An improved control-limit-based principal component analysis method for condition monitoring of marine turbine generators

The safe operation of marine turbine generators is a crucial concern in industries and academics. It is always important to monitor the health status of marine turbine generators. The lubricant oil usually carries abundant information on the turbine operation conditions. Various oil parameters of the turbines have been used in the existing monitoring systems. However, many of them conflict with each other by contrary detection results. Hence, it should eliminate the redundant oil parameters for efficient condition monitoring. Although many research studies addressed the redundant feature reduction issue using principal component analysis (PCA), PCA is designed for features with a linear relationship, which is not the case in marine turbine generator monitoring. This paper proposes a new nonlinear analysis method, the improved control-limit based PCA, to extract distinct failure indicators from the oil parameters of marine turbine generators. The contribution of this method is that the Hotelling statistic and Q statistic are combined to calculate a fixed control limit for PCA. The ability of the improved PCA to dealing with nonlinearity has been significantly enhanced by the proposed method. Experimental validation demonstrates that the extracted failure indicator using the proposed method is more effective than existing monitoring indexes with respect to fault detection accuracy.