Automated detection of anomalous industrial process operation

A method of detecting operational changes in an industrial process, comprising:

• receiving, by a reporting service executing on one or more computing devices, time-series data for analysis, the time-series data being associated with a process control system, and the time-series data representing one or more values of a process control tag of the process control system over an interval of time;
• analyzing, by the reporting service, the process data by:
• apportioning the received time-series data into a plurality of sub-intervals, the plurality of sub-intervals comprising the interval of time, and each sub-interval including a predetermined number of individual data values sampled from the time-series data,
• determining a minimum data value of the individual data values of each sub-interval,
• determining a maximum data value of the individual data values of each sub-interval,
• executing an entropy calculation of the individual data values of each sub-interval, and
• scaling the entropy of each sub-interval based on the determined minimum and maximum data values thereof;
• detecting, by the reporting service, that one or more sub-intervals are anomalous relative to an expected value by performing a statistical analysis for each sub-interval;
• and
• publishing, by the reporting service, at least one report indicative of the detected anomalous sub-intervals into a report database.

A computer-readable storage device having processor readable instructions stored thereon including instructions that, when executed by a processor, implement a reporting service for detecting operational changes in an industrial process, the reporting service configured to:

• receive time-series data, wherein the time-series data is associated with a process control system, and wherein the time-series data represents one or more values of a process control tag of the process control system over an interval of time;
• apportion the retrieved time-series data into a plurality of sub-intervals, wherein the plurality of sub-intervals comprise the interval of time, and wherein each sub-interval includes a predetermined number of individual data values sampled from the time-series data;
• determine a minimum data value of the individual data values of each sub-interval;
• determine a maximum data value of the individual data values of each sub-interval;
• execute an entropy calculation of each sub-interval;
• scale the entropy of each sub-interval based on the determined minimum and maximum data values thereof;
• detect that one or more sub-intervals are anomalous relative to an expected value by performing a statistical analysis for each sub-interval;
• and
• publish at least one report indicative of the detected anomalous sub-intervals into a report database.