Abstract: Examples techniques to manage performance of assets installed in an industrial facility are described. Operating parameters of a component from amongst one or more components of an asset are monitored. A range of values indicative of normal operational behavior of the asset is predefined for each operating parameter of the component. One or more operating parameters of the component are identified to deviate from corresponding predefined range of values. Deviation in an operating parameter is a symptom of a fault. A severity index is assigned to each symptom of a fault based on an amount of the deviation in the respective operating parameter from corresponding predefined range of values. Further, a fault severity indicator is assigned for the fault associated with the component based on severity indexes of each symptom of the fault. A corrective action is caused when the fault severity indicator is above a predetermined threshold.

Abstract: Techniques for risk management in a supply chain are described. In one aspect, to determine an overall impact of a fault associated with an equipment operating in a facility, multiple operational parameters of the equipment are monitored to detect an anomaly, based on which a fault in the equipment is identified. On identifying the fault, a first impact factor corresponding to an energy impact, a second impact factor corresponding to an emission impact, and a third impact factor corresponding to an economic impact are derived based on the multiple operational parameters. Further, an integrated risk indicator is computed from the impact factors to determine a current overall impact of the fault on the equipment. Accordingly, a risk profile including one or more remedial strategies is generated based on the integrated risk indicator and is issued to personnel of the facility for risk management.

Abstract: Examples techniques to select training data to train Artificial Intelligence models to monitor industrial processes are described. From historical data relating to an industrial process, a range of values exhibited by operating parameters of the industrial process under normal operation is estimated. One or more steady time windows are identified for the operating parameters. A steady time window of an operating parameter is a duration of time where values of the operating parameter are within the estimated range of values. Based on the identified steady time windows, a composite steady time window is determined. The composite steady time window is a duration of time where a maximum of the identified steady time windows overlap. The data corresponding to the composite steady time window is provided as training data to the AI model.

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.