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Poincaré Plot Features and Statistical Features From Current and Vibration Signals for Fault Severity Classification of Helical Gear Tooth Breaks


 Most of approaches of feature extraction for data driven rotating machinery fault diagnosis assume characteristics of periodicity and seasonality typically inherent to linear signals obtained from different sensors. Nevertheless, the behaviour of rotating machinery is not necessarily linear when a failure occurs. Thus, new techniques based on the theory of chaos and non-linear systems are needed to extract proper features of signals. This paper introduces the use of features extracted from the Poincar' Plot (PP), which are computed over vibration and current signals measured on a gearbox powered by an induction motor. A comparison between the performance of classic statistical features and PP features is developed by applying feature analysis based on ANOVA and cluster validity assessment to rank and select the subset of best features. KNN clustering algorithm is used to test the performance of the selected feature set for fault severity classification. The use of PP for the analysis of non-linear, non periodic signals is not new, however its application in mechanical systems is not widely extended. Our contribution aims at highlighting the use of the PP features, supported by data collected from a test bed under real conditions of speed and load, to proof the potential application of this approach. The results show that PP features extracted from current signal yields 96% of classification accuracy when using at least eleven features.