The nature of statistical learning theory
The nature of statistical learning theory
Time-frequency representations-based detector of chaos in oscillatory circuits
Signal Processing - Signal processing in UWB communications
Expert Systems with Applications: An International Journal
Design Methodology of a Fault Aware Controller Using an Incipient Fault Diagonizer
HIS '09 Proceedings of the 2009 Ninth International Conference on Hybrid Intelligent Systems - Volume 03
Information Sciences: an International Journal
IEEE Transactions on Neural Networks
Multi-appliance recognition system with hybrid SVM/GMM classifier in ubiquitous smart home
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
Reliability assessment and failure analysis of lithium iron phosphate batteries
Information Sciences: an International Journal
Hi-index | 0.07 |
Motor fault diagnosis in dynamic condition is a typical multi-sensor data fusion problem. It involves the use of information collected from multiple sensors, such as vibration, sound, current, voltage, and temperature, to detect and identify motor faults. From the viewpoint of evidence theory, information obtained from each sensor can be considered as a piece of evidence, and as such, the multi-sensor based motor fault diagnosis can be viewed as the problem of evidence fusion. In this article we propose and investigate a hybrid method for fault signal classification based on sensor data fusion by using the Support Vector Machine (SVM) and Short Term Fourier Transform (STFT) techniques. We report a practical application of this hybrid model and evaluate its performance. Finally, we compare the performance of the proposed system against some other standard fault classification techniques.