Methods, systems, and computer-readable storage media for receiving, by an anomalous operation detection service, current signal data representing a driving current applied to a device over a time period, processing, by an anomalous operation detection service, the current signal data through a deep neural network (DNN) module, a frequency spectrum analysis (FSA) module, and a time series classifier (TSC) module to provide a set of indications, each indication in the set of indications indicating one of normal operation of the device and anomalous operation of the device, processing, by an anomalous operation detection service, the set of indications through a voting gate to provide an output indication, the output indication indicating one of normal operation of the device and anomalous operation of the device, and selectively transmitting one or more of an alert and a message based on the output indication.

A system for monitoring a plurality of machines of a facility is disclosed. The system comprises a plurality of data network devices configured to communicate with one another via at least one network. The data network devices are configured to collect machine data from the plurality of machines and distribute the machine data via the at least one network. The system further comprises data display devices configured to provide a graphical user interface that enables a user to view and analyze the machine data that is collected and distributed by the data network devices.

An electronic device including an application processor and a communication processor. The communication processor including a resource memory, the communication processor configured to monitor an occupancy rate of the resource memory, determine whether the electronic device is in an idle state, forcibly release a network connection, clear the resource memory, and reconnect the network connection.

A method and/or system for automated detection and automated remediation of anomalies in Robotic Process Automation (RPA) environment is disclosed. The method comprises auto discovering resources (RPA components and its dependencies) in an RPA platform. The discovered resources are monitored though observation metrics whose values are obtained by executing pre-defined scripts. The obtained values are validated against threshold values to determine if there are any anomalies, wherein the threshold values may either be static values or dynamic values. If there is a breach of threshold, a remediation plan is automatically executed causing the remediation of anomalies. The system is trained to determine the dynamic threshold values through machine learning models which are developed and trained through metrics data and by determining error patterns from the historic unstructured log data.

A computer-implemented method for automating service provider status and reporting during a service visit includes the initial steps of creating a service provider transaction, initiating the transaction, and calculating an estimated completion time of the transaction. The estimated completion time is based on at least one service condition, which may include the availability of servicing tools and components, the availability of service provider employees, the priority status, if any, of the service provider transaction, and the level of difficulty of service provider transaction, among others. Preferably, the service conditions include constant or variable associated values. The completion time is calculated based upon a sum of these values. If an unexpected service need or service delay arises, the service provider transaction status is updated, which includes recalculating the estimated time of completion based on a new service condition that arose from the unexpected need or delay. When the service provider transaction is complete, the customer reviews the transaction, confirms that the service provider transaction is complete, and schedules a service completion event.

The disclosed embodiments relate to a system that automatically selects a prognostic-surveillance technique to analyze a set of time-series signals. During operation, the system receives the set of time-series signals obtained from sensors in a monitored system. Next, the system determines whether the set of time-series signals is univariate or multivariate. When the set of time-series signals is multivariate, the system determines if there exist cross-correlations among signals in the set of time-series signals. If so, the system performs subsequent prognostic-surveillance operations by analyzing the cross-correlations. Otherwise, if the set of time-series signals is univariate, the system performs subsequent prognostic-surveillance operations by analyzing serial correlations for the univariate time-series signal.

Requests for computing resources and other resources can be predicted and managed. For example, a system can determine a baseline prediction indicating a number of requests for an object over a future time-period. The system can then execute a first model to generate a first set of values based on seasonality in the baseline prediction, a second model to generate a second set of values based on short-term trends in the baseline prediction, and a third model to generate a third set of values based on the baseline prediction. The system can select a most accurate model from among the three models and generate an output prediction by applying the set of values output by the most accurate model to the baseline prediction. Based on the output prediction, the system can cause an adjustment to be made to a provisioning process for the object.

Methods, systems, and non-transitory computer readable medium are described for sensor metrology data integration. A method includes receiving sets of sensor data and sets of metrology data. Each set of sensor data includes corresponding sensor values associated with producing corresponding product by manufacturing equipment and a corresponding sensor data identifier. Each set of metrology data includes corresponding metrology values associated with the corresponding product manufactured by the manufacturing equipment and a corresponding metrology data identifier. The method further includes determining common portions between each corresponding sensor data identifier and each corresponding metrology data identifier. The method further includes, for each of the sensor-metrology matches, generating a corresponding set of aggregated sensor-metrology data and storing the sets of aggregated sensor-metrology data to train a machine learning model. The trained machine learning model is capable of generating one or more outputs for performing a corrective action associated with the manufacturing equipment.

A processor includes an execution pipeline that includes a plurality of execution stages, execution pipeline control logic, and a debug system. The execution pipeline control logic is configured to control flow of an instruction through the execution stages. The debug system includes a debug pipeline and debug pipeline control logic. The debug pipeline includes a plurality of debug stages. Each debug pipeline stage corresponds to an execution pipeline stage, and the total number of debug stages corresponds to the total number of execution stages. The debug pipeline control logic is coupled to the execution pipeline control logic. The debug pipeline control logic is configured to control flow through the debug stages of debug information associated with the instruction, and to advance the debug information into a next of the debug stages in correspondence with the execution pipeline control logic advancing the instruction into a corresponding stage of the execution pipeline.

A computing system is configured to derive insights related to asset operation and present these insights via a GUI. To these ends, the computing system (a) receives data related to the operation of assets, (b) based on this data, derives a plurality of insights related to the operation of at least a subset of the assets, (c) from the insights, defines a given subset of insights to be presented to a user, (d) defines at least one aggregated insight representative of one or more individual insights in the given subset of insights that are related to a common underlying problem, and (e) causes the user's client station to display a visualization of the given subset of insights including (i) an insights pane that provides a high-level overview of the subset of insights and (ii) a details pane that provides additional details regarding a selected one of the subset of insights.