A system and method that automatically resolves conflicts among sensor information in a sensor fusion robot system. Such methods can accommodate converging ambiguous and divergent sensor information in a manner that can allow continued, and relatively accurate, robotic operations. The processes can include handling sensor conflict via sensor prioritization, including, but not limited, prioritization based on the particular stage or segment of the assembly operation when the conflict occurs, overriding sensor data that exceeds a threshold value, and/or prioritization based on evaluations of recent sensor performance, predictions, system configuration, and/or historical information. The processes can include responding to sensor conflicts through comparisons of the accuracy of workpiece location predictions from different sensors during different assembly stages in connection with arriving at a determination of which sensor(s) is providing accurate and reliable predictions.

Predictive maintenance methods and systems, including a method of applying transfer entropy techniques to find a causal link between parameters; a method of applying quality weighting to context data based on a priori knowledge of the accuracy of the context data; a method of detecting a maintenance action from parameter data by detecting a step and a process capability improvement; a method of managing unattended alerts by considering cost/benefit of attending to one or more alerts over time and assigning alert expiry time and/or ranking the alerts accordingly; a method of displaying components of a complex system in a functional way enabling improvements in system diagnostics; a method of determining the time of an event indicator in time series parameter data; a method of classifying an event associated with a fault condition occurring within a system; and a method of determining whether an event recorded in parameter data is attributable to an external factor.

A determiner which learns acceleration measurement data which has been obtained by an accelerated aging test and indicates that a facility changes from a normal state to an aged state, and advance label data which is obtained by giving a label to data indicating characteristics of aging in the acceleration measurement data. Measurement data of aging diagnosis is obtained from the facility which is operating, teacher aging degree label data is found from a record of maintenance of the facility, and additional data is obtained from the measurement data and the teacher aging degree label data. When a difference between predicted aging degree label data and teacher aging degree label data is greater than a predetermined value, learning data is selected as additional learning data. The additional learning data is learned to update the determiner.

A method for providing process data of a device in an industrial automation environment to a computer system. In one embodiment, the method includes the following steps: executing a process data model on the device for generating estimated process data; determining that the estimated process data deviates from the real process data by more than a threshold value; and only if the estimated process data deviates from the real process data by more than the threshold value: transmitting information representing the real process data from the device to the computer system.

A time series data processing system according to the present invention includes a learning unit configured to learn so as to generate a model that takes, of time series data measured from a measurement target, boundary period time series data that is time series data of a boundary period between a normal period and an anomalous period as an input and outputs a teaching signal determined by a preset function in accordance with change of time of the boundary period time series data. The normal period is a period in which the measurement target is determined to be in a normal state. The anomalous period is a period in which the measurement target is determined to be in an anomalous state.

Techniques for geometric aging data reduction for machine learning applications are disclosed. In some embodiments, an artificial-intelligence powered system receives a first time-series dataset that tracks at least one metric value over time. The system then generates a second time-series dataset that includes a reduced version of a first portion of the time-series dataset and a non-reduced version of a second portion of the time-series dataset. The second portion of the time-series dataset may include metric values that are more recent than the first portion of the time-series dataset. The system further trains a machine learning model using the second time-series dataset that includes the reduced version of the first portion of the time-series dataset and the non-reduced version of the second portion of the time-series dataset. The trained model may be applied to reduced and/or non-reduced data to detect multivariate anomalies and/or provide other analytic insights.

One example includes a rotary electric machine. The device includes at least one sensor. Each of the at least one sensor can be configured to provide a sensor signal in a first data format, the sensor signal providing an indication of a respective one of a plurality of operational characteristics of the rotary electric machine. The device also includes a programmable interface configured to receive the sensor signal from each of the at least one sensor and to translate the first data format associated with each of the at least one sensor into a second data format associated with a respective type of sensor corresponding to the respective at least one sensor and to provide at least one output signal in the second data format. Each of the at least one output signal can correspond to at least one of the operational characteristics.

The progress of deterioration of a manufacturing facility is prevented while keeping KPI within an allowable range. A facility diagnosis device includes a deterioration prevention mode definition storage unit that stores information including an operation control method for preventing deterioration of a manufacturing facility or a portion thereof for each of predetermined deterioration prevention modes; a KPI calculation unit that calculates a predetermined KPI by using a deterioration degree predicted for each of the deterioration prevention modes for the manufacturing facility or the portion of the manufacturing facility, and determines whether the KPI satisfies a predetermined condition; a deterioration prevention determination unit that determines a deterioration prevention mode that should be executed from a satisfaction determination result; and a control information output unit that outputs control information on the manufacturing facility or the portion of the manufacturing facility according to the deterioration prevention mode that should be executed.

Techniques are disclosed for summarizing, diagnosing, and correcting the cause of anomalous behavior in computing systems. In some embodiments, a system identifies a plurality of time series that track different metrics over time for a set of one or more computing resources. The system detects a first set of anomalies in a first time series that tracks a first metric and assigns a different respective range of time to each anomaly. The system determines whether the respective range of time assigned to an anomaly overlaps with timestamps or ranges of time associated with anomalies from one or more other time series. The system generates at least one cluster that groups metrics based on how many anomalies have respective ranges of time and/or timestamps that overlap. The system may preform, based on the cluster, one or more automated actions for diagnosing or correcting a cause of anomalous behavior.

A method of monitoring one or more manufacturing components includes clustering vibration data associated with the one or more manufacturing components to generate a plurality of clusters, determining a vibration characteristic of the one or more manufacturing components based on the plurality of clusters, comparing auxiliary data associated with the one or more manufacturing components and an auxiliary data prediction model associated with the one or more manufacturing components, and determining an auxiliary characteristic of the one or more manufacturing components based on a comparison of the auxiliary data associated and the auxiliary data prediction model. The method includes determining a state of the one or more manufacturing components based on the vibration characteristic and the auxiliary characteristic and broadcasting a notification based on the state.