This paper presents a new approach to intelligent fault diagnosis based on statistics analysis, an improved distance evaluation technique and adaptive neuro-fuzzy inference system (ANFIS). The approach consists of three stages. First, different features, including time-domain statistical characteristics, frequency-domain statistical characteristics and empirical mode decomposition (EMD) energy entropies, are extracted to acquire more fault characteristic information. Second, an improved distance evaluation technique is proposed, and with it, the most superior features are selected from the original feature set. Finally, the most superior features are fed into ANFIS to identify different abnormal cases. The proposed approach is applied to fault diagnosis of rolling element bearings, and testing results show that the proposed approach can reliably recognise different fault categories and severities. Moreover, the effectiveness of the proposed feature selection method is also demonstrated by the testing results.

A new approach to intelligent fault diagnosis of rotating machinery

An adaptive SK technique and its application for fault detection of rolling element bearings

A novel signal compression method based on optimal ensemble empirical mode decomposition for bearing vibration signals

Roller bearing acoustic signature extraction by wavelet packet transform, applications in fault detection and size estimation

Faulty bearing signal recovery from large noise using a hybrid method based on spectral kurtosis and ensemble empirical mode decomposition

https://ltu.diva-portal.org/smash/get/diva2:985432/FULLTEXT01.pdf

Elin Johansson

Papers

Towards automatic detection of local bearing defects in rotating machines