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: 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: Industrial processes and equipment are prone to operational changes and faulty operation of such processes and equipment can adversely affect output of the overall setup. Existing systems for monitoring and fault detection consider individual instances of data for fault detection, which may not be suitable for industrial processes. Disclosed herein is a system and a method for anomaly detection in an industrial enterprise. The system collects data from a plurality of sensors as input. The system processes the collected data along temporal dimension, during which the data is split to multiple segments of fixed window size. Data in each segment is processed to identify anomalous data, and data in segments identified as containing the anomalous data is further processed to identify one or more sensors that are faulty and are contributing to the anomalous data.

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: Industrial processes and equipment are prone to operational changes and faulty operation of such processes and equipment can adversely affect output of the overall setup. Existing systems for monitoring and fault detection consider individual instances of data for fault detection, which may not be suitable for industrial processes. Disclosed herein is a system and a method for anomaly detection in an industrial enterprise. The system collects data from a plurality of sensors as input. The system processes the collected data along temporal dimension, during which the data is split to multiple segments of fixed window size. Data in each segment is processed to identify anomalous data, and data in segments identified as containing the anomalous data is further processed to identify one or more sensors that are faulty and are contributing to the anomalous data.