Abb Schweiz Ag

Honeywell International Inc.

Jinendra K Gugaliya

Kaushik Ghosh

Nandkishor Kubal

Mangesh Dattatraya Kapadi

Naveen Bhutani

Rahul Kumar-Vij

Abhilash Pani

Marcel Dix

Mithun P Acharya

Lingathurai Palanisamy

Moncef Chioua

Jagadeesh Brahmajosyula

Veronika Domova

Aldo Dagnino

Arun Gupta

Vinay Kariwala

Gudi Ravindra

Ravindra D Gudi

Arpit Sisodia

Wilhelm Wiese

Vidyadhar Rao

Promodh Narayan Ravichandran

Nikhil Venkata Saisantosh Podila

Jayaram Balasubrahmanyan

Guan Tien Tan

Jinyi Mo

Sam Ade Jacobs

Harvinder Singh

Alessandro Bongiovi

Aldo Dangnino

Arun Kumar Gupta

The present application discloses method and sewer () for detecting faults associated with a gas chromatograph device () in a process plant. The gas chromatograph device () is associated with a database () 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. The fault signature data is generated using machine learning model trained by providing the faults and the historic gas chromatogram.

Method and control system for detecting faults associated with gas chromatograph device in process plant

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.

System and method for generating HMI graphics

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.

Fault diagnosis system and method for electric drives

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.

Anomaly detection system and method for electric drives

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.

Method for controlling a process plant using transition data

Unique systems, methods, techniques and apparatuses of an alarm management system are disclosed herein. One exemplary embodiment is a method for monitoring an industrial plant comprising determining a sequence of alarm events for each of a plurality of time intervals including a first alarm event of a plurality of alarm events and a second alarm event of the plurality of alarm events; determining a count of the first alarm events and second alarm events; determining the alarm events exceed a support threshold value; determining a third count of a sub-sequence of the sequences of alarm events including the first alarm event followed by the second alarm event in response to determining the first count and the second count exceeds the support threshold value; determining a ratio using the first count, the second count, and the third count exceeds a display threshold value; and displaying the sub-sequence.

Industrial plant alarm management

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.

Method for identifying a sequence of events associated with a condition in a process plant

Multiple models for various stages of a non-linear process control are developed by clustering perturbation data obtained from the nonlinear process so as to permit multiple local data regions to be identified as a function of substantial similarity between the data, wherein the data of first data set represent the non-linear process. A discrete model corresponding to each of the local data regions is generated. The number of the discrete models may be reduced as a function of prediction error between actual outputs of the process and predicted outputs of the models and as a function of a gap metric based on closed loop similarity and frequency response similarity between the models.

Decomposition of nonlinear dynamics using multiple model approach and gap metric analysis

A process for optimizing a portfolio of products produced from a crop includes the use of an objective function to determine optimized quantities of the products in the portfolio. The objective function, for example, includes quantity terms for the products. The objective function may also include additional terms such as an energy term and/or a storage term. The energy term, for example, relates to an amount of energy required to produce the products. The storage term, for example, relates to the cost of storing products. The crop, for example, may be sugarcane, and the products, for example, may be sugar, molasses, bagasse, biofuel, electricity, and/or carbon credits.

Computer evaluation of crop related industries

A product recovery prediction model that models recovery of a product from a crop is generated by inputting training product recovery data by date, age, and variety. A first model that models season dependent effects on product recovery, and/or a second model that models age dependent effects on product recovery, and/or a third model that models other effects such as, for example, weather dependent effects on product recovery are generated. The first, second, and/or third models are combined, and the product recovery prediction model is generated based on the combined first, second, and/or third models and on the training product recovery data. The crop may be sugarcane, and the product may be sugar. The product recovery prediction model may be used to predict recovery of the product to use for harvesting or any economical decisions.

System and method for sugarcane recovery estimation

Growing community of inventors

Karnataka, India

Jinendra K Gugaliya