Fault diagnosis of slurry pH data base on autoregressive integrated moving average and least squares support vector machines

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.