Name
Title | ||
|---|---|---|
Fault diagnosis of slurry pH data base on autoregressive integrated moving average and least squares support vector machines |
Abstract | ||
|---|---|---|
A hybrid model that exploits the unique strength of the autoregressive integrated moving average (ARIMA) model and the least squares support vector machine (LSSVM) model was proposed for slurry pH value fault diagonosis in wet flue gas desulfurization (WFGD) system. The hybrid model was validated and evaluated by operating data and compared with individual ARIMA and LSSVM models. The results show that the hybrid prediction model can capture both linear and nonlinear patterns and has a better prediction performance than any single model. On this base, a sensor fault diagnosis system for pH value was designed by using the hybrid model. Firstly, the sensor fault location is determined on the reconstruction residuals, and then data reconstruction is implemented by the hybrid model instead of fault data. The simulation results from a 600 MW unit case study show that the model has high modeling precision and strong generalization. The fault diagnosis based on the hybrid model can diagnose the sensor's fault and obtain credible reconstruction data. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICNC.2013.6817959 | ICNC |
Keywords | Field | DocType |
autoregressive integrated moving average (arima),least squares support vector machine (lssvm),autoregressive integrated moving average model,sensor fault diagnosis system,wet flue gas desulfurization system,hybrid prediction model,slurries,power 600 mw,autoregressive moving average processes,flue gas desulphurisation,environmental science computing,lssvm model,sensor fault location,least squares approximations,least squares support vector machine model,fault diagnosis,data reconstruction,slurry ph value fault diagonosis,arima model,wfgd system,reconstruction residuals,ph value,time series,support vector machines,mathematical model,data models,time series analysis,predictive models | Least squares,Nonlinear system,Data reconstruction,Least squares support vector machine,Computer science,Support vector machine,Autoregressive integrated moving average,Artificial intelligence,Slurry,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zongliang Qiao | 1 | 0 | 0.68 |
| Jianxin Zhou | 2 | 0 | 0.34 |
| Fengqi Si | 3 | 3 | 3.45 |
| Zhigao Xu | 4 | 2 | 1.73 |
| lei zhang | 5 | 403 | 143.70 |