Desenvolvimento de um sistema de análise de imagem para quantificação do tamanho e distribuição de partículas de desgaste

This paper describes the development of an image analysis system for wear particles found in industrial equipment lubricating oil. Hence, it was utilized an image acquisition system to capture image samples of the oil held in filter membranes. An analytical methodology was also developed to classify the particles quantitatively and qualitatively, relating them to the wear mode where they had been generated. The ISO 4406 standard was applied for quantitative classification and Artificial Neural Networks were used for qualitative classification analysis. The applied system consists of a digital camera, a monocular optical microscope, one oil filtering system and two software programs developed to perform the automated analysis of the acquired wear particles images. Mineral oils of gearbox industrial machines were used in the process of achieving the samples. The wear particles were analyzed by an optical microscopy system, to obtain the sample image related to the wear. The results showed that the analysis system that was developed classifies the individual particles through Artificial Neural Networks with 96% accuracy, in addition to analyzing the multiple particles contained in the samples and generating an evaluation report and wear evolution. The software programs developed for the analysis have a graphical interface easy to use. They can be used in the study and evaluation of the wear particles obtained from industrial oil samples in the companies or in the universities for educational purposes. The system can be applied to analyze the surface of metals acquired by metallographic processes or in the image analysis of microorganisms acquired from blood samples, opening a wide field of application within universities and researches.