is a concept of KB; ~~ is a set of conceptual model input and output parameters.
~~

**Fig.1 Heterogeneous KB structure in hybrid expert system environment**
In order to use several KB and DB in expert system, these bases must be united in unified information space by input programming interpreting language which allows to effectively adapt and expand the conceptual model of gas turbine engine.

The solver of expert system performs decision of the certain problem by the following request:

MM(S1) MM(S2){t}; ?S1,S2

MM(S1) is a reference mathematical model of gas turbine engine under test;

MM(S2) is a mathematical model of defective gas turbine engine;

S1, S2 are parameters to be under diagnosis; {t} is a logic operator.

EKB is represented by exact, fuzzy and combined production (“IF X, THEN Y”):

X is a precondition represented by D1 D2 ... Dm, where Di is a disjunction represented by E11&E12&...E1n; Y is a inference represented by G1&G2&...Gk. A fuzziness in the EKB can be represented as: (X Y(Z)), where {X},{Y},{Z} are fuzzy sets defined in universal sets {U},{V},{W}. The X,Y,Z sets partially or completely define indefinite parameters of gas turbine engine fuzzy diagnostic model.

The gas turbine engine diagnostics in expert system EKB can be represented as the following: Task(X) Subtask 1(X1)#...#Subtask N(Xn), where X is a set of task input and output parameters.; # is a symbol of pass from one EKB to another.

So, every task (target) can be split up into several subtasks (subtargets). In this case, the selection of appropriate method to solve a basic task depends on specialties of each subtask solving. The parameter controls the objective task solving is a specialty of its solving process. Therefore, the production of EKB task is transformed in subtask’s productions: Task(X) = Specialty Subtask1(X1) = Specialty1#...#Subtask N(Xn)=Specialty n.

The proposed approach to heterogeneous knowledge base development for gas turbine engine diagnosis and control based on static and dynamic hybrid expert systems allows the following:

- to facilitate the adaptation of the object under diagnostic;

- to apply different knowledge sets (rules and fuzzy rules in database, knowledge base, expert knowledge base) including different inference algorithm for efficient diagnostic problem solving;

- to create a powerful hybrid information system with a simultaneous graphic interpretation of object under test;

- to include software units for sensor imitation.