Diagnosis of artificially created surface damage levels of planet gear teeth using ordinal ranking

Abstract

Effective diagnosis of damage levels is important for condition based preventive maintenance of gearboxes. One special characteristic of damage levels is the inherent ordinal information among different levels. Retaining the ordinal information is therefore important for diagnosing damage levels. Classification, a machine learning technique, has been widely adopted for automated diagnosis of gear faults. However, classification cannot keep the ordinal information because the damage levels are treated as nominal variables. This paper employs ordinal ranking, another machine learning technique, to preserve the ordinal information in automated diagnosis of damage levels. As to ordinal ranking, feature selection is important. However, most existing feature selection methods are for classification, which are not suitable for ordinal ranking. This paper designs a feature selection method for ordinal ranking based on correlation coefficients. A diagnosis approach based on ordinal ranking and the proposed feature selection method is then introduced. This method is tested on diagnosis of artificially created surface damage levels of planet gear teeth in a planetary gearbox. Experimental results show the effectiveness of the proposed diagnosis approach. The advantages of using ordinal ranking for diagnosing gear damage levels are also demonstrated.