Wavelet Analysis For Fault Diagnosis Research Articles

Feature extraction using Discrete Wavelet Transform for fault classification of planetary gearbox – A comparative study

Monitoring the condition of unreachable gears of epicyclic gearbox in real-time increases the asset reliability by anticipating the failures through preventive maintenance. Machine vibrations exhibit fruitful information about the failure at different operating conditions when measured at a convenient location through signal processing techniques. This study demonstrates the potency of discrete wavelet analysis for fault diagnosis of the planetary gearbox using Artificial Neural Network and Support Vector Machine. The mean squared energy of the detail coefficients at eight decomposed levels for different wavelet families is considered a feature. The J48 algorithm selects the prominent features which form an input to the classifiers. Afterward, their relative ability in classifying faults is compared in detail. Further, the classification performance of commonly used machine learning algorithms in the literature is also compared. It is found that the mean square energy of detailed coefficients of Discrete Wavelet Transform exhibit excellent fault diagnosing characteristics with various classifiers and is highly recommended.

Bearing fault diagnosis based on wavelet packet energy spectrum and SVM

Aiming at the limitation of wavelet analysis in fault diagnosis, combining wavelet packet energy spectrum and support vector machine algorithm, a fault diagnosis method based on wavelet packet energy spectrum and support vector machine algorithm (SVM) is proposed. This method first performs wavelet packet transformation on the test data, and the vibration signal is decomposed into independent frequency bands. The signal energy changes in different frequency bands reflect the change of the operating state, and the wavelet packet energy spectrum of each frequency band is extracted as a feature vector. Finally, the SVM algorithm is used to test the bearing faults. The test results show that after processing and analysis of a large number of measured bearing data, the fault of the bearing can be diagnosed relatively accurately.

Application of Wavelet Analysis in Fault Diagnosis for Asynchronous Motor by Simulation

In this paper, the wavelet analysis is used in the fault diagnosis of asynchronous motor, emulating fault signal by the simulation software. Then the faults are diagnosed according to analysis signal by the wavelet analysis. Wavelet analysis is one kind of late-model effective fault diagnosis method, which can express local characteristics of signal in both time domain and frequency. The model is built in MATLAB/Simulink, emulating the signal of asynchronous motor.

Fault Detection and Classification in Power Electronic Circuits Using Wavelet Transform and Neural Network

Problem statement: The identification of faults in any analog circuit is highly required to ensure the reliability of the circuit. Early detection of faults in a circuit can greatly assist in maintenance of the system by avoiding possibly harmful damage borne out of the fault. Approach: A novel method for establishing a fault dictionary using Wavelet transform is presented. The Circuit Under Test (CUT) is three phase single level inverter. The transform coefficients for the fault free circuit as well as for the simulated faults of CUT are found. The Wavelet transform is applied to the output of CUT and Standard Deviation (SD) of the transform coefficients are extracted. Using the transform coefficients, fault dictionary has been formed. In order to identify the type of fault, a neural network classifier has been utilized. Results: The compatibility of wavelet analysis with the various classification techniques for fault diagnosis has been illustrated in this study. The results of the study demonstrate the suitability and viability of wavelet analysis in fault diagnosis of power electronic circuits. Conclusion: The proposed approach is found to be more reliable in accurate identification and isolation of faults using fault dictionary. Moreover, the neural classifier improves the efficiency of the system as neural networks do not require prior knowledge as they are capable of learning and evolving through a number of learning algorithms.