Abstract: Vehicular maintenance is predicted using real time telematics data and historical maintenance data. Different statistical models are used, and an intersecting set of results is generated. Environmental weather may also be used to further refine predictions.

Abstract: Vehicular maintenance is predicted using real time telematics data and historical maintenance data. Different statistical models are used, and an intersecting set of results is generated. Environmental weather may also be used to further refine predictions.

Abstract: Vehicular maintenance is predicted using real time telematics data and historical maintenance data. Different statistical models are used, and an intersecting set of results is generated. Environmental weather may also be used to further refine predictions.

Abstract: Vehicular maintenance is predicted using real time telematics data and historical maintenance data. Different statistical models are used, and an intersecting set of results is generated. Environmental weather may also be used to further refine predictions.

Abstract: A method and apparatus for providing equipment maintenance are disclosed. The method receives data that is captured by at least one sensor deployed at a location, wherein the data is associated with at least one parameter of an equipment, receives atmospheric data for the location, monitors the at least one parameter of the equipment and the atmospheric data for the location, determines for the at least one parameter, whether an update to a risk measure associated with the equipment is needed, when a deviation from a baseline is detected that is greater than a threshold for a maximum deviation from the baseline, performs the update to the risk measure associated with the equipment, when the update to the risk measure associated with the equipment is needed, and generates a ticket for a remedial action based on the risk measure, when the risk measure triggers the scheduling of the remedial action.

Abstract: Disclosed are systems, methods and computer-readable media for identifying failed points in a network in real time. The system and method employ a topology database against which parsed and enhanced fault notifications are compared to identify the location of the fault notifications. The fault notifications are associated into a single event. A root cause analysis module having machine learning capabilities is used to match the single event with a predicted root cause by accessing a root cause database established with existing historic data and heuristically derived failure scenarios.

Abstract: A method and apparatus for providing equipment maintenance are disclosed. The method receives data that is captured by at least one sensor deployed at a location, wherein the data is associated with at least one parameter of an equipment, receives atmospheric data for the location, monitors the at least one parameter of the equipment and the atmospheric data for the location, determines for the at least one parameter, whether an update to a risk measure associated with the equipment is needed, when a deviation from a baseline is detected that is greater than a threshold for a maximum deviation from the baseline, performs the update to the risk measure associated with the equipment, when the update to the risk measure associated with the equipment is needed, and generates a ticket for a remedial action based on the risk measure, when the risk measure triggers the scheduling of the remedial action.

Abstract: A method and apparatus for providing equipment maintenance are disclosed. The method receives data that is captured by at least one sensor deployed at a location, wherein the data is associated with at least one parameter of an equipment, receives atmospheric data for the location, monitors the at least one parameter of the equipment and the atmospheric data for the location, determines for the at least one parameter, whether an update to a risk measure associated with the equipment is needed, when a deviation from a baseline is detected that is greater than a threshold for a maximum deviation from the baseline, performs the update to the risk measure associated with the equipment, when the update to the risk measure associated with the equipment is needed, and generates a ticket for a remedial action based on the risk measure, when the risk measure triggers the scheduling of the remedial action.

Abstract: Vehicular maintenance is predicted using real time telematics data and historical maintenance data. Different statistical models are used, and an intersecting set of results is generated. Environmental weather may also be used to further refine predictions.

Abstract: Vehicular maintenance is predicted using real time telematics data and historical maintenance data. Different statistical models are used, and an intersecting set of results is generated. Environmental weather may also be used to further refine predictions.

Abstract: A method and apparatus for providing equipment maintenance are disclosed. For example, the method receives data that is captured by at least one sensor deployed at a location, wherein the data is associated with at least one parameter, receives atmospheric data for the location, monitors the at least one parameter of the equipment and the atmospheric data for the location, determines for the at least one parameter, whether an update to a risk measure associated with the equipment is needed, when a deviation from a baseline is detected that is greater than a threshold for a maximum deviation from the baseline, performs the update to the risk measure associated with the equipment, when the update to the risk measure associated with the equipment is needed, and generates a ticket for a remedial action based on the risk measure, when the risk measure triggers the scheduling of the remedial action.

Abstract: Vehicular maintenance is predicted using real time telematics data and historical maintenance data. Different statistical models are used, and an intersecting set of results is generated. Environmental weather may also be used to further refine predictions.