Abstract: Computer-implemented methods are provided herein for quantifying correlated risk in a network of a plurality of assets having at least one dependency, where each asset belongs to at least one entity. The method includes generating a dependency graph based on relationships between the assets, at least one dependency, and at least one entity, and executing a plurality of Monte Carlo simulations over the dependency graph. Executing a plurality of Monte Carlo simulations includes generating a seed event in the dependency graph, where the seed event has a probability distribution, and propagating disruption through the dependency graph based on the seed event. The method further includes assessing loss for each of the assets, and aggregating losses for two or more assets to determine correlated risk in the network.

Abstract: Computer-implemented methods are provided herein for quantifying correlated risk in a network of a plurality of assets having at least one dependency, where each asset belongs to at least one entity. The method includes generating a dependency graph based on relationships between the assets, at least one dependency, and at least one entity, and executing a plurality of Monte Carlo simulations over the dependency graph. Executing a plurality of Monte Carlo simulations includes generating a seed event in the dependency graph, where the seed event has a probability distribution, and propagating disruption through the dependency graph based on the seed event. The method further includes assessing loss for each of the assets, and aggregating losses for two or more assets to determine correlated risk in the network.

Abstract: Computer-implemented methods are provided herein for quantifying correlated risk in a network of a plurality of assets having at least one dependency, where each asset belongs to at least one entity. The method includes generating a dependency graph based on relationships between the assets, at least one dependency, and at least one entity, and executing a plurality of Monte Carlo simulations over the dependency graph. Executing a plurality of Monte Carlo simulations includes generating a seed event in the dependency graph, where the seed event has a probability distribution, and propagating disruption through the dependency graph based on the seed event. The method further includes assessing loss for each of the assets, and aggregating losses for two or more assets to determine correlated risk in the network.

Abstract: Computer-implemented methods are provided herein for quantifying correlated risk in a network of a plurality of assets having at least one dependency, where each asset belongs to at least one entity. The method includes generating a dependency graph based on relationships between the assets, at least one dependency, and at least one entity, and executing a plurality of Monte Carlo simulations over the dependency graph. Executing a plurality of Monte Carlo simulations includes generating a seed event in the dependency graph, where the seed event has a probability distribution, and propagating disruption through the dependency graph based on the seed event. The method further includes assessing loss for each of the assets, and aggregating losses for two or more assets to determine correlated risk in the network.

Abstract: A method includes identifying costs associated with different outcomes of a failure prediction algorithm. The algorithm is configured to predict one or more faults with at least one piece of industrial equipment. The different outcomes include both successful and unsuccessful predictions by the algorithm. The method also includes identifying a threshold value for the algorithm using the costs, where the threshold value is used by the failure prediction algorithm to identify whether maintenance of the at least one piece of industrial equipment is needed. The method further includes providing the threshold value to the algorithm. The threshold value is selected such that a net positive economic benefit is obtained from use of the threshold value with the failure prediction algorithm. In addition, the method can include generating a signal indicating whether maintenance is needed based on a comparison of an indicator value calculated using the algorithm and the threshold value.

Abstract: Computer-implemented methods are provided herein for quantifying correlated risk in a network of a plurality of assets having at least one dependency, where each asset belongs to at least one entity. The method includes generating a dependency graph based on relationships between the assets, at least one dependency, and at least one entity, and executing a plurality of Monte Carlo simulations over the dependency graph. Executing a plurality of Monte Carlo simulations includes generating a seed event in the dependency graph, where the seed event has a probability distribution, and propagating disruption through the dependency graph based on the seed event. The method further includes assessing loss for each of the assets, and aggregating losses for two or more assets to determine correlated risk in the network.

Abstract: A method includes identifying a statistical performance of a monitoring rule associated with an asset monitoring system. The monitoring rule includes logic configured to identify one or more faults with at least one asset, and the statistical performance includes an effectiveness of the monitoring rule in identifying the one or more faults. The method also includes identifying an economic performance of the monitoring rule, where the economic performance is based on costs associated with different outcomes of the monitoring rule. The method further includes updating or replacing the monitoring rule based on the economic performance.

Abstract: Computer-implemented methods are provided herein for quantifying correlated risk in a network of a plurality of assets having at least one dependency, where each asset belongs to at least one entity. The method includes generating a dependency graph based on relationships between the assets, at least one dependency, and at least one entity, and executing a plurality of Monte Carlo simulations over the dependency graph. Executing a plurality of Monte Carlo simulations includes generating a seed event in the dependency graph, where the seed event has a probability distribution, and propagating disruption through the dependency graph based on the seed event. The method further includes assessing loss for each of the assets, and aggregating losses for two or more assets to determine correlated risk in the network.

Abstract: A method includes receiving information associated with operation of multiple pieces of industrial equipment at multiple sites. The multiple pieces of industrial equipment are of a same or similar type, and the information represents different types of data including operational data associated with the multiple pieces of industrial equipment and text-based data associated with the multiple pieces of industrial equipment. The method also includes analyzing the information to identify correlations in the information associated with one or more conditions involving the pieces of industrial equipment. The method further includes, based on the analyzing, generating one or more monitoring rules or routines to be used to identify the one or more conditions. The multiple pieces of industrial equipment could include machines with rotating components, machines with heat transfer equipment, or general process equipment.

Abstract: Computer-implemented methods are provided herein for quantifying correlated risk in a network of a plurality of assets having at least one dependency, where each asset belongs to at least one entity. The method includes generating a dependency graph based on relationships between the assets, at least one dependency, and at least one entity, and executing a plurality of Monte Carlo simulations over the dependency graph. Executing a plurality of Monte Carlo simulations includes generating a seed event in the dependency graph, where the seed event has a probability distribution, and propagating disruption through the dependency graph based on the seed event. The method further includes assessing loss for each of the assets, and aggregating losses for two or more assets to determine correlated risk in the network.

Abstract: A voltage pattern analysis system and method may automate aspects of the process of mapping or assigning utility meters to a specific transformer or other distribution node by identifying misassociated meters and correcting a system-wide transformer assignment or distribution node assignment. When a meter's voltage signal does not correlate well with other meters' voltage signals on the same transformer, the meter is likely misassociated to that transformer. A pairwise voltage signal correlation may be computed for all meters assigned to a transformer and a voltage signal correlation for every transformer in the system, or a subset thereof, may be imputed. Individual meter correlations may then be compared with the transformer correlation. For meters identified as misassociation candidates, transformers or other distribution nodes within a specified radius may be considered for reassignment of the meter.

Abstract: A freeze prediction system for components such as one or more water pipes situated in a building. The building may have a heating system, a mechanism with a temperature setting connected to the heating system, and an indoor temperature indicator connected to the thermostat. A call may be made for heat if thermostat setting is greater than an indoor temperature. A check may be made as to whether a call for heat may be answered. Outdoor conditions may be read and accounted for in the system. An expected time may be calculated of a freeze danger. One may determine whether the freeze danger is significant according to the expected time of freeze danger compared to a predetermined time. If the freeze danger is not significant, a regular equipment failed alert may be sent out. If the freeze danger is significant, a pipe freeze warning alert may be sent out.

Abstract: Computer-implemented methods are provided herein for quantifying correlated risk in a network of a plurality of assets having at least one dependency, where each asset belongs to at least one entity. The method includes generating a dependency graph based on relationships between the assets, at least one dependency, and at least one entity, and executing a plurality of Monte Carlo simulations over the dependency graph. Executing a plurality of Monte Carlo simulations includes generating a seed event in the dependency graph, where the seed event has a probability distribution, and propagating disruption through the dependency graph based on the seed event. The method further includes assessing loss for each of the assets, and aggregating losses for two or more assets to determine correlated risk in the network.

Abstract: A freeze prediction system for components such as one or more water pipes situated in a building. The building may have a heating system, a mechanism with a temperature setting connected to the heating system, and an indoor temperature indicator connected to the thermostat. A call may be made for heat if thermostat setting is greater than an indoor temperature. A check may be made as to whether a call for heat may be answered. Outdoor conditions may be read and accounted for in the system. An expected time may be calculated of a freeze danger. One may determine whether the freeze danger is significant according to the expected time of freeze danger compared to a predetermined time. If the freeze danger is not significant, a regular equipment failed alert may be sent out. If the freeze danger is significant, a pipe freeze warning alert may be sent out.

Abstract: A freeze prediction system for components such as one or more water pipes situated in a building. The building may have a heating system, a mechanism with a temperature setting connected to the heating system, and an indoor temperature indicator connected to the thermostat. A call may be made for heat if thermostat setting is greater than an indoor temperature. A check may be made as to whether a call for heat may be answered. Outdoor conditions may be read and accounted for in the system. An expected time may be calculated of a freeze danger. One may determine whether the freeze danger is significant according to the expected time of freeze danger compared to a predetermined time. If the freeze danger is not significant, a regular equipment failed alert may be sent out. If the freeze danger is significant, a pipe freeze warning alert may be sent out.

Abstract: A freeze prediction system for components such as one or more water pipes situated in a building. The building may have a heating system, a mechanism with a temperature setting connected to the heating system, and an indoor temperature indicator connected to the thermostat. A call may be made for heat if thermostat setting is greater than an indoor temperature. A check may be made as to whether a call for heat may be answered. Outdoor conditions may be read and accounted for in the system. An expected time may be calculated of a freeze danger. One may determine whether the freeze danger is significant according to the expected time of freeze danger compared to a predetermined time. If the freeze danger is not significant, a regular equipment failed alert may be sent out. If the freeze danger is significant, a pipe freeze warning alert may be sent out.

Abstract: A method includes identifying a statistical performance of a monitoring rule associated with an asset monitoring system. The monitoring rule includes logic configured to identify one or more faults with at least one asset, and the statistical performance includes an effectiveness of the monitoring rule in identifying the one or more faults. The method also includes identifying an economic performance of the monitoring rule, where the economic performance is based on costs associated with different outcomes of the monitoring rule. The method further includes updating or replacing the monitoring rule based on the economic performance.

Abstract: A method includes identifying costs associated with different outcomes of a failure prediction algorithm. The algorithm is configured to predict one or more faults with at least one piece of industrial equipment. The different outcomes include both successful and unsuccessful predictions by the algorithm. The method also includes identifying a threshold value for the algorithm using the costs, where the threshold value is used by the failure prediction algorithm to identify whether maintenance of the at least one piece of industrial equipment is needed. The method further includes providing the threshold value to the algorithm. The threshold value is selected such that a net positive economic benefit is obtained from use of the threshold value with the failure prediction algorithm. In addition, the method can include generating a signal indicating whether maintenance is needed based on a comparison of an indicator value calculated using the algorithm and the threshold value.

Abstract: A voltage pattern analysis system and method may automate aspects of the process of mapping or assigning utility meters to a specific transformer or other distribution node by identifying misassociated meters and correcting a system-wide transformer assignment or distribution node assignment. When a meter's voltage signal does not correlate well with other meters' voltage signals on the same transformer, the meter is likely misassociated to that transformer. A pairwise voltage signal correlation may be computed for all meters assigned to a transformer and a voltage signal correlation for every transformer in the system, or a subset thereof, may be imputed. Individual meter correlations may then be compared with the transformer correlation. For meters identified as misassociation candidates, transformers or other distribution nodes within a specified radius may be considered for reassignment of the meter.