Fault diagnosis of three-way catalytic converter using improved fuzzy C-means clustering
Article
Figures
Metrics
Preview PDF
Reference
Related
Cited by
Materials
Abstract:
The model precision of three-way catalytic converter is restricted by its complex physical and chemical reaction, which limits the accuracy of fault diagnosis based on its reaction model. To solve this problem, we propose a fault diagnosis method using improved fuzzy C-means (FCM) clustering. The method includes fault feature extraction and optimization using fractional Fourier transform(FRFT), dimensionality reduction of fractal feature using kernel entropy component analysis(KECA) and FCM fault feature clustering based on improved similarity measure. Firstly, we obtain the detailed features of different fault conditions from time domain to frequency domain using FRFT, then select the optimal FRFT order by particle swarm optimization (PSO) algorithm and these high-dimensional FRFT features with optimal order are transformed into fractal feature vectors through the fractal operator. Next, these fractal feature vectors dimensionality is reduced with KECA. At last, the reduced feature vectors are submitted to the improved FCM for fault clustering analysis. Numerical experiment results show that compared with the FCM method of Euclidean distance or cosine distance, the proposed method could obtain better fault identification result.
