Adaptive signal processing: theory and applications
Adaptive signal processing: theory and applications
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Signal processing algorithms in MATLAB
Signal processing algorithms in MATLAB
Adaptive Filtering: Algorithms and Practical Implementation
Adaptive Filtering: Algorithms and Practical Implementation
Realization of adaptive NEXT canceller for ADSL on DSP kit
DNCOCO'06 Proceedings of the 5th WSEAS international conference on Data networks, communications and computers
Bearing fault diagnosis based on neural network classification and wavelet transform
WAMUS'06 Proceedings of the 6th WSEAS international conference on Wavelet analysis & multirate systems
Sequential Fuzzy Diagnosis for Condition Monitoring of Rolling Bearing Based on Neural Network
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
An approach for generation of perception frames based fuzzy neural network from data
WSEAS TRANSACTIONS on SYSTEMS
An approach to fault diagnosis of rolling bearings
WSEAS Transactions on Systems and Control
WSEAS TRANSACTIONS on SYSTEMS
A neo-fuzzy approach for bottom parameters estimation in oil wells
WSEAS Transactions on Systems and Control
ISPRA'10 Proceedings of the 9th WSEAS international conference on Signal processing, robotics and automation
Algorithms for estimation in distributed parameter systems based on sensor networks and ANFIS
WSEAS TRANSACTIONS on SYSTEMS
Hi-index | 0.00 |
This paper presents a method of fault diagnosis for a rolling bearing used in a reciprocating machine by the adaptive filtering technique and a fuzzy neural network. The adaptive filtering is used for noise cancelling and feature extraction from vibration signal measured for the diagnosis. A fuzzy neural network is used to automatically distinguish the fault types of a bearing by time domain features. Using the signals processed by adaptive filtering, the neural network can quickly converge when learning, and can quickly distinguish fault types when diagnosing. The spectrum analysis of an enveloped time signal is also used for the fault diagnosis. Practical examples of diagnosis for a rice husking machine are shown in order to verify the efficiency of the method. All diagnosis results of the spectrum analysis and the fuzzy neural network show that the method proposed in this paper is very effective even for cancelling highly correlated noise, and for automatically discriminating the fault types with a high accuracy.