A LNICA based Fault Detection Method in Industrial Processes

Fault detection is an important part of process control in order to achieve the safe operation. Data-driven methods including Independent Component Analysis (ICA) have been widely studied and applied. ICA performs reliable analysis by maximizing non-Gaussianity and obtaining statistically independent latent variables. To improve the adaptability of ICA on the industrial data, a Log-normal Independent Component Analysis (LNICA) method was proposed in this paper to maximize non-Gaussianity of the observed data. The experimental results on digital simulation data showed that our method could effectively detect the pulse disturbance. And the results on Tennessee Eastman (TE) process indicated that the LNICA had less false alarm rate, better fault detection performance than the traditional PCA and ICA methods. Also the LNICA had higher fault detection efficiency in the independent component optimization.