System, method, and computer program for early event detection

An apparatus comprising:

• at least one memory configured to store operating data associated with an industrial process;
• and
• at least one processor configured to:
• generate a first model and a second model using a first training set of the operating data, the at least one processor configured to generate the first model by producing a first Principal Component Analysis (PCA) model and reducing a number of principal components in the first PCA model, the at least one processor configured to generate the second model by adjusting a membership function and a digital latch associated with a Fuzzy Logic model, the first training set comprising operating data associated with normal operation of the industrial process;
• form an early event detection (EED) model using the first and second models;
• validate at least one of the first model and the EED model by generating a third model and comparing operation of at least one of the first model and the EED model to operation of the third model, the third model comprising a second PCA model, the at least one processor configured to generate the second PCA model using a second training set of the operating data, the second training set comprising operating data associated with normal operation of the industrial process distinct from the first training set;
• use the EED model to predict one or more events associated with the industrial process, the one or more events associated with one or more abnormal conditions in the industrial process, the at least one processor configured to predict the one or more events by:
• generating one or more initial event predictions using current operating data associated with the industrial process and the first model;
• and
• adjust the one or more initial event predictions by at least one of:
• normalizing, shaping, and tuning an output of the first model using the second model;
• and
• generate one or more notifications identifying the one or more predicted events.

A tangible computer-readable medium embodying a computer program, the computer program comprising computer readable program code for:

• receiving operating data associated with an industrial process;
• generating a first model and a second model using a first training set of the operating data, the first model generated by producing a first Principal Component Analysis (PCA) model and reducing a number of principal components in the first PCA model, the second model generated by adjusting a membership function and a digital latch associated with a Fuzzy Logic model, the first training set comprising operating data associated with normal operation of the industrial process;
• forming an early event detection (BED) model using the first and second models;
• validating at least one of the first model and the EED model by generating a third model and comparing operation of at least one of the first model and the EED model to operation of the third model, the third model comprising a second PCA model generated using a second training set of the operating data, the second training set comprising operating data associated with normal operation of the industrial process distinct from the first training set;
• using the EED model to predict one or more events associated with the industrial process, the one or more events associated with one or more abnormal conditions in the industrial process, the one or more events predicted by:
• generating one or more initial event predictions using current operating data associated with the industrial process and the first model;
• and
• adjusting the one or more initial event predictions by at least one of:
• normalizing, shaping, and tuning an output of the first model using the second model;
• and
• generating one or more notifications identifying the one or more predicted events.

A system comprising:

• a plurality of controllers each configured to generate output data for controlling one or more actuators associated with an industrial process using input data from one or more sensors;
• and
• an early event detector configured to:
• receive operating data comprising at least some of the input or output data;
• generate a first model and a second model using a first training set of the operating data, the early event detector configured to generate the first model by producing a first Principal Component Analysis (PCA) model and reducing a number of principal components in the first PCA model, the early event detector configured to generate the second model by adjusting a membership function ad a digital latch associated with a Fuzzy Logic model, the first training set comprising operating data associated with normal operation of the industrial process;
• form a early event detection (EED) model using the first ad second models;
• validate at least one of the first model ad the EED model by generating a third model and comparing operation of at least one of the first model ad the EED model to operation of the third model, the third model comprising a second PCA model, the early event detector configured to generate the second PCA model using a second training set of the operating data, the second training set comprising operating data associated with normal operation of the industrial process distinct from the first training set;
• use the EED model to predict one or more events associated with the industrial process, the one or more events associated with one or more abnormal conditions in the industrial process, the one or more events predicted by:
• generating one or more initial event predictions using current operating data associated with the industrial process and the first model;
• and
• adjusting the one or more initial event predictions by at least one of:
• normalizing, shaping, and tuning an output of the first model using the second model;
• and
• generate one or more notifications identifying the one or more predicted events.