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This paper addresses the issues of data reduction, visualization, clustering and classification for fault diagnosis and prognosis of the Liquid Cooling System (LCS) in an aircraft. LCS is a cooling system that consists of a left and a right loop, where each loop is composed of a variety of components including a heat exchanger, source control units, a compressor, and a pump. The LCS data and the fault correlation analysis used in the paper are provided by Hamilton Sundstrand (HS) - A United Technologies Company (UTC). This data set includes a variety of sensor measurements for system parameters including temperatures and pressures of different components, along with liquid levels and valve positions of the pumps and controllers. A graphical user interface (GUI) is developed in Matlab that facilitates extensive plotting of the parameters versus each other, and/or time to observe the trends in the data.