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In our days an interest to programming of the expert systems is very high and it is even out of the boards of the academic researches. The expert system really helps to solve the actual practical problems in the different fields. One of such problems is an estimation of electromagnetic vibration exciter’s (EVE) forced mechanical oscillations stability. The EVE is a non-linear converter of electrical energy to mechanical energy. It is enough a perturbation a transient process. Therefore it is necessary to research a stability of the mechanical oscillations. The practical methods of EVE’s forced periodical oscillations stability research can be received by the construction of the non-linear system’s stationary models in the victinity of the periodical oscillations. However, it is determined that this way has some disadvantages: there is very complicated connection between the electrical, magnetic and mechanical parameters of EVE, it’s very difficult to determine them.

Moreover, the indeterminate forms, such as “hysteresis” and “saturation” carry out the essential problems for an estimation of the EVE’s forced oscillations stability fields. Therefore it is important to carry out the expert rules for the solving of above showed problem. A specification for the EVE’s forced oscillations stability determination should consist of the data about the EVE’s electromechanical parameters: frequency of the mains supply- <50 Hz (a), 50 Hz (a- ); voltage of the mains supply - U<220 V (b), U>220 V (b-); usage duration- c; mechanical oscillations amplitude- A<10 mm (d), A>10 mm (b-), Etc.

In this case the finished estimation of the EVE’s forced oscillation stability can be received from the solving of the following inequalities: CC +CS <0 and CCCS>0, here CC,.CS= f(a, b, c…)

The rules of the expert estimation production are:

P1: If abde, then the EVE’s forced oscillations are stable;

P2: If a- b- d-, then the EVE’s forced oscillations have some fields of non- stabilit and etc.

Depending on set of the initial data the search vector chooses this or that working regime.


Converter Factors of electrical parameters of electric motors in their mass production have strict requirements. This is stipulated that that time, conducted for the electrical parameter measurement, is sharply limited by the velocity of transporter, and at the same time it is required the high accuracy of transformation because the flaw final product depends on the control data. But the last is connected with the determined financial losses.

Three-phase power comsumed by the motor, as well as phasic currents, belong to the number of converted electrical parameters of electric motors. Becides, it is necessary to estimate the values of voltage in the network in order to reveal a deflection of the value from the given one and to enter the necessary correction to the measured values of power and current. Rather disadvantageous conditions (frequent switches, unplugging the loads from power sources ), strict temperature and accuracy requirement provide the usage of the converters with extended functional possibilities, so-called "intellectual" converters.

In the given report there are considered intellectual converters of electrical parameters of the electric motors, sharply distinguishing from traditional in their functional possibilities.

Intellectual converter allows execution in the realtime mode following nontraditional measurement-computing procedures: adapting time in averaging of the results to change a supplying network; mutual dependency of amount of intra-periodical discrete steps on spectral saturation signals in supplying motors circuits; optimization of amount of counting for time of averaging on the base of knowledge for presence of supreme harmonica at a current moment of measurements; active self-diagnostics of conditions of converter and entering of the necessary correction into the factors in the realtime mode.


The use of integrated knowledge-based information technologies, such as hybrid expert system, is the perspective way to increase an efficiency of gas turbine engine diagnosis.

The mobility of an expert system is defined by the knowledge base mobility and its ability to renew from different information sources (databases, expert knowledge bases, etc.) as well inference procedures. Due to knowledge specification, concepts in the knowledge base can be divided into exact and non-exact, complete and uncompleted, static and dynamic, single-valued и multiple-valued, etc. Besides, the expert knowledge usually is imprecise and has a subjective character. Inaccuracy and ambiguity of knowledge cause expert system to deal with several alternative areas.

The fuzzy logic could be applied for technical control and diagnostic hybrid system processing at least in three basic ways:

  1. a fuzziness processing, i.e. the precondition has fuzzy variable, and inference engine has the mean of data extraction;

  2. a fuzzy relations matrix which defines a set of factors and set of preconditions. The matrix contains fuzzy relations, the measure of which is represented by material number from [0,1]. To define the reasons of a condition, the transformation of the matrix and factors is used, then, the received system of equations is solved by the min-max method.

  3. a fuzzy logic inference algorithm. The given approach is the most frequently used at construction fuzzy knowledge bases.

The use of hybrid expert system for gas turbine engine parameter diagnosis problem expands the capabilities of such sophisticated systems, increases its flexibility and mobility, provides more amounts of data to be processed within the same computing resources and increases validity and accuracy of calculated results.

Parametric diagnostic algorithm is based on the comparison between certain engine mathematical model and reference non-defective engine model that implies an inspection of state variables to be within acceptable thresholds. Falling outside the thresholds indicates a failure in the certain engine unit. In hybrid expert system, the reference gas turbine engine model is stored in knowledge base and is corrected as expert system gains an experience. The real pattern is formed in database connecting with the reference model through user queries. The gas turbine engine parameter diagnostic system development under hybrid expert system is performed considering all specialties of expert system environment and model adaptation.

The hybrid expert system consists of the following functional units:

  • database storing reference and factual data to be processed; comparison results; conceptual, infologic and physical gas turbine engine models;

  • knowledge base: static (all concepts are stored as expert knowledge (productions), formulas, facts, relationship, tables) and dynamic knowledge bases (as combined neural net models in the form of reference dynamic processes that considers partial or complete indeterminacy of diagnostic parameters);

  • logic inference engine;

  • adapting unit;

  • description unit;

  • scheduler;

  • solver.

The internal form of expert knowledge representation is an inference tree that is especially useful at beginning stages of problem solving, for example, for the reference gas turbine engine mathematical model adaptation.

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