A diagnostic rule acquisition method based on concept lattice theory is proposed, in order to solve the problem of lack of diagnostic knowledge in aviation equipment. Incomplete diagnostic context is defined to formulate fault samples into 3-value table. An increment algorithm is designed to construct approximate concept lattices according to the incomplete diagnostic context. Hasse diagrams of such concept lattices shows visually the dependency between diagnosis result and test parameters. General discernibility matrices and function are introduced from a rule acquisition perspective to reduce attributes of the incomplete diagnostic context. The test parameters set is reduced by Boolean reasoning of general discernibility function, then the optimized approximate diagnostic rule set is acquired from the reduced concept lattices. New test samples can be diagnosed with the optimized approximate diagnosticrules. The diagnosis method is used in some aviation radar system and has a diagnosis precision of 77.7%. It validates that the proposed method can acquire effective diagnostic rules from incomplete fault information.