Classification of heart sounds using an artificial neural network
Pattern Recognition Letters
Mechanic signal analysis based on the Haar-type orthogonal matrix
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Application of mother wavelet functions for automatic gear and bearing fault diagnosis
Expert Systems with Applications: An International Journal
Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference
Expert Systems with Applications: An International Journal
ROCOM'10 Proceedings of the 10th WSEAS international conference on Robotics, control and manufacturing technology
A self-adaptive data analysis for fault diagnosis of an automotive air-conditioner blower
Expert Systems with Applications: An International Journal
Application of multiclass support vector machines for fault diagnosis of field air defense gun
Expert Systems with Applications: An International Journal
EEMD method and WNN for fault diagnosis of locomotive roller bearings
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
An investigation of a fault diagnostic technique for internal combustion engines using discrete wavelet transform (DWT) and neural network is presented in this paper. Generally, sound emission signal serves as a promising alternative to the condition monitoring and fault diagnosis in rotating machinery when the vibration signal is not available. Most of the conventional fault diagnosis techniques using sound emission and vibration signals are based on analyzing the signal amplitude in the time or frequency domain. Meanwhile, the continuous wavelet transform (CWT) technique was developed for obtaining both time-domain and frequency-domain information. Unfortunately, the CWT technique is often operated over a longer computing time. In the present study, a DWT technique which is combined with a feature selection of energy spectrum and fault classification using neural network for analyzing fault signal is proposed for improving the shortcomings without losing its original property. The features of the sound emission signal at different resolution levels are extracted by multi-resolution analysis and Parseval's theorem [Gaing, Z. L. (2004). Wavelet-based neural network for power disturbance recognition and classification. IEEE Transactions on Power Delivery 19, 1560-1568]. The algorithm is obtained from previous work by Daubechies [Daubechies, I. (1988). Orthonormal bases of compactly supported wavelets. Communication on Pure and Applied Mathematics 41, 909-996.], the''db4'', ''db8'' and ''db20'' wavelet functions are adopted to perform the proposed DWT technique. Then, these features are used for fault recognition using a neural network. The experimental results indicated that the proposed system using the sound emission signal is effective and can be used for fault diagnosis of various engine operating conditions.