A computer-implemented method and system is provided. The system adaptively switches prediction strategies to optimize time-variant energy demand and consumption of built environments associated with renewable energy sources. The system analyzes a first, second, third, fourth and a fifth set of statistical data. The system derives of a set of prediction strategies for controlled and directional execution of analysis and evaluation of a set of predictions for optimum usage and operation of the plurality of energy consuming devices. The system monitors a set of factors corresponding to the set of prediction strategies and switches a prediction strategy from the set of derived prediction strategies. The system predicts a set of predictions for identification of a potential future time-variant energy demand and consumption and predicts a set of predictions. The system manipulates an operational state of the plurality of energy consuming devices and the plurality of energy storage and supply means.

Systems, apparatuses and methods may provide for technology that obtains categorization information and corresponding uncertainty information from a perception subsystem, wherein the categorization information and the corresponding uncertainty information are to be associated with an object in an environment. The technology may also determine whether the corresponding uncertainty information satisfies one or more relevance criteria, and automatically control the perception subsystem to increase an accuracy in one or more subsequent categorizations of the object if the corresponding uncertainty information satisfies the one or more relevance criteria. In one example, determining whether the corresponding uncertainty information satisfies the relevance criteria includes taking a plurality of samples from the categorization information and the corresponding uncertainty information, generating a plurality of actuation plans based on the plurality of samples, and determining a safety deviation across the plurality of actuation plans, wherein the relevance criteria are satisfied if the safety deviation exceeds a threshold.

This application provides methods and systems for automated condition-based maintenance of mechanical systems. Example systems may at least one memory coupled to one or more computer processors that are configured to receive first data from the mechanical system indicative of performance of a first component of the mechanical system, determine, using the first data, a first performance metric for the first component, determine, using the first performance metric, a probability value that a fault has occurred at the first component, and determine, using the probability value, a predicted length of time until failure of the first component.

The present disclosure proposes a diagnostic system capable of properly identifying the cause of even an error for which multiple factors or multiple compound factors may be accountable. The diagnostic system according to the present disclosure is provided with a learning device for learning at least one of a recipe defining operations of an inspection device, log data describing states of the device, or specimen data describing characteristics of a specimen in association with error types of the device, and estimates the cause of the error by using the learning device (refer to FIG. 4).

A method of configuring robot fleets with additive manufacturing capabilities includes receiving a request for a robotic fleet to perform a job and determining a job definition data structure based on the request. The job definition data structure defines a set of tasks to be performed in furtherance of the job. The method includes determining a provisioning configuration for each additive manufacturing system based on the task to which the additive manufacturing system is assigned, the set of 3D printing requirements, the printing instructions, and the status of the additive manufacturing system. The method includes provisioning the additive manufacturing system based on the provisioning configuration and a set of additive manufacturing system provisioning rules that are accessible to an intelligence layer to ensure that provisioned systems comply with the provisioning rules. The method includes deploying the robotic fleet based on the robotic fleet configuration data structure to perform the job.

A method for controlling a wind turbine includes receiving operational data of at least one component of the wind turbine. The operational data includes a time-series of one or more high speed signals both before, during, and after an anomaly. Further, the high speed signal(s) may be digital or analog signals. The method also includes storing the operational data. Moreover, the method includes analyzing the stored operational data to identify a specific type and location of the anomaly using at least one of pattern recognition, machine learning, or rules-based conditions. In addition, the method includes determining an appropriate response action for the specific type and location of the anomaly. Further, the method includes adjusting a control parameter of the wind turbine. Thus, the method includes implementing the appropriate response action for the specific type and location of the anomaly.

A determination device includes: a memory; and a processor coupled to the memory and configured to: obtain sensor data on motion of a device from a plurality of sensors, extract, from the sensor data, data related to an anomaly based on a threshold value used in detecting the anomaly with use of the sensor data, convert the data related to the anomaly into structural data having a graph structure focusing on an analogous relationship between or among the plurality of sensors, and generate a classifier that identifies a cause of the anomaly with use of the structural data.

A diagnosis device for diagnosing a plant based on an operating state of the plant includes a monitoring data acquisition unit configured to acquire a plurality of monitoring data which are measurement values of a parameter related to the operating state of the plant measured at different times, a diagnosis target pattern generation unit configured to generate a diagnosis target pattern that is a plot pattern where each of the plurality of monitoring data is plotted against plant output data of the plant, and a pattern diagnosis unit configured to diagnose the plant based on the plot pattern of the diagnosis target pattern.

An apparatus for monitoring an actuator system, a method for providing an apparatus for monitoring an actuator system, and a method for monitoring an actuator system where the has at least one actuator and at least one data output signal. An anomaly detector detects anomalies. A suppressing engine determines time periods in which a control intervention has been performed. In a resulting monitoring signal, only anomalies are indicated which do not overlap with time periods in which the control intervention has been performed resulting in less irrelevant alerts and false positives output to a human supervisor monitoring the actuator system. The apparatus for monitoring a system may be provided with a plurality of actuators that may affect one another over time. The apparatus may be applied to a system of submersible pumps, or a system of conveyor belts.

Methods and systems for calculating a probability of failure and/or estimating a lifetime of an equipment component are disclosure. In an embodiment, a method of calculating a probability of failure of an equipment component comprises: generating a finite element model of the equipment component using device properties of the equipment component; using the finite element model of the equipment component to construct a polynomial basis for a polynomial chaos expansion; calculating expansion coefficients for the polynomial chaos expansion which express creep stress and strain in the equipment component as a function of operating parameters of the equipment component; receiving measured operating parameter values for the equipment component; and calculating a probability of failure of the equipment component using the measured operating parameter values.