Uncertainty caused by incomplete information brings great challenges to fault diagnostic rules gaining method for aviation electronic equipment. In order to solve the problem, by defining the incompleteness from the two angles of narrow sense and broad sense respectively, the consistency first completer algorithm is designed to solve the inconsistency problem caused by incomplete information based on maximal confidence and attribute value expectation, which exists in indirect completing algorithms. The symptom attributes concept lattice and diagnostic decision-making attributes concept lattice are constructed, and the equivalent relationship on the symptom attributes concept is introduced. Based on this, the disjunctive normal form discernibility function of the maximum inconsistent symptom attributes concept set is computed, then the optimal reduced attribute set is obtained based on which diagnostic rules are gained. Taking the weapon launching system as an example, the approach is validated with a precision of 83.3%. It can be concluded that the approach is better than the existing representative approach in dealing with incomplete information, accuracy and intuitionism of the diagnosis knowledge display.