A morphogram with the optimal selection of parameters used in morphological analysis for enhancing the ability in bearing fault diagnosis

Abstract

Morphological analysis is a signal processing method that extracts the local morphological features of a signal by intersecting it with a structuring element (SE). When a bearing suffers from a localized fault, an impulse-type cyclic signal is generated. The amplitude and the cyclic time interval of impacts could reflect the health status of the inspected bearing and the cause of defects, respectively. In this paper, an enhanced morphological analysis called ‘morphogram’ is presented for extracting the cyclic impacts caused by a certain bearing fault. Based on the theory of morphology, the morphogram is realized by simple mathematical operators, including Minkowski addition and subtraction. The morphogram is able to detect all possible fault intervals. The most likely fault-interval-based construction index (CI) is maximized to establish the optimal range of the flat SE for the extraction of bearing fault cyclic features so that the type and cause of bearing faults can be easily determined in a time domain. The morphogram has been validated by simulated bearing fault signals, real bearing faulty signals collected from a laboratorial rotary machine and an industrial bearing fault signal. The results show that the morphogram is able to detect all possible bearing fault intervals. Based on the most likely bearing fault interval shown on the morphogram, the CI is effective in determining the optimal parameters of the flat SE for the extraction of bearing fault cyclic features for bearing fault diagnosis.