A defect profiling and tracking system for a process-manufacturing enterprise is provided. The system includes a memory and a processor. The processor is configured to access entity data for a plurality of entities of the process-manufacturing enterprise and process parameter data for one or more deviating entities. The processor is configured to analyze the entity data and the process parameter data for each of the deviating entities to determine a plurality of relationships between quality defects and the process parameters to generate a unique entity specific process signature (EPS) for each entity. The processor is configured to receive real-time process parameter data for one or more entities to generate a real-time process signature for the one or more entities and compare the real-time process signature of each entity with EPS corresponding to the entity to detect one or more EPS matches that are indicative of a quality defect.

The present invention discloses mechanical equipment running state fuzzy evaluation and prediction methods with occurrence probability of failure modes. The evaluation method includes the following steps: S1, determining a product set and a failure mode set thereof: S2, determining a feature set corresponding to each failure mode; S3, calculating the degradation degree of each feature; S4, calculating the occurrence probability of each failure mode; S5, calculating the membership degree of the occurrence probability of each failure mode; S6, fuzzy comprehensive evaluation is applied to the running state of the part; and S7, fuzzy comprehensive evaluation is applied to the running state of mechanical equipment. The invention further discloses a mechanical equipment running state prediction method.

Systems, methods and apparatus are disclosed that provide an approach to understand, analyze and generate useful output of patient-provider interactions in healthcare. Embodiments of the disclosure provide systems, methods and apparatus for creating understanding, and generating summaries and action item from an interaction between a patient, a provider and optionally a user.

Tuning model structures of dynamic systems are described herein. One method for tuning model structures of a dynamic system includes predicting a variable for each of a number of models associated with a number of model structures of a dynamic system, calculating a rate of error of the predicted variable for each of the number of models compared to an observed variable, determining a best model structure among the number of model structures based on the calculated rate of error, and creating a revised model structure using the best model structure to tune the number of model structures of the dynamic system.

Tuning model structures of dynamic systems are described herein. One method for tuning model structures of a dynamic system includes predicting a variable for each of a number of models associated with a number of model structures of a dynamic system, calculating a rate of error of the predicted variable for each of the number of models compared to an observed variable, determining a best model structure among the number of model structures based on the calculated rate of error, and creating a revised model structure using the best model structure to tune the number of model structures of the dynamic system.

A method of generating the knowledge base used for a programmable fuzzy controller comprising the steps of determining the relevant input and output variables to be controlled; creating artificial potential fields for each of said variables; sampling each of said potential fields in order to generate fuzzy membership functions; compiling said fuzzy membership functions into fuzzy sets; and mapping inputs fuzzy set to output fuzzy sets through a rule base. The relevant input and output variables are including: minimum, maximum, and equilibrium values; an importance weight; a non-linearity value; a control direction; and information as to whether said variable is an input or output variable. Further provided is a programmable fuzzy controller whose fuzzy knowledge base is obtained by the method described.