PCA and PLS monitoring approaches for fault detection of wastewater treatment process

Abstract

In order to produce the goods with high quality, the industrial system is becoming more complex than before. Meanwhile, the plant is suffering from high potential risks and the faults within it is difficult to be detected. Researchers have made efforts to diagnose the possible faults occurred in the system to further prevent the process from being broken down. Model-based method is proposed at the beginning and it receives effective monitoring results indeed. However, the mathematic model of the system should be known as prior, and it is challenging to achieve the goal. On the other hand, the Multivariate Statistical Process Monitoring (MSPM) does not require that people have clearly understood the process model before carrying out the diagnosis method. Two approaches based on Principal Components Analysis (PCA) and Partial Least Squares (PLS) separately are included in the framework of MSPM. This paper firstly introduces PCA and PLS techniques, including the algorithms, the test statistic, and differences between them. Then the thesis proposes Benchmark Simulation Model No.1 (BSM1), which is used to testify the efficiency of the proposed methods. Finally, the simulation is carried out and the result is reported.