Diagnosis of pitting damage levels of planet gears based on ordinal ranking

Information on damage levels is useful for condition based preventive maintenance decision making. To diagnose the damage level of machinery, classification methods have been widely employed. However, classification methods couldn´t utilize the ordinal information contained in damage levels. Ordinal ranking, a recently studied supervised learning method, utilizes the ordinal information in the data, which makes it useful in diagnosing damage levels. This paper applied a reported ordinal ranking algorithm to diagnose the pitting damage levels in a planet gear for the first time. Experiment results show that ordinal ranking can generate a good ranking model for damage level diagnosis. Comparisons with a classical classification method demonstrate the advantage of ordinal ranking.