Context-awareness in preventative maintenance is provided by receiving sensor data from a plurality of monitored systems; extracting a first plurality of features from a set of work orders for the monitored systems, wherein individual work orders include a root cause analysis for a context in which a nonconformance in an indicated monitored system occurred; predicting, via a machine learning model, a nonconformance likelihood for each monitored system based on the first plurality of features; selecting a subset of alerts based on predicted nonconformance likelihoods for the monitored systems; in response to receiving a user selection from the first set of alerts and a reason for the user selection, recording the reason as a modifier for the machine learning model; and updating the machine learning model to predict the subsequent nonconformance likelihoods using a second plurality of features that excludes the additional feature identified from the first plurality of features.

An event chain reaction system (128) is disclosed. The event chain reaction system (128) comprises: —at least one communication interface (156) configured for receiving at least one event stream (160), wherein the event stream (160) comprises at least one sequence of ordered events generated by at least one analytical system (112), wherein each event comprises information about a change in a state of the analytical system (112) and/or any of loaded resources; —at least one chain reaction component (158) comprising at least one chain matching element (162), wherein the chain matching element (162) is configured for recognizing at least one chain on the event stream (160), wherein the chain comprises a set of ordered events to be searched, wherein a first event of the chain defines a start event (166), wherein the chain matching element (162) is configured for identifying the start event (166) in the event stream (160) and, upon identifying the start event (166), the chain matching element (162) is configured for successively determining whether the other events of the chain match to one of the events of the event stream (160), wherein, in case all events of the chain are matched to events of the event stream (160), the chain matching element (162) is configured for triggering at least one reaction, wherein the reaction comprises generating information that a chain was matched and/or issuing a command to at least one component of the analytical system (112), wherein, in case one of the events of the chain is not matched, the chain matching element (162) is configured for resetting to its initial state and waiting for the start event (166). Further, a system (110) for monitoring and/or controlling, a computer implemented method and a computer program for determining at least one feature of at least one component of an analytical system (112), a computer implemented method and a computer program for monitoring and/or controlling at least one feature of at least one component of an analytical system (112) are disclosed.

Assistance device for automatically generating training data of a time series of sensor data, further on called temporal sensor data, applied to train an Artificial Intelligence system used for detecting anomalous behavior of a technical system, including a processor configured to perform - obtaining historical temporal sensor data, dividing the historical temporal sensor data into a temporal sequence of segments and assigning one segment type out of several different segment types to each segment, iteratively for each segment, determining a neighborhood pattern of segment types, determining the most frequently occurring neighborhood pattern from all determined neighborhood patterns as reference pattern for normal operation of the technical system, -selecting a subsequence of segments out of the historical temporal sensor data, which is ordered according to the reference pattern, and - outputting the subsequence of segments for applying as training data.

In an information processing apparatus, an internal variable holding unit holds a smaller number of values of an internal variable than N, the internal variable being sequentially calculated in time series based on sensor data acquired by a sensor data acquisition unit. An internal variable calculation unit calculates the internal variable corresponding to a (j+1)-th time point (j is an integer of one to N−1) based on the sensor data at the (j+1)-th time point and the internal variable corresponding to a j-th time point. A feature calculation unit calculates a feature by extracting a statistical characteristic included in the sensor data from the first time point to the N-th time point based on the internal variable at the N-th time point. A state diagnosis unit makes a diagnosis of a state of the mechanical apparatus based on the feature.

Methods and systems for autonomous vehicle recharging or refueling are disclosed. Autonomous electric vehicles may be automatically recharged by routing the vehicles to available charging stations when not in operation, according to methods described herein. A charge level of the battery of an autonomous electric vehicle may be monitored until it reaches a recharging threshold, at which point an on-board computer may generate a predicted use profile for the vehicle. Based upon the predicted use profile, a time and location for the vehicle to recharge may be determined. In some embodiments, the vehicle may be controlled to automatically travel to a charging station, recharge the battery, and return to its starting location in order to recharge when not in use.

Methods and systems autonomously parking and retrieving vehicles are disclosed. Available parking spaces or parking facilities may be identified, and the vehicle may be navigated to an available space from a drop-off location without passengers. Special-purpose sensors, GPS data, or wireless signal triangulation may be used to identify vehicles and available parking spots. Upon a user request or a prediction of upcoming user demand, the vehicle may be retrieved autonomously from a parking space. Other vehicles may be autonomously moved to facilitate parking or retrieval.

Systems, methods, and processor-readable storage media for AI-assisted electrical power grid fault analysis predict the cause of a fault and cause the fault to be remedied by receiving an indication that a first fault has occurred, identifying a plurality of additional fault records associated with the first fault, obtaining a first prediction of the cause of the fault based on the first fault record and the plurality of fault records by applying the fault records to a machine learning model, obtaining a second prediction of the cause of the fault by applying the fault records to a rules-based model, and obtaining a final prediction of the cause of the first fault based on the first prediction and the second prediction. The final prediction of the cause of the first fault is used to cause the predicted cause of the first fault to be remedied.

Methods and systems for assessing, detecting, and responding to malfunctions involving components of autonomous vehicles and/or smart homes are described herein. Autonomous operation features and related components can be assessed using direct or indirect data regarding operation. Vehicle collision and/or smart home incident monitoring, damage detection, and responses are also described, with particular focus on the particular challenges associated with incident response for unoccupied vehicles and/or smart homes. Operating data associated with the autonomous vehicle and/or smart home may be received. Within the operating, an unusual condition indicative of a likelihood of incident may be detected. Based on the unusual condition, it may be determined that the incident occurred. Accordingly, a response to the incident may be determined. The response may be implemented by the autonomous vehicle and/or smart home.

Methods and systems autonomously parking and retrieving vehicles are disclosed. Available parking spaces or parking facilities may be identified, and the vehicle may be navigated to an available space from a drop-off location without passengers. Special-purpose sensors, GPS data, or wireless signal triangulation may be used to identify vehicles and available parking spots. Upon a user request or a prediction of upcoming user demand, the vehicle may be retrieved autonomously from a parking space. Other vehicles may be autonomously moved to facilitate parking or retrieval.

Methods and systems for assessing, detecting, and responding to malfunctions involving components of autonomous vehicles and/or smart homes are described herein. Autonomous operation features and related components can be assessed using direct or indirect data regarding operation. Vehicle collision and/or smart home incident monitoring, damage detection, and responses are also described, with particular focus on the particular challenges associated with incident response for unoccupied vehicles and/or smart homes. Operating data associated with the autonomous vehicle and/or smart home may be received. Within the operating, an unusual condition indicative of a likelihood of incident may be detected. Based on the unusual condition, it may be determined that the incident occurred. Accordingly, a response to the incident may be determined. The response may be implemented by the autonomous vehicle and/or smart home.