Real Objects â—½  
Physically Based Rendering â—½  
Test Objects

AbstractWhen designing or improving systems for automated optical inspection (AOI), systematic evaluation is an important but costly necessity to achieve and ensure high quality. Computer graphics methods can be used to quickly create large virtual sets of samples of test objects and to simulate image acquisition setups. We use procedural modeling techniques to generate virtual objects with varying appearance and properties, mimicking real objects and sample sets. Physical simulation of rigid bodies is deployed to simulate the placement of virtual objects, and using physically-based rendering techniques we create synthetic images. These are used as input to an AOI system instead of physically acquired images. This enables the development, optimization, and evaluation of the image processing and classification steps of an AOI system independently of a physical realization. We demonstrate this approach for shards of glass, as sorting glass is one challenging practical application for AOI.

Control of Small Unconventional UAV Based on an On-Line Adaptive ADRC System

2014 â—½  
Vol 494-495 â—½  
pp. 1206-1211 â—½  
Author(s):  
Tong Yue Gao â—½  
Dong Dong Wang â—½  
Fei Tao â—½  
Hai Lang Ge
Keyword(s):  
Control System â—½  
Attitude Control â—½  
Control Process â—½  
Time Varying â—½  
Control Methods â—½  
Research Focus â—½  
Flight Experiment â—½  
Random Disturbances â—½  
On Line â—½  
Adaptive Ability

Recently, the UAV has become the research focus at home and abroad. this paper puts forward a unconventional type: double ducted tilting Subminiature UAV system (SUAV) , and carries out the research of the control system for this SUAV. Since SUAV flight attitude control process has strong time-varying characteristics, and there are random disturbances, the conventional control methods with unchanged parameters are often unworkable. An on-line adaptive ADRC control system is designed in this paper. An on-line adaptive ADRC system implements a simultaneous on-line tuning of ADRC rules and output scale of ADRC control system. The flight experiment showed that the proposed adaptive ADRC system provides quicker response, smaller overshoot, higher precision, robustness and adaptive ability. It satisfies the needs of autonomous flight.

Computational Imaging in Machine Vision System for Automated Optical Inspection

2009 â—½  
Author(s):  
Nak-Hoon Ko â—½  
Yoon-Suk Lee â—½  
Sang-Chul Jung â—½  
Dae-Chan Kim â—½  
Tae-Il Choi â—½  
...  
Keyword(s):  
Machine Vision â—½  
Vision System â—½  
Computational Imaging â—½  
Machine Vision System â—½  
Optical Inspection â—½  
Automated Optical Inspection
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