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Data-driven state monitoring requiring a little priori knowledge plays a key role for timely fault detection and is therefore of great importance for the safe and economical operation of the thermal power plant. The main drawback for most of the existing data-driven methods is the complex procedure of data preprocessing and model training especially when unlabelled operating data is used. To overcome this issue, this paper proposes a new framework of data-driven state monitoring approach fordoi:10.1051/matecconf/201927201003 fatcat:7mkzdi3dgvbv7mtpoznhwocn34