Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference

Abstract

A bearing fault diagnosis method has been proposed based on multi-scale entropy (MSE) and adaptive neuro-fuzzy inference system (ANFIS), in order to tackle the nonlinearity existing in bearing vibration as well as the uncertainty inherent in the diagnostic information. MSE refers to the calculation of entropies (e.g. appropriate entropy, sample entropy) across a sequence of scales, which takes into account not only the dynamic nonlinearity but also the interaction and coupling effects between mechanical components, thus providing much more information regarding machinery operating condition in comparison with traditional single scale-based entropy. ANFIS can benefit from the decision-making under uncertainty enabled by fuzzy logic as well as from learning and adaptation that neural networks provide. In this study, MSE and ANFIS are employed for feature extraction and fault recognition, respectively. Experiments were conducted on electrical motor bearings with three different fault categories and several levels of fault severity. The experimental results indicate that the proposed approach cannot only reliably discriminate among different fault categories, but identify the level of fault severity. Thus, the proposed approach has possibility for bearing incipient fault diagnosis.