Computer Systems And Methods For Performing Root Cause Analysis And Building A Predictive Model For Rare Event Occurrences In Plant-Wide Operations

A method as in 2 further comprising determining whether a sensor is of a lower relevancy includes:

• creating control zones based on sensor behavior;
• for each time series of the time series data, calculating a relevancy score between event zone realizations and control zone realizations;
• and
• designating a sensor as being of lower relevancy if the sensor is associated with a relatively low relevancy score.

A method as in 1 further comprising:

• obtaining real-time time series data from sensors associated with the precursor patterns;
• transforming the obtained real-time time series data to create signal representations of the time series data;
• and
• determining a probability of a particular KPI event based on the probabilistic networks and the signal representations of the time series data.

A method as in 7 wherein determining a probability of a particular KPI event includes:

• determining probabilities of the particular KPI event for the set of windows of time based on the probabilistic networks and the signal representations of the time series data;
• calculating a cumulative probability function based on the probabilities of the particular KPI event for the set of windows of time;
• calculating a probability density function based on the probabilities of the particular KPI event for the set of windows of time;
• and
• determining a probability of the particular KPI event and a concentration of the risk of the particular KPI event based on the cumulative probability function and probability density function.

A system for performing root-cause analysis on an industrial process, the system comprising:

• a plurality of sensors associated with the industrial process;
• memory;
• at least one processor in communication with the sensors and the memory, the at least one processor configured to:
• obtain, from the plurality of sensors and store in the memory, plant-wide historical time series data relating to at least one key process indicator (KPI) event;
• identify precursor patterns indicating that a KPI event is likely to occur, each precursor pattern corresponding to a window of time;
• select precursor patterns that occur frequently before a KPI event within corresponding windows of time and that occur infrequently outside of the corresponding windows of time;
• create in the memory a dependency graph based on the time series data and precursor patterns;
• create in the memory a signal representation for each source based on the dependency graph;
• and
• create in the memory and train, based on the dependency graph and the signal representations, probabilistic networks for a set of windows of time, the probabilistic networks configured to be used to predict whether a KPI event is likely to occur in the industrial process.

A system as in 10 wherein the processor is further configured to determine whether a sensor is of a lower relevancy by:

• creating control zones based on sensor behavior;
• for each time series of the time series data, calculating a relevancy score between event zone realizations and control zone realizations;
• and
• designating a sensor as being of lower relevancy if the sensor is associated with a relatively low relevancy score.

A system as in 9 wherein the processor is further configured to:

• obtain real-time time series data from sensors associated with the precursor patterns;
• transform the obtained real-time time series data to create signal representations of the time series data;
• and
• determine a probability of a particular KPI event based on the probabilistic networks and the signal representations of the time series data.

A system as in 14 wherein the processor is configured to determine a probability of a particular KPI event by:

• determining probabilities of the particular KPI event for the set of windows of time based on the probabilistic networks and the signal representations of the time series data;
• calculating a cumulative probability function based on the probabilities of a particular KPI event for the set of windows of time;
• calculating a probability density function based on the probabilities of a particular KPI event for the set of windows of time;
• and
• determining a probability of the particular KPI event and a concentration of the risk of the particular KPI event based on the cumulative probability function and probability density function.

A computer-implemented system for performing root-cause analysis on an industrial process, the system comprising:

• processor elements configured to perform root cause analysis of key process indicator (KPI) events based on industrial plant-wide historical data and to predict occurrences of KPI events based on real-time data, the processor elements including:
• a data assembly receiving as input a description and occurrence of KPI events, time series data for a plurality of sensors, and a specification of a look-back window during which dynamics leading to a subject KPI event in the industrial process develops, the data assembly performing a reduction of a very large set of data resulting in a relevancy score construction for each time series;
• a root cause analyzer in communication with the data assembly and configured to receive time series with high relevancy scores, the root cause analyzer using a multi-length motif discovery process to identify repeatable precursor patterns, and selecting precursors patterns having high occurrences in the look-back window for the construction of a probabilistic graph model, given a current set of observations for each precursor pattern, the constructed model enabling return probabilities of an event in the industrial process for various time horizons;
• and
• an online interface to the industrial process deploying the constructed model in a manner that specifies which precursor patterns should be monitored in real-time, and based on distance scores for each precursor pattern, the online model returning actual probabilities of subject plant events and the concentration of risk.