Disclosed is a malfunction early-warning method for production logistics delivery equipment. After a sensor obtains past signal data, performing feature extraction and dimensionality reduction so as to obtain a feature vector; using a growing neural gas (GNG) algorithm to divide normal state data into different operation situations so as to obtain several cluster centers, and calculating the Euclidean distance between the feature vector and the cluster centers obtained from current operation data, so as to obtain a similarity trend; constructing a past memory matrix, using an improved particle swarm algorithm to optimize an LS-SVM regression model parameter, and calculating the residual value of the current state. Finally, combining the residual value and the similarity trend to obtain a risk coefficient, assessing the equipment state, and issuing an early warning for an equipment malfunction.

Methods and systems for identifying autonomous vehicle users are described herein. An autonomous vehicle may receive a request to transport a first user to a first destination location. While travelling along a route to the first destination location, the autonomous vehicle may receive a request to pick up a second user at a second starting location and transport the second user to a second destination location. The autonomous vehicle may travel to the second starting location when the second user is within the threshold distance of the autonomous vehicle. Upon arriving at the second starting location, the autonomous vehicle may detect whether a person approaching the vehicle is the second user by detecting a biometric identifier for the person. As a result, the second user may be allowed to enter the autonomous vehicle and/or the autonomous vehicle may begin travelling to the second destination location.

Embodiments of the disclosure provide a thermal model based on an adaptive filter bank for characterizing heat transfer of a volume of a thermal system. In one embodiment, the adaptive filter bank is used for diagnostics that provides information related to the condition of a thermal system. The diagnostics are based on an analysis of heat transfer characteristics of a dynamic representation of the thermal system. In accordance with the embodiments, thermal coefficients are generated based on an adaptive filter bank. One or more filters are applied to the thermal coefficients based on a sampling rate and one or more estimate thermal coefficient thresholds are generated based on the sampling rate. It is determined whether at least one of the thermal coefficients that is filtered satisfies at least one of the estimated thermal coefficient thresholds. Thereupon, alert information indicative of a diagnostic event is provided based on the determination.

Monitoring indoor environmental conditions is provided. Sensor data and its corresponding time stamps from is collect from a number of environmental sensors within an enclosed environment. A set of all possible states is defined for a specified time period, each state representing a range of sensor data values. A probability of the system changing from any one state to another is modeled according to a Markov chain. When a new sensor data value is received from a sensor it is compared to a last sensor data value of a previous state, and a probability of transition from the previous state to the current state is determined. If the probability of transition from the previous state to the current state is less than a predetermined threshold, an anomaly is detected, and a service request is generated.

A system for monitoring a component part of a top drive in real time comprises a top drive, one or more operating sensors coupled to the top drive, and an onboard processing transceiver coupled to the top drive and in communication with the operating sensors. The operating sensors are configured to measure operational data of the top drive during operation. The onboard processing transceiver is configured to determine a remaining life of the component part.

A state change detection unit obtains the data generation probability on the basis of the values of observation data and the value of a parameter of a prior distribution, obtains, on the basis of the data generation probability, a run length probability distribution of the time-series observation data acquired up to the current time point as a condition, and detects a change in the state of a facility on the basis of the run length probability distribution. Furthermore, an update unit updates the value of the parameter of the prior distribution using the values of the observation data, to generate the prior distribution to be used for calculating the data generation probability at a next time point.

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 for assessing, detecting, and responding to malfunctions involving components of autonomous vehicle and/or smart homes are described herein. Autonomous operation features and related components can be assessed using direct or indirect data regarding operation. Such assessment may be performed to determine the condition of components for salvage following a collision or other loss-event. To this end, the information regarding a plurality of components may be received. A component of the plurality of components may be identified for assessment. Assessment may including causing test signals to be sent to the identified component. In response to the test signal, one or more responses may be received. The received response may be compared to an expected response to determine whether the identified component is salvageable.

Methods and systems for autonomous and semi-autonomous vehicle control relating to malfunctions are disclosed. Malfunctioning sensors or software of autonomous vehicles may be identified from operating data of the vehicle, and a component maintenance requirement status associated with such malfunctioning component may be generated. Based upon such status, usage restrictions may be enacted to limit operation of the vehicle while the component is malfunctioning. This may include disabling or restricting use of certain autonomous or semi-autonomous features of the vehicle until the component is repaired or replaced. Repair may be accomplished by automatically scheduling repair of the vehicle or installing an updated or uncorrupted version of a software program, in various embodiments.

Methods and systems for assessing, detecting, and responding to malfunctions involving components of autonomous vehicles and/or smart homes are described herein. Malfunctions may be detected by receiving sensor data from a plurality of sensors. One of these sensors may be selected for assessment. An electronic device may obtain from the selected sensor a set of signals. When the set of signals includes signals that are outside of a determined range of signals associated with proper functioning for the selected sensor, it may be determined that the selected sensor is malfunctioning. In response, an action may be performed to resolve the malfunction and/or mitigate consequences of the malfunction.