Support vector machine based data processing algorithm for wear degree classification of slurry pump systems

Classification is a useful tool in identifying fault patterns. Generally, a good classification implementation is closely related to the effectiveness of data used. The word “effectiveness” implies that the data should be clean and the features indicating fault patterns should be properly selected. Unfortunately, data cleaning is not often implemented in reported work of fault pattern classifications. In this paper, a data processing algorithm is developed to achieve the effectiveness, which includes data cleaning followed by feature selection. A data cleaning algorithm is developed based on support vector machine and random sub-sampling validation. Candidate outliers are selected based on fraction values provided by the proposed data cleaning algorithm and final outliers are determined based on their removal impacts on classification performance. The feature selection algorithm adopts the classical sequential backward feature selection. The performance of the data cleaning algorithm is tested using three benchmark datasets. The tests show good capability of the data cleaning algorithm in identifying outliers for all datasets. The proposed data processing algorithm is adopted in the classification of the wear degree of pump impellers in a slurry pump system. The results show good effectiveness of sequentially using data cleaning and feature selection in addressing fault pattern classification problems.