BEARING DEFECT INSPECTION BASED ON MACHINE VISIONGUIDED BY : DR. T.D JOHN

SUBMITTED BY: VARUN KUMARROLL NO:16S1 M.TECH*

1. INTRODUCTION

2. INSPECTION TECHNIQUE

3. MACHINE VISION

4. APPLICATIONS

5. ADVANTAGES

6. M/C VISION EXPERIMENT AND PARAMETERS

7.RESULTS

8.CONCLUSION

9.REFERENCES

CONTENTS*

INTRODUCTIONBearings Are Important Components That Connect Different Machine Parts To Reduce Friction.

The Quality Of Bearings Can Directly Influence The Performance Of Many Machines And Can Even Cause Serious Disaster.

Bearings Are Widely Used In Ac,cars And Many Other Rotating Machines.

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INSPECTIONSInspection Measures Are Classified Into 3 Steps:

1. Material Inspection

2. Assembling Inspection

3. Final Goods Inspection*

Material Inspection mainly focuses on the dimension and surface inspection of receiving material.

Assembling inspection is used to inspect the defects that are caused by assembling process including surface inspection.

The final goods inspection is mainly focused on the surface defects giving full inspection before packing.

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WHAT IS MACHINE VISION ?Machine vision has been defined by the Machine Vision Association of the Society of Manufacturing Engineers and the Automated Imaging Association as The use of devices for optical, noncontact sensing to automatically receive and interpret an image of a real scene in order to obtain information and/or control machines or process.

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BLOCK DIAGRAM OF MACHINE VISION*

APPLICATIONSInspecting of the surfaces of bathtubs for scratches.Verifying that welds are strong enough. Checking paper in the production process for flaws.Finding irregularities on flat glass.Reading license plates of cars. Recognising and identifying persons.

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ADVANTAGESThe Quality of the product is increased.

Machine vision can lead to significant cost reductions.

Time for inspection can be significantly reduced.

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TERMS USED Pixel: smallest element of an image that can be individually processed.Grey scale: brightness of a pixel.Thresholding: process of assigning white to each pixel in the image with grey scale above a particular value , while all pixels below this value become black . That value is called threshold(T).Image consisting of black & white is called binary image.Blob :connected region in binary image .Blob analysis: method of analyzing a image that has undergone binarization processing ,it identifies segmented object a/c to geometric parameters .

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HOW IS BEARING DEFECT INSPECTED?Lighting and Image acquisition system

Inspection algorithms

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LIGHTING AND IMAGE ACQUISITION SYSTEMThe cover of a bearing is composed of 3 parts: inner ring, seal and outer ring.During the final inspection, many appearance defects needed to be inspected like

1. Cracks, rusts on the inner and outer rings 2. Deformations, scratches, cracks, rusts on seal. *

The system consists of;

1.Area scan camera 2.Ring shaped white light illuminator 3.Lens 4.Light shield*

Lighting and image acquisition system

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Common Defects In Bearings In order to get enhanced deformation information,3 bearings are captured in a single image.

The left and right bearing are used to inspect deformations defects.

Centre bearing is used to inspect other defects other than deformations.

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INSPECTION ALGORITHMSThe purpose of the system is to decide whether a bearing has appearance defects.

The main procedure of inspection algorithms consists of 3 parts;

1. Center position inspection 2. Right position inspection 3. Left position inspection*

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CENTER IMAGE INSPECTION*IMAGE ACQUISITIONIMAGE PREPROCESSINGCENTER BEARING SEGMENTATIONTEXT DETECTION AND RECOGNITIONDIMENSION DETECTIONP2C TRANSFORMATIONDEFECT INSPECTIONRESULTS RETURN

CENTER BEARING SEGMENTATION

PURPOSE: TO SEGMENT CENTER BEARING FROM WHOLE IMAGE

Placing the bearing at the center.Threshold methods are used to segment bearings from background.Using blob analysis to extract the center bearing from binary image.The edge points of inner ring and outer rings are sampled.

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P2C TRANSFORMATIONTo facilitate the algorithms, the ring shaped image of bearing is transformed into a rectangle image by P2C transformation.

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DIMENSION DETECTIONAfter the rectangle image is obtained next is to segment different parts of bearing and detect dimension defect.The rectangle image is projected on the horizontal direction to get the distribution curve.Binary image based on thresholds, is calculated according to the positions of projection valleys.Dimension detection is mainly focused on the inspection of chamfer dimension, sizes of rings , and seal.

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R=Ri iRi={1,if |di-i|>3i 0

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Text detection and RecognitionSince the contrast between text regions and backgrounds is high, the threshold algorithm is adopted directly to segment the seal region.

T(j)=(4/5)*(1/W)F(i,j) j=1,2,3H

T(j)-threshold value.W & H are width & height of seal. F(i,j) grey scale value of original value.

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DEFECT INSPECTION ON CENTER BEARING

Separate the image into various regions called ROI.

STEP 1: Adaptive threshold surface construction

STEP 2: Image segmentation

STEP 3: Feature extraction and classification*

SIDE BEARING INSPECTION*

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Experiment:Algorithm programmed in C++.Samples collected by skilled human inspectors.The restriction is set to very high level to avoid defective bearings being recognized as good.Compare proposed machine vision inspection with experienced human inspection.

Implementation of inspection algorithm2 set of samples collected.Set 1: 821 good ,100 defective.Set 2: 4713 good , 300 defective

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EVALUATION CRITERIAPrecision, Recall and F-measure are used.Precision =TP/ (TP + FP ).Recall=TP/ (TP + FN ).TP: no: of good bearings.FP: good bearings recognized as faulty ones.FN: faulty bearings recognized as good one .F-measure is weighted harmonic mean of precision and recall.

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RESULTS*

RESULT*

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CONCLUSIONThis study develops a machine vision system for inspection of bearing surfaces. Experimental results show that the proposed system can detect different defects on bearing covers with high efficiency and accuracy. In conclusion, it is possible to apply the machine vision system to the automatic production line, replacing the human workers. However, there are still many other works need to be done. *

REFERENCESHao Shen , Shuxiao Li, Duoyu Gu, Hongxing Chang,Bearing defect inspection based on machine vision, Measurement 45 (2012) 719733A. Srivania , M. Anthony Xavior , Investigation of Surface Texture Using Image Processing Techniques, Procedia Engineering 97 ( 2014 ) 1943 1947Joe Derganc, Franjo Pernu, A Machine Vision System for Inspecting BearingsUnderstanding and applying Machine vision by Hello Zeuch,(4-8,38-40,45-47,87-89)

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THANK YOU*

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