Abstract: The present application discloses method and sewer (111) for detecting faults associated with a gas chromatograph device (100) in a process plant. The gas chromatograph device (100) is associated with a database (110) configured to store a measured chromatogram, and historic chromatograms. Initially, the server receives the measured chromatogram from the database. Upon receiving the measured chromatogram, the server is configured to detect at least one real-time symptom for measured chromatogram. The real-time symptoms may be detected by comparing the historic chromatograms with predetermined configuration data to the measured chromatogram. Upon detecting the real-time symptoms, the server is configured to determine faults associated with the gas chromatograph device. The faults are determined by mapping the real-time symptoms and fault signature data received from the database.

Abstract: A computer-implemented method and arrangements for generating human machine interface, HMI, graphics associated with an industrial automation system implementing machine learning include receiving migrated HMI graphics. The migrated HMI graphics are obtained from a legacy HMI graphics and comprise a plurality of industrial objects. Further, the method comprises identifying a missing industrial object in said migrated HMI graphics using a historical labelled dataset comprising a plurality of labelled HMI graphics of the industrial object that comprises a graphical object and a data object. Once, the missing industrial object is identified the migrated HMI graphics is therefore corrected to include the identified missing industrial object and a corrected HMI graphics is generated. Furthermore, the method comprises transmitting a notification indicating the corrected HMI graphics to a computing device.

Abstract: The present disclosure relates to diagnosing a fault in an electric drive of a process plant. The fault diagnosis method includes receiving fault data from an electric drive upon occurrence of the fault. The method further includes obtaining a fault code and a drive type associated with the electric drive from the fault data. In addition, the method comprises determining one or more drive parts to replace by comparing the fault code and the drive type with a mapped data for a plurality of drive types. The mapped data for each drive type includes a relation between a plurality of fault codes and a plurality of drive parts. The method further includes initiating a maintenance operation involving replacement of the one or more drive parts to address the fault.

Abstract: One illustrative method comprises retrieving a set of drive information associated with an operation of an electric drive in a time period from a drive control unit and one or more sensor units. The method further includes obtaining reliability information associated with the electric drive from a server. The reliability information is computed in the server, with the set of drive information and a model corresponding to a drive type of the electric drive. The reliability information includes a probability of occurrence of the abnormal condition in a specified future time period. The method further includes, providing at least one condition indication regarding the abnormal condition to one of a human machine interface and a drive controller based on the probability of occurrence of the abnormal condition.

Abstract: A computer-implemented method and arrangements for generating human machine interface, HMI, graphics associated with an industrial automation system implementing machine learning include receiving migrated HMI graphics. The migrated HMI graphics are obtained from a legacy HMI graphics and comprise a plurality of industrial objects. Further, the method comprises identifying a missing industrial object in said migrated HMI graphics using a historical labelled dataset comprising a plurality of labelled HMI graphics of the industrial object that comprises a graphical object and a data object. Once, the missing industrial object is identified the migrated HMI graphics is therefore corrected to include the identified missing industrial object and a corrected HMI graphics is generated. Furthermore, the method comprises transmitting a notification indicating the corrected HMI graphics to a computing device.

Abstract: The present disclosure discloses method and control system for detecting condition of plurality of process equipment in industrial plant. The proposed methodology implements machine learning techniques for detecting the fault. The machine learning techniques include supervised learning and unsupervised learning. Real-time values of plurality of parameters associated with each of the plurality of process equipment along with weight matrix and threshold attribute is used in the unsupervised learning to detect the condition. The weight matrix associated with each of the plurality of process equipment is generated using the supervised learning. Plurality of historic values of the plurality of parameters relating to the corresponding process equipment are analysed in the supervised learning to generate the weight matrix for the corresponding process equipment.

Abstract: The present application discloses method and sewer (111) for detecting faults associated with a gas chromatograph device (100) in a process plant. The gas chromatograph device (100) is associated with a database (110) configured to store a measured chromatogram, and historic chromatograms. Initially, the server receives the measured chromatogram from the database. Upon receiving the measured chromatogram, the server is configured to detect at least one real-time symptom for measured chromatogram. The real-time symptoms may be detected by comparing the historic chromatograms with predetermined configuration data to the measured chromatogram. Upon detecting the real-time symptoms, the server is configured to determine faults associated with the gas chromatograph device. The faults are determined by mapping the real-time symptoms and fault signature data received from the database.

Abstract: One illustrative method comprises retrieving a set of drive information associated with an operation of an electric drive in a time period from a drive control unit and one or more sensor units. The method further includes obtaining reliability information associated with the electric drive from a server. The reliability information is computed in the server, with the set of drive information and a model corresponding to a drive type of the electric drive. The reliability information includes a probability of occurrence of the abnormal condition in a specified future time period. The method further includes, providing at least one condition indication regarding the abnormal condition to one of a human machine interface and a drive controller based on the probability of occurrence of the abnormal condition.

Abstract: The present disclosure relates to diagnosing a fault in an electric drive of a process plant. The fault diagnosis method includes receiving fault data from an electric drive upon occurrence of the fault. The method further includes obtaining a fault code and a drive type associated with the electric drive from the fault data. In addition, the method comprises determining one or more drive parts to replace by comparing the fault code and the drive type with a mapped data for a plurality of drive types. The mapped data for each drive type includes a relation between a plurality of fault codes and a plurality of drive parts. The method further includes initiating a maintenance operation involving replacement of the one or more drive parts to address the fault.

Abstract: The present invention discloses a method for controlling a process in a process plant using a controller. The method comprises receivable associated with the process, determining a first value of at least one key performance indicator associated with the transition from the process data of the first process variable between the first steady state and the second steady state, comparing the determined first value of the at least one key performance indicator against a threshold value of the at least one key performance indicator; and determining a correction factor for modifying a set point of the process variable based on the comparison, for controlling the process.

Abstract: The invention provides a method for identifying a sequence of events associated with a condition in a process plant using a control system. The method comprises recording process data, which is timestamped, and recording audio input from each personnel of a plurality of personnel of the process plant. The audio input is synchronized according to time with the process data. A keyword is identified from the temporally synchronized content of each audio input, and compared with the process information of one or more of an event and an equipment, for identifying at least one of a new process information and a supplementary process information related to the condition. One or more of the new process information and the supplementary process information identified for each keyword, and the plurality of events identified from the process data, are used for identifying the sequence of events associated with the condition.

Abstract: The present invention discloses a method for controlling a process in a process plant using a controller. The method comprises receivable associated with the process, determining a first value of at least one key performance indicator associated with the transition from the process data of the first process variable between the first steady state and the second steady state, comparing the determined first value of the at least one key performance indicator against a threshold value of the at least one key performance indicator; and determining a correction factor for modifying a set point of the process variable based on the comparison, for controlling the process.

Abstract: The invention provides a method for identifying a sequence of events associated with a condition in a process plant using a control system. The method comprises recording process data, which is timestamped, and recording audio input from each personnel of a plurality of personnel of the process plant. The audio input is synchronized according to time with the process data. A keyword is identified from the temporally synchronized content of each audio input, and compared with the process information of one or more of an event and an equipment, for identifying at least one of a new process information and a supplementary process information related to the condition. One or more of the new process information and the supplementary process information identified for each keyword, and the plurality of events identified from the process data, are used for identifying the sequence of events associated with the condition.