This paper presents a PCA (principal component analysis)-based diagnostic approach, combining the principal component scores with the principal component loadings, to determine the fault location of sensors in a pilot-scale SBR (sequencing batch reactor activated sludge process) wastewater treatment process. The PCA diagnostic model is firstly built with the historical normal data, and the determination of fault location of sensors in wastewater treatment process is further achieved through the combination of the scores with the loadings of principal components. The study results reveal that PCA model can be used to detect faults; the loadings of principal components can well represent the contributions of variables to the principal components; and the scores of principal components give a clear indication of the faulty samples. The feasibility and effectiveness of the application of the combination of score plots with loading plots for sensor fault diagnosis in the wastewater treatment process are well demonstrated in the study.
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