Method and system for identifying and forecasting the development of faults in equipment

A method for identifying incipient defects in process units, which consists in performing steps of:

• receiving data from the test unit that characterize operating parameters of said unit;
• forming a reference sample of received unit operating parameters, the said sample corresponding to a continuous operation time interval of the test unit;
• building up a state matrix of reference sample parameter values;
• building up at least one empirical model for predicting state of the test unit, which represents the unit's state in a multidimensional space of unit parameters;
• defining integral criteria that characterize deviations of the test unit's parameters;
• determining imbalances that reflect degree of unit operating parameters influence on said deviations of the test unit operating parameters;
• analyzing the input information from the test unit using the obtained set of empirical models by comparing the received test unit parameters with the model parameters within a given time interval;
• modifying the reference sample by replenishing it with points collected within the new time period and filtering points corresponding to the mode of operation described by the model and corresponding to a new functional state of the test unit;
• updating pre-built empirical models based on the filtered sample;
• determining degree of input test unit parameters deviation from parameters of empirical models for a given period of time based on said integral criteria and revealing imbalances for these parameters;
• sorting calculated imbalances to determine upper imbalances, which represent the parameters that most strongly contribute to the test unit state change;
• identifying at least one significant process abnormality in at least one test unit parameter based on certain integral criteria and upper imbalances;
• identifying type of defect in the test unit for each significant process abnormality;
• compiling a digital classifier of defects in the unit based on identified significant process abnormalities, containing identified parameters of process abnormalities in various operating modes of the test unit;
• identifying at least one incipient defect is determined and predicting its development by means of processing input information from the test unit by a neural network trained on the generated digital classifiers.