Abstract: Embodiments of the present disclosure provide for asset lifetime monitoring. Estimated values for factors representing root causes of operational anomalies of an asset may be generated utilizing one or more models. Estimated remaining lifetime values for one or more root cause variables may be generated that indicate a time until the value for a root cause variable is estimated to reach a particular limit threshold corresponding to the root cause variable, and/or an estimated second remaining lifetime value for an asset health index representing a combination of one or more root cause variables. The second remaining lifetime value for the asset health index may be provided to enable processing of the second remaining lifetime value as the remaining useful lifetime of the asset based on overall root cause variables.

Abstract: Various embodiments described herein relate to management of a portfolio of assets. In this regard, a request to generate a dashboard visualization associated with a portfolio of assets received. The request includes an asset descriptor describing one or more assets in the portfolio of assets. Furthermore, in response to the request, aggregated data associated with the portfolio of assets is obtained based on the asset descriptor and metrics for an asset hierarchy associated with the portfolio of assets are determined based on a model related to a time series mapping of attributes for the aggregated data. The dashboard visualization comprising the metrics for an asset hierarchy associated with the portfolio of assets is also provided to an electronic interface of a computing device.

Abstract: An Asset Performance Monitoring (APM) based-system includes an APM workflow engine receiving measured data values for dependent process variables from a process. A process and control simulator includes a dynamic operator training simulations (OTS) model. The APM workflow engine initializes the OTS model at a defined operating point at values for independent process variables from the measured data values to synchronize to the OTS model. The OTS model simulates at the defined operating point to generate model predicted values for key dependent process variables used to generate a trained data model that generates trained model predicted values for the key dependent process variables. The trained model predicted values are compared to the measured data values to generate symptom inputs processed by fault models to identify a suspected fault with the processing equipment/process. The APM workflow engine triggers an alert relating to inspection or maintenance action regarding the processing equipment/process.

Abstract: An Asset Performance Monitoring (APM) based-system includes an APM workflow engine receiving measured data values for dependent process variables from a process. A process and control simulator includes a dynamic operator training simulations (OTS) model. The APM workflow engine initializes the OTS model at a defined operating point at values for independent process variables from the measured data values to synchronize to the OTS model. The OTS model simulates at the defined operating point to generate model predicted values for key dependent process variables used to generate a trained data model that generates trained model predicted values for the key dependent process variables. The trained model predicted values are compared to the measured data values to generate symptom inputs processed by fault models to identify a suspected fault with the processing equipment/process. The APM workflow engine triggers an alert relating to inspection or maintenance action regarding the processing equipment/process.

Abstract: An apparatus, method, and non-transitory machine-readable medium provide for improved feature extraction and fault detection in a non-stationary process through unsupervised machine learning. The apparatus includes a memory and a processor operably connected to the memory. The processor receives training data regarding a field device in an industrial process control and automation system; extracts a meaningful feature from the training data; performs an unsupervised classification to determine a health index for the meaningful feature; identifies a faulty condition of real-time data using the health index of the meaningful feature; and performs a rectifying operation in the industrial process control and automation system for correcting the faulty condition of the field device.

Abstract: An apparatus, method, and non-transitory machine-readable medium provide for improved feature extraction and fault detection in a non-stationary process through unsupervised machine learning. The apparatus includes a memory and a processor operably connected to the memory. The processor receives training data regarding a field device in an industrial process control and automation system; extracts a meaningful feature from the training data; performs an unsupervised classification to determine a health index for the meaningful feature; identifies a faulty condition of real-time data using the health index of the meaningful feature; and performs a rectifying operation in the industrial process control and automation system for correcting the faulty condition of the field device.

Abstract: A framework for autonomous predictive health monitoring includes online monitoring, offline training, and self-learning components. The monitoring component includes analyzing streaming incoming process data, which includes process variable and key performance indicators (KPIs), from multiple sources, in real time, to determine an overall health index, determine faults, diagnose and isolate faulty process variables that contribute to the health index, and predict a trend and a magnitude of the health index before failure. The self-learning component includes services linked to event management, to correct the health index from probabilities calculated based on operator feedback on true or false events after analyzing each of the detected events, self-tune limits and other model parameters, and trigger training of a model when a new normal pattern is detected.

Abstract: This disclosure provides systems and methods for process control system performance analysis using scenario data. A method includes receiving, by an analysis system, an operating status of a process control system. The method includes identifying, by the analysis system, one or more scenarios in a scenario knowledge base that have at least one output variable that corresponds to the operating status. The method includes producing an output report, by the analysis system, that includes the operating status, the identified one or more scenarios, and one or more input variables or input variable values that correspond to the identified one or more scenarios.

Abstract: A method includes associating multiple real-time applications with a framework. The real-time applications include applications for monitoring or controlling equipment in at least one industrial facility. Each application has at least one input variable and at least one output variable. The method also includes identifying relationships between the input and output variables of the applications to identify data dependencies. The method further includes receiving data updates at the framework and notifying at least one of the applications of the data updates based on the data dependencies to support data-driven operation of the framework. The data-driven operation of the framework provides data to the applications to support performance monitoring of the equipment, analysis of the equipment's operation, and/or identification of abnormal equipment conditions.

Abstract: A method includes associating multiple real-time applications with a framework. The real-time applications include applications for monitoring or controlling equipment in at least one industrial facility. Each application has at least one input variable and at least one output variable. The method also includes identifying relationships between the input and output variables of the applications to identify data dependencies. The method further includes receiving data updates at the framework and notifying at least one of the applications of the data updates based on the data dependencies to support data-driven operation of the framework. The data-driven operation of the framework provides data to the applications to support performance monitoring of the equipment, analysis of the equipment's operation, and/or identification of abnormal equipment conditions.