Principles of Neurocomputing for Science and Engineering
Principles of Neurocomputing for Science and Engineering
Expert Systems with Applications: An International Journal
SVM practical industrial application for mechanical faults diagnostic
Expert Systems with Applications: An International Journal
Maximum power point tracking (MPPT) system of small wind power generator using RBFNN approach
Expert Systems with Applications: An International Journal
Induction motors bearing fault detection using pattern recognition techniques
Expert Systems with Applications: An International Journal
WISM'11 Proceedings of the 2011 international conference on Web information systems and mining - Volume Part I
Intelligent fault diagnosis of rotating machinery using infrared thermal image
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Fault diagnosis method of machinery based on fisher's linear discriminant and possibility theory
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
A rule-based intelligent method for fault diagnosis of rotating machinery
Knowledge-Based Systems
Engineering Applications of Artificial Intelligence
Hi-index | 12.06 |
A new method for intelligent fault diagnosis of rotating machinery based on wavelet packet transform (WPT), empirical mode decomposition (EMD), dimensionless parameters, a distance evaluation technique and radial basis function (RBF) network is proposed in this paper. In this method, WPT and EMD are, respectively, used to preprocess vibration signals to mine fault characteristic information more accurately. Then, dimensionless parameters in time domain are extracted from each of the original vibration signals and preprocessed signals to form a combined feature set. Moreover, the distance evaluation technique is utilised to calculate evaluation factors of the combined feature set. Finally, according to the evaluation factors, the corresponding sensitive features are selected and input into the RBF network to automatically identify different machine operation conditions. An experiment of rolling element bearings is carried out to test the performance of the proposed method. The experimental result demonstrates that the method combining WPT, EMD, the distance evaluation technique and the RBF network may accurately extract fault information and select sensitive features, and therefore it may correctly diagnose the different fault categories occurring in the bearings. Furthermore, this method is applied to slight rub fault diagnosis of a heavy oil catalytic cracking unit, the actual result shows the method may be applied to fault diagnosis of rotating machinery effectively.