Method and system for localization of faults in an industrial manufacturing plant

A system, comprising:

• one or more hardware processors;
• a communication interface;
• and
• a memory storing a plurality of instructions, wherein the plurality of instructions when executed, cause the one or more hardware processors to:
• receive a plurality of multivariate time-series data corresponding to operations of an industrial manufacturing plant, as input data, wherein the plurality of multivariate time-series comprise information from a plurality of sensors monitor working of a plurality of components of the industrial manufacturing plant;
• pre-process the input data to generate a pre-processed data;
• compute a plurality of soft-sensor data using the pre-processed data, wherein a plurality of soft-sensors corresponding to the computed soft-sensor data includes physics-based soft sensors and data-driven soft sensors;
• combine the pre-processed data with the plurality of soft-sensor data to generate a combined plant data;
• perform a fault analysis on the combined plant data, by a) determining whether one or more faults are present in the combined plant data, and b) identifying a fault detection point if the one or more faults are present in the combined plant data;
• and
• perform a fault localization to determine one or more variables from the combined plant data having a dominant effect on the one or more faults, if the combined plant data is determined to have the one or more faults, wherein performing, the fault localization comprises applying a multi-level variable perturbation, by:
• perturbing one or more of the variables from the combined plant data, input to a selected diagnosis model across the fault detection point to calculate a perturbation score corresponding to each of the one or more of the variables, in a plurality of iterations;
• computing a contribution score as equal to value of a cumulative perturbation score of the plurality of iterations, and sorting the plurality of variables in the order of the contribution score;
• eliminating all variables from among the one or more variables, having value of the contribution score below a predetermined percentile, to obtain a plurality of candidate variables, wherein the plurality of candidate variables are variables having the dominant effect on the one or more faults;
• and
• iteratively performing perturbation on the plurality of candidate variables, a) for a predetermined number of perturbation levels, or b) till a predetermined number of localized variables are obtained.

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

• receiving a plurality of multivariate time-series data corresponding to operations of an industrial manufacturing plant, as input data, wherein the plurality of multivariate time-series data comprise information from a plurality of sensors monitoring working of a plurality of components of the industrial manufacturing plant;
• pre-processing the input data to generate a pre-processed data;
• computing a plurality of soft-sensor data using the pre-processed data, wherein a plurality of soft-sensors corresponding to the computed soft-sensor data includes physics-based soft sensors and data-driven soft sensors;
• combining the pre-processed data with the plurality of soft-sensor data to generate a combined plant data;
• performing a fault analysis on the combined plant data, comprising a) determining whether one or more faults are present in the combined plant data, and b) identifying a fault detection point if the one or more faults are present in the combined plant data;
• and
• performing a fault localization to determine one or more variables from the combined plant data having a dominant effect on the one or more faults, if the combined plant data is determined to have the one or more faults, wherein performing, the fault localization comprises applying a multi-level variable perturbation, comprising:
• perturbing one or more of the variables from the combined plant data, input to a selected diagnosis model across the fault detection point to calculate a perturbation score corresponding to each of the one or more of the variables, in a plurality of iterations;
• computing a contribution score as equal to value of a cumulative perturbation score of the plurality of iterations, and sorting the plurality of variables in the order of the contribution score;
• eliminating all variables from among the one or more variables, having value of the contribution score below a predetermined percentile, to obtain a plurality of candidate variables, wherein the plurality of candidate variables are variables having the dominant effect on the one or more faults;
• and
• iteratively performing perturbation on the plurality of candidate variables, a) for a predetermined number of perturbation levels, or b) till a predetermined number of localized variables are obtained.