The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Optimal MLP neural network classifier for fault detection of three phase induction motor
Expert Systems with Applications: An International Journal
SVM practical industrial application for mechanical faults diagnostic
Expert Systems with Applications: An International Journal
A PC-based architecture for parameter analysis of vector-controlled induction motor drive
Computers and Electrical Engineering
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
| Hi-index | 12.06 |
In this paper, we propose and implement a decision-level fusion model by combining the information of multi-level wavelet decomposition for fault diagnosis of induction motor using transient stator current signal. Firstly, the start-up transient current signals are collected from different faulty motors. Then signal preprocessing is conducted containing smoothing and subtracting to reduce the influence of line frequency in transient current signals. Next, we employ discrete wavelet transform technique to decompose the preprocessed signals into different frequency ranges of products, and then features are extracted from decomposed detail components. Finally, two decision-level fusion strategies, Bayesian belief fusion and multi-agent fusion, are employed. That is, fault features are classified using several classifiers and generated decisions are fused using a specific fusion algorithm. The proposed approach is evaluated by an experiment of fault diagnosis for induction motors. Experiment results show that excellent diagnosis performance can be obtained.