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Space and Earth observation programs demand stringent guarantees ensuring smooth and reliable operations of space vehicles and satellites. Due to unforeseen circumstances and naturally occurring faults, it is desired that a fault-diagnosis system be capable of detecting, isolating, identifying, or classifying faults in the system. Unfortunately, none of the existing fault-diagnosis methodologies alone can meet all the requirements of an ideal fault- diagnosis system due to the variety of fault types, their severity, and handling mechanisms. However, it is possible to overcome these shortcomings through the integration of different existing fault-diagnosis methodologies. In this paper, a novel learning-based, diagnostic-tree approach is proposed which complements and strengthens existing efficient fault detection mechanisms with an additional ability to classify different types of faults to effectively determine potential fault causes in a subsystem of a satellite. This extra capability serves as a semiautomatic diagnostic decision support aid to expert human operators at ground stations and enables them to determine fault causes and to take quick and efficient recovery/reconfiguration actions. The developed diagnosis/analysis procedure exploits a qualitative technique denoted as diagnostic tree (DX-tree) analysis as a diagnostic tool for fault cause analysis in the attitude control subsystem (ACS) of a satellite. DX-trees constructed by our proposed machine-learning-based automatic tree synthesis algorithm are demonstrated to be able to determine both known and unforeseen combinations of events leading to different fault scenarios generated through synthetic attitude control subsystem data of a satellite. Though the immediate application of our proposed approach would be at ground stations, the proposed technique has potential for being integrated with causal model-based diagnosis and recovery techniques for future autonomous space vehicle missions.