摘要
A newly developed on-line visual ferrograph（OLVF） gives a new way for engine wear state monitoring. However, the reliability of on-line wear debris image processing is challenged in both monitoring ship engines and the Caterpillar bench test, which weren＇t reported in previous studies. Two problems were encountered in monitoring engines and processing images. First, small wear debris becomes hard to be identified from the image background after monitoring for a period of time. Second, the identification accuracy for wear debris is greatly reduced by background noise because of oil getting dark after nmning a period of time. Therefore, the methods adopted in image processing are examined. Two main reasons for the problems in wear debris identification are generalized as follows. Generally, the binary threshold was determined by global image pixels, and was easily affected by the non-objective zone in the image. The boundary of the objective zone in the binary image was misrecognized because of oil color becoming lighter during monitoring. Accordingly, improvements were made as follows. The objective zone in a global binary image was identified by scanning a column of pixels, and then a secondary binary process confined in the objective zone was carried out to identify small wear debris. Linear filtering with a specific template was used to depress noise in a binary image, and then a low-pass filtering was performed to eliminate the residual noise. Furthermore, the morphology parameters of single wear debris were extracted by separating each wear debris by a gray stack, and two indexes, WRWR （relative wear rate） and WRWS （relative wear severity）, were proposed for wear description. New indexes were provided for on-line monitoring of engines.
A newly developed on-line visual ferrograph（OLVF） gives a new way for engine wear state monitoring. However, the reliability of on-line wear debris image processing is challenged in both monitoring ship engines and the Caterpillar bench test, which weren＇t reported in previous studies. Two problems were encountered in monitoring engines and processing images. First, small wear debris becomes hard to be identified from the image background after monitoring for a period of time. Second, the identification accuracy for wear debris is greatly reduced by background noise because of oil getting dark after nmning a period of time. Therefore, the methods adopted in image processing are examined. Two main reasons for the problems in wear debris identification are generalized as follows. Generally, the binary threshold was determined by global image pixels, and was easily affected by the non-objective zone in the image. The boundary of the objective zone in the binary image was misrecognized because of oil color becoming lighter during monitoring. Accordingly, improvements were made as follows. The objective zone in a global binary image was identified by scanning a column of pixels, and then a secondary binary process confined in the objective zone was carried out to identify small wear debris. Linear filtering with a specific template was used to depress noise in a binary image, and then a low-pass filtering was performed to eliminate the residual noise. Furthermore, the morphology parameters of single wear debris were extracted by separating each wear debris by a gray stack, and two indexes, WRWR （relative wear rate） and WRWS （relative wear severity）, were proposed for wear description. New indexes were provided for on-line monitoring of engines.
基金
supported by National Basic Research Program of China (973 Program, Grant No. 2009CB724404)
National Hitech Research and Development Program of China (863 Program, Grant No. 2006AA04Z431)
National Natural Science Foundation of China (Grant No. 50905135)
