Study Of Feature Optimization And Ensemble Classification Methods For Wear Particles

Tribosystem condition monitoring and recognition has been paid more and more attention in academia and industry. Analysis of wear particles in lubricating oil has been recognized as one of the most effective methods in the study of tribosystem condition description and recognition. However, classification of types of wear particles was usually examined by experts in the field due to the diversity and complexity of wear particles, which limited its industrial applications. With the advancement of microscope, computer, sensor technology, information technology and modern mathematical theory, although wear particle analysis tends to head for an automated, intellectualized stage, there are still insufficient for practical uses in existing automatic wear-particle classification systems with respect to wear-particle image processing, feature optimization and pattern recognition.One challenging issue in wear particle analysis is to establish the effective characteristic parameter system to describe wear particles properly using a few numerical features. In this paper, feature relevance and feature redundancy of wear particle are defined according to their intrinsic relationship between wear-particle features. To solve the optimization and selection of wear particle parameters, thepaper proposed two different algorithms------Recorre and genetic algorithm (GA). Italso analyses the advantage and application area of each method respectively. It is believed that the developed methods can reduce dimensionality of input features significantly. Recorre algorithm relies on general characteristics of training data to select some features without involving any learning algorithm, while GA requires one predetermined wear-particle classifier in feature selection and uses its performance to evaluate and determine which features are selected. GA can perform better than Recorre but it trends to be computationally more expensive. Recorre algorithm has the advantage of high speed and ability to scale to large datasets but its performance is moderate. GA is applicable to the cases when the accuracy is preferentially considered while Recorre is more practical if the number of instances becomes very large. Ensemble learning is an active field in intelligent learning field, which combines multiple component learners that have been trained in the same task to improve the accuracy of classification systems. The paper applies ensemble learning to classify wear particles and presents a new ensemble method, i.e. GA-Bagging for automated wear particle analysis which combines the feature selection technique, GA, with ensemble learning technique, Bagging algorithm. The proposed method can improve the accuracy and generalization performance of automated wear-particle classification systems significantly and provides a new approach for automatization and intelligentization of wear particle analysis.