Abstract: This disclosure relates to a method and system for hybrid data augmentation for estimating performance degradation in industrial plant. Performance degradation in industrial plants cannot be measured by sensors or laboratory measurements and there are no methods to annotate performance degradation state. The embodiments of the present disclosure provide a knowledge-based data augmentation that use physics based information to model performance degradation. The disclosed method augments high fidelity data with knowledge-based methods into high and low confidence data which are used to calculate performance score of high confidence data. A physics-informed machine learning model is trained on high confidence data. The resulting model is then used to predict performance score for low confidence data. The model is further used for training prognostics and diagnostics models to predict and identify root causes responsible for performance degradation.

Abstract: System (100) for optimization of industrial operation, based on simulation data thereof, system comprising processing unit (102) configured to generate simulation data related to industrial operation; generate knowledge graph ontology; generate knowledge graph instance representing simulation data; receive user request to analyze quality of industrial operation, from user device (104) of user; analyze quality of industrial operation; generate response to user request, based on analyzed quality of industrial operation; send response to user device; receive first user input from user device, wherein first user input is indicative of user approval on industrial operation; and when user approval on industrial operation is positive, employ simulation data for implementation of industrial operation; or when user approval on industrial operation is negative, generate updated simulation data, based on analysis of quality of industrial operation, and repeat aforementioned steps for updated simulation data.

Abstract: Fault diagnosis in industries typically involves identification of key variables/sensors bearing fault signature, classification of detected fault into known fault classes and detecting root causes/sources of the fault. This disclosure relates to a method and system for a deep learning based causal inference in a multivariate time series data of abnormal events and failures in industrial manufacturing processes and equipment. The system generates causal networks for non-linear and non-stationary multivariate time series data. The causal network learns for a dynamic non-stationary and nonlinear complex process or system fault using observed data without any prior process knowledge. The causal networks of faults are identified in real-time using a deep learning-based causal network learning technique.

Abstract: Existing systems for fault detection and classification have the disadvantage that they have limited or no capability for fault localization and root cause identification, probably due to the challenges associated with modeling the nonlinear interactions among process variables and capturing the nonstationary behavior that is typical of most industrial processes. The disclosure herein generally relates to industrial manufacturing systems, and, more particularly, to method and system for localization of faults in an industrial manufacturing plant. The system uses a perturbation based approach for fault localization, in which the system determines variables having dominant effect on identified faults, in terms of a perturbation score calculated for each of the variables.

Abstract: A method (500) and system (100) for optimizing workforce allocation across units is disclosed. The method (500) includes receiving data associated with the units. The method (500) may include identifying employee pool, skill demand and workload index of each of units. The method (500) may further include generating schedule for each unit based on skill demand, workload index, and scheduling constraints. The method (500) may include identifying surplus units and deficit units, and deficit skills based on generated schedule. Further, the method (500) included determining that employee relocation is required based on surplus units, deficit units and deficit skills. The method (500) further includes identifying employees that are eligible for relocation from surplus units based on deficient skills and employee preferences. The method (500) further includes validating employee relocation options based on identified eligible employees.

Abstract: The embodiments of the present disclosure herein address unresolved limitations in handling feature selection, inadequacies in capturing nonlinear relationships, issues related to interpretability in Artificial Intelligence (AI) models, and a lack of adaptability to the dynamic nature of industrial environments. Embodiments herein provide a method and system for a recursive ensemble feature selection using an explainable artificial intelligence (XAI). The method begins with a thorough digital modelling process, where the careful selection of features and identification of optimal time lags take precedence. By utilizing XAI techniques, the system ensures a transparent and interpretable selection of features and time lags. This method forms a robust foundation for subsequent forecasting model development tailored to specific datasets.

Abstract: Current approaches for identifying accretion in rotary kiln lack access to information regarding the internal condition of the rotary kiln such as temperatures of the wall, gas or solid bed and specific methodology that is required to calculate accretion and hence forecast. Present disclosure provides method and system for forecasting and diagnosing accretion in rotary kiln. The system first takes historical data associated with rotary kiln, real-time data, and a future time horizon information. Then, system predicts accretion scores for future time horizon based on received data using a pretrained accretion forecasting model which are further utilized to estimate a rate of accretion. Thereafter, the system identifies high accretion (HA) operating regime and low accretion (LA) operating regime over predefined time period. Further, system identifies one or more accretion variables responsible for causing each of the HA operating regime and the LA operating regime using an accretion diagnostic model.

Abstract: As discussed earlier, accretion happens to be the most critical problem faced by users of the rotary kiln as it leads to shutdown of the rotary kiln which ultimately leads to reduction in production. Currently available accretion monitoring systems require expensive equipment and sensors which increases the production cost. Present disclosure provides method and system for monitoring accretion happening inside the rotary kiln. The system first takes real-time data associated with a rotary kiln as input. The system then localizes accretion clusters present in the rotary kiln using an accretion localization model. Thereafter, the system estimates the accretion probability score based on the HTM statistics calculated based on the accretion cluster information and real-time data using an accretion scoring model. Further, the system compute accretion score representative of real-time accretion condition of the rotary kiln based on the accretion probability score and the inputs.

Abstract: The disclosure is a method and a system for root cause identification (RCI) of faults in manufacturing and process industries. With complex interrelated multivariate data in manufacturing and process industries, the process of root RCI of faults is challenging. Further, the existing techniques for RCI have significant dependency on manual inputs and subject matter knowledge/experts. The disclosure is method and a system for root cause identification of a fault based on causal maps. The root cause of fault is identified in several steps including: generation of casual maps using data received from a manufacturing and process industry and root cause identification from the causal maps based on a Fault Traversal and Root Cause Identification (FTRCI) technique. The FTRCI identifies root cause from the causal map by identifying a fault traversal pathway from a leaf node in the causal map, wherein the fault traversal pathway is identified for even cyclic paths.

Abstract: This disclosure relates to a method and system for hybrid data augmentation for estimating performance degradation in industrial plant. Performance degradation in industrial plants cannot be measured by sensors or laboratory measurements and there are no methods to annotate performance degradation state. The embodiments of the present disclosure provide a knowledge-based data augmentation that use physics based information to model performance degradation. The disclosed method augments high fidelity data with knowledge-based methods into high and low confidence data which are used to calculate performance score of high confidence data. A physics-informed machine learning model is trained on high confidence data. The resulting model is then used to predict performance score for low confidence data. The model is further used for training prognostics and diagnostics models to predict and identify root causes responsible for performance degradation.

Abstract: Existing systems for fault detection and classification have the disadvantage that they have limited or no capability for fault localization and root cause identification, probably due to the challenges associated with modeling the nonlinear interactions among process variables and capturing the nonstationary behavior that is typical of most industrial processes. The disclosure herein generally relates to industrial manufacturing systems, and, more particularly, to method and system for localization of faults in an industrial manufacturing plant. The system uses a perturbation based approach for fault localization, in which the system determines variables having dominant effect on identified faults, in terms of a perturbation score calculated for each of the variables.

Abstract: It is important to know the flow rates of oil and gas from individual wells in connected oil and gas wells. The existing methods for multiphase flow measurement are prohibitively expensive and used infrequently. The system is configured to ingest real-time and non-real-time data from a plurality of well data sources. Utilizing this data, a plurality of physics-guided data-driven well surveillance models run in real-time for forecasting a plurality of parameters including the flow rates of oil, gas and brine from individual wells, computing the health of well assets and performing fault detection and localization in well assets. The system is also configured to automatically compose a well performance optimization problem based on the current performance of the wells and health of well assets and solve the problem to identify optimal process settings for improving the operation of connected oil and gas wells.

Abstract: Fault diagnosis in industries typically involves identification of key variables/sensors bearing fault signature, classification of detected fault into known fault classes and detecting root causes/sources of the fault. This disclosure relates to a method and system for a deep learning based causal inference in a multivariate time series data of abnormal events and failures in industrial manufacturing processes and equipment. The system generates causal networks for non-linear and non-stationary multivariate time series data. The causal network learns for a dynamic non-stationary and nonlinear complex process or system fault using observed data without any prior process knowledge. The causal networks of faults are identified in real-time using a deep learning-based causal network learning technique.

Abstract: The disclosure is a method and a system for root cause identification (RCI) of faults in manufacturing and process industries. With complex interrelated multivariate data in manufacturing and process industries, the process of root RCI of faults is challenging. Further, the existing techniques for RCI have significant dependency on manual inputs and subject matter knowledge/experts. The disclosure is method and a system for root cause identification of a fault based on causal maps. The root cause of fault is identified in several steps including: generation of casual maps using data received from a manufacturing and process industry and root cause identification from the causal maps based on a Fault Traversal and Root Cause Identification (FTRCI) technique. The FTRCI identifies root cause from the causal map by identifying a fault traversal pathway from a leaf node in the causal map, wherein the fault traversal pathway is identified for even cyclic paths.

Abstract: It is important to know the flow rates of oil and gas from individual wells in connected oil and gas wells. The existing methods for multiphase flow measurement are prohibitively expensive and used infrequently. The system is configured to ingest real-time and non-real-time data from a plurality of well data sources. Utilizing this data, a plurality of physics-guided data-driven well surveillance models run in real-time for forecasting a plurality of parameters including the flow rates of oil, gas and brine from individual wells, computing the health of well assets and performing fault detection and localization in well assets. The system is also configured to automatically compose a well performance optimization problem based on the current performance of the wells and health of well assets and solve the problem to identify optimal process settings for improving the operation of connected oil and gas wells.