Shape and texture features in the automated classification of adhesive and abrasive wear particles

Abstract

In this study the automated classification system, developed previously by the authors, was used to classify wear particles. Two kinds of wear particles, adhesive and abrasive, were classified. The wear particles were generated using a pin-on-disk tribometer. Various operating conditions of load, sliding time and abrasive grit size were applied to simulate adhesive and abrasive wear of different severity. SEM images of wear particles were acquired, forming a database for further analysis. The particle images were divided into eight groups or classes, each class representing different wear test conditions. All eight particle classes were first examined visually. Next, area, perimeter and elongation parameters were determined for each class and the parameters were statistically analysed. The automated classification system, based on particle surface texture, was then applied to all particle classes. The results of the automated particle classification were compared to those based on either the visual assessment of particle morphology or numerical parameter values. It was shown that the texture-based classification system was a more efficient and accurate way of distinguishing between various wear particles than classification based on size and shape of wear particles. It seems that the texture-based classification method developed has great potential to become a very useful tool in the machine condition monitoring industry.

Direct detection of wear conditions by classification of ferrograph images

“…1, the natural logical process to construct an automatic ferrography system is linear, including the following steps: image preprocessing →wear particle segmentation →feature extraction →wear particle recognition →wear condition detection. Various image processing techniques, such as image segmentation [8][9][10][11], feature extraction [12,13], and classification methods [14][15][16][17][18][19], have been applied to wear particle analysis.…”

Directional fractal signature methods for trabecular bone texture in hand radiographs: Data from the Osteoarthritis Initiative

“…42 Also, the method could be extended to work on 3D images such as MRI and used, for example, in the characterisation of brain tumors. 43 Possible applications in engineering could be found in determination of the condition status of a mechanical system and prediction of machine failure, [44][45][46] determination of a relationship between surface texture and coefficient of friction, 47 and multiscale characterisation of 3D engineering surfaces. 48 However, there is a limitation in use of our methods.…”

Automated classification of articular cartilage surfaces based on surface texture

“…Detailed information on the feature extraction columns separately and downsampling by two (32) method incorporated in the automated classification [47]. This produced both approximated (LL) and detail images (LH, HL, HH ) used in this study can be found in reference [25] and on the dimension reduction technique in reference [26]. The methods used in this work are briefly…”

“…Larger images are usually recommended tures such as clusters of chondrocytes surrounded by the amorphous matrix [55]. Although these sub-in pattern recognition to avoid classifying of insignificant features, as previous studies have shown surface cartilage images are different to the ESEM images of unworn top cartilage surfaces analysed [25,26].…”