Method and system for predicting turbomachinery failure events employing genetic algorithm

A method for predicting or detecting an event in turbomachinery comprising:

• obtaining operational data from at least one machine using at least one sensor, said at least one machine comprising a turbomachine, said turbomachine comprising at least one of, a compressor, an engine, a generator and a turbine, said operational data comprising a plurality of performance metrics associated with the operation of said at least one machine, said performance metrics being associated with a plurality of time periods;
• obtaining peer operational data from at least one peer machine, said at least one peer machine comprising a turbomachine, said turbomachine comprising at least one of, a compressor, an engine, a generator and a turbine, said peer operational data comprising a plurality of performance metrics associated with the operation of said at least one peer machine;
• determining if said at least one peer machine has experienced said event or has not experienced said event;
• employing a genetic algorithm to analyze said operational data and said peer operational data, said genetic algorithm comprising;
• generating a plurality of clauses, said clauses used to characterize said operational data, each of said clauses comprising a plurality of alleles, said alleles comprising a count of time periods, at least one performance metric, a comparison operator, a threshold value, and a positive fraction;
• evaluating said plurality of clauses as being either “true” or “false”, a “true”
• evaluation being obtained if for any given number of time periods equal to said count of time periods, at least a fraction of said time periods equal to said positive fraction contain a performance metric which satisfies said comparison operator with respect to said threshold value, and a “false” evaluation obtained otherwise;
• applying a fitness function to identify a fitness value for each of said clauses, said fitness value determined by the degree to which each of said clauses evaluates as “true” when applied to said at least one peer machine for which it is known said event has occurred, and which each of said clauses evaluates as “false” when applied to said at least one peer machine for which it is known said event has not occurred;
• selecting a plurality of said clauses having a greater fitness value than other clauses, those clauses having a greater fitness value forming a selected clauses group;
• applying a perturbation to said alleles of said selected clauses to create additional clauses and adding said additional clauses to said selected clauses group;
• repeating, at least one of, said applying a fitness function step, selecting a plurality of said clauses step and applying a perturbation step until a predetermined fitness value is reached for said selected clauses;
• applying said selected clauses to the operational data from said at least one machine to determine whether the operational data indicates a past, present or future event.