Certain aspects of the present disclosure provide techniques for a method, including: receiving multi-dimensional event data associated with a vehicle event; determining, based on the multi-dimensional event data, an inspection classification for the vehicle event; receiving multi-dimensional analysis data associated with the inspection classification for the vehicle event; determining, based on the multi-dimensional analysis data, a repair classification for the vehicle event; receiving multi-dimensional action data associated with the repair classification for the vehicle event; and determining, based on the multi-dimensional action data, a monitoring classification for the vehicle event.

The subject application relates to monitoring of drive by wire sensors in vehicles. Systems, methods and apparatuses of predictive maintenance of drive by wire systems of vehicles are provided. For example, a data storage device is configured in a vehicle to receive and store sensor data from at least one drive by wire system of the vehicle. During a period in which the vehicle is assumed to be operating normally, the sensor data stored in the data storage device is used to train an artificial neural network to recognize the normal patterns in the sensor data. Subsequently, using the trained artificial neural network, the data storage device determines whether operations of the at least one drive by wire system are abnormal based on the sensor data. A maintenance alert can be generated for the vehicle in response to a determination that the operations of the at least one drive by wire system are abnormal according to the artificial neural network.

A method of monitoring one or more manufacturing components includes clustering vibration data associated with the one or more manufacturing components to generate a plurality of clusters, determining a vibration characteristic of the one or more manufacturing components based on the plurality of clusters, comparing auxiliary data associated with the one or more manufacturing components and an auxiliary data prediction model associated with the one or more manufacturing components, and determining an auxiliary characteristic of the one or more manufacturing components based on a comparison of the auxiliary data associated and the auxiliary data prediction model. The method includes determining a state of the one or more manufacturing components based on the vibration characteristic and the auxiliary characteristic and broadcasting a notification based on the state.

The present invention relates to non-thermal renewable energy turbines (20,24,34, 38,40), in particular to the monitoring of turbine performance to identify a loss of performance indicative of faults or component degradation. The method involves comparison of measured power from a target turbine (20) with a predicted value for same turbine. The predicted value is calculated using the output from a plurality of other turbines (24,34,38,40) from an array and a predictive model including weightings for the other turbines (24, 34,38,40) based on the strength of correlation of their historical with historical data from the target turbine (20).

A service provision server collects and accumulates, for each vehicle, vehicle information obtained by an onboard device of the vehicle and environmental information related to a travel history of the vehicle. Next, the service provision server performs predictive diagnosis about necessity of maintenance for each vehicle, based on the accumulated vehicle information and environmental information, and generates maintenance prediction information that includes content and time of the maintenance, for each vehicle based on a result of the predictive diagnosis. Then, the service provision server predicts maintenance demand in an area where a maintenance business operator provides maintenance, based on the maintenance prediction information for a plurality of vehicles, and dynamically sets a maintenance price to be presented by the maintenance business operator, based on the predicted amount of maintenance demand and a reserve amount of maintenance resources reserved by the maintenance business operator.

The present invention is related to a system and method for scheduling and performing maintenance and/or repair on electrical equipment such as electrical panels, electrical outlets, electrical devices and the like. More particularly, the present invention relates to a system and method that allows a building supervisor/owner and/or an electrical maintenance provider to schedule what electrical equipment will be maintained and/or repaired and when that maintenance and/or repair will be conducted. The present invention will then provide a system and method to allow the electrical maintenance/repair provider to perform the maintenance and/or repair on the desired electrical equipment and provide immediate and up-to-date feedback to the system that the required maintenance and/or repair has been completed.

A method, apparatus, system, and computer program product for managing a platform. Sensor information for a platform health of the platform is received from a sensor system for the platform. The sensor information for the platform health of the platform is sent by a computer system into a machine learning model trained using historical sensor information indicating a historical platform health and historical context information corresponding to the historical sensor information in which the historical context information is for a set of operating conditions. A remaining useful life of a component in the platform is received by the computer system from the machine learning model.

A method for remaining useful life prediction includes generating parameter data related to a performance of an electro-mechanical element. The method includes generating simulated behavior data of the electro-mechanical element by executing a digital-twin simulation model based on estimated operating conditions, and generating deviation data that characterizes how the parameter data deviates from the simulated behavior data. The deviation data includes a deterministic component and a stochastic component. The method includes generating extrapolated deviation data by extrapolating the deterministic component and the stochastic component of the deviation data forward in time, calculating a remaining useful life of the electro-mechanical element in response to the extrapolated deviation data, and reporting the remaining useful life to a person associated with the vehicle.

A universal automatic test system for a digital plugboard based on imagine processing, including a digital plugboard test platform, an image acquisition and processing module, a test instrument module and a control and processing module.

A method for predicting an operating anomaly comprises steps of (i) taking an assembly comprising at least a first and a second equipment item, each equipment item comprising a first operating parameter, (ii) recording and storing measurements over time of the first parameters for the first and the second equipment items, (iii) collecting the measurements during or after the completion of at least one part of an operating cycle, (iv) processing the collected measurements to detect a possible malfunction of the first and second equipment items by establishing a coefficient of determination, (v) emitting a first notification indicating the possible malfunction and/or triggering additional steps if the first coefficient of determination is less than a first threshold, and (vi) emitting a second notification and/or adjusting the first threshold if the first coefficient of determination is greater than or equal to the first threshold.