Adaptive neuro-fuzzy inference systems for analysis of internal carotid arterial Doppler signals
Computers in Biology and Medicine
An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
A multidimensional hybrid intelligent method for gear fault diagnosis
Expert Systems with Applications: An International Journal
Optimal MLP neural network classifier for fault detection of three phase induction motor
Expert Systems with Applications: An International Journal
Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference
Expert Systems with Applications: An International Journal
The slow-changing alarm system of condition monitoring for rotating machinery
WSEAS TRANSACTIONS on SYSTEMS
Robust condition monitoring for early detection of broken rotor bars in induction motors
Expert Systems with Applications: An International Journal
EEMD method and WNN for fault diagnosis of locomotive roller bearings
Expert Systems with Applications: An International Journal
SVM practical industrial application for mechanical faults diagnostic
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Advances in Artificial Neural Systems
Performance evaluation of a copper omega type Coriolis mass flow sensor with an aid of ANFIS tool
Expert Systems with Applications: An International Journal
A rule-based intelligent method for fault diagnosis of rotating machinery
Knowledge-Based Systems
Computational Intelligence and Neuroscience
Hi-index | 12.07 |
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.