Abstract: The disclosed embodiments provide a system that performs seasonality-compensated prognostic-surveillance operations for an asset. During operation, the system obtains time-series sensor signals gathered from sensors in the asset during operation of the asset. Next, the system identifies seasonality modes in the time-series sensor signals. The system then determines frequencies and phase angles for the identified seasonality modes. Next, the system uses the determined frequencies and phase angles to filter out the seasonality modes from the time-series sensor signals to produce seasonality-compensated time-series sensor signals. The system then applies an inferential model to the seasonality-compensated time-series sensor signals to detect incipient anomalies that arise during operation of the asset. Finally, when an incipient anomaly is detected, the system generates a notification regarding the anomaly.

Abstract: An apparatus for providing an automotive preventive maintenance service includes: a vehicle information creator that creates vehicle information including vehicle diagnosis information obtained in real time while a vehicle is driven and vehicle state information showing a current state of the vehicle; a vehicle breakdown generation predictor that creates vehicle part states each composed of a preventive maintenance emergency degree and a vehicle part on the basis of the vehicle information, and predicts breakdown generation of the vehicle; an automotive repair shop recommender that recommends an automotive repair shop based on a cost or a distance on the basis of the preventive maintenance emergency degree in at least one of the vehicle part states; and a preventive maintenance service compensator that detects whether to perform a maintenance service about a corresponding vehicle part state according to the preventive maintenance emergency degree, and provides preventive maintenance service compensation.

Abstract: In an embodiment, a fleet scheduler includes a processor; and a non-transitory computer-readable storage medium storing a program to be executed by the processor, the program including instructions for: gathering data representing real-world conditions; generating and maintaining predictive models based on the gathered data; and generating a master schedule for a plurality of vehicles based on the gathered data and the predictive models.

Abstract: A method for predicting end-of-life for a component includes determining a baseline lifetime model for a component connected to a machine functional safety system. The component is part of a system with physical devices. The method includes monitoring environmental conditions and usage conditions of the component and modifying the baseline lifetime model based on the monitored environmental and usage conditions to produce a modified lifetime model for the component. The method includes tracking a lifetime progress of the component with respect to the modified lifetime model and sending an alert in response to lifetime progress of the component reaching a lifetime threshold associated with the modified lifetime model.