Blog Summary All the variables are not equally responsible for system's behavior, so it can be really helpful if the total number of variables to be monitored to a few important ones. ML techniques which can really help in determining the minimum set variables which largely captures the process behavior. Table content Explanation Explanation : Machine Learning can help in process monitoring in various ways. In a typical process plant there are many process variable are to be

Blog Summary In AI/ML domain, model development approaches can be segregated as "supervised" or "unsupervised". Regression & Classification models are generally supervised kind of models; and clustering models use unsupervised approach. Table content Explanation Explanation : In AI/ML domain, model development approaches can be segregated as "supervised" or "unsupervised". In supervised learning approach the data includes inputs and associated outputs. However the unsupervise

Blog Summary In process and manufacturing industries it is not always feasible to measure every variables of interest. In process industries soft sensors can be really helpful in monitoring complex processes. Table content Explanation Explanation : In process and manufacturing industries it is not always feasible to measure every variables of interest. In those scenarios soft sensors can be really helpful. So, Soft sensors, also known as virtual sensors, are models used to es

There are techniques in AI/ML to solve all of the above challenges. For example Recurrent Neural Network (RNN) can be used to process the time evolution data to build a classification model. As the system's behavior under each type of fault can be different , now with the Fault classification model using RNN whenever a fault occurs and the subsequent pattern of data helps in determining the type of fault. This is also know as Fault Diagnosis. When twin models are developed us

In process & manufacturing applications identifying the onset of system abnormality / failure at the earliest is very crucial. Models which help in determining the occurrences of such abnormalities as "fault detection" models. The techniques to isolate the variables which are causing the abnormality is generally known as "fault identification". Along with these there are scenarios where the type of fault needs to be determined, this generally known as "Fault Classification or

Unplanned down times and shut-downs, one of the major challenges industries face when disruptions happen due to an unanticipated break-down event. So plants always rely on preventive maintenance to avoid such occurrences. However , in order to optimize the productivity it is always desirable to have minimal number maintenance schedules. Data driven "predictive analytics" is one of the better options to optimize between maintenance stoppages and risk of unplanned disruptions.