A method includes receiving, by a processing device, first data indicative of a processing recipe. The method further includes receiving second data. The second data includes operational data associated with the processing recipe. The method further includes receiving third data. The third data includes historical data associated with the processing recipe. The method further includes performing analysis indicative of performance of a processing chamber based on the first, second, and third data. The method further includes causing performance of a corrective action in view of the analysis.

A deviation inclination calculation unit (22) calculates a deviation score by using evaluation data obtained from a subject apparatus as an input, in each of a plurality of outlier detection methods specifying data deviated from other data from among subject data, and calculating deviation scores indicating deviation degrees of the data specified, and calculates deviation inclination information from the deviation scores calculated. An abnormality detection unit (23) calculates, for each abnormality pattern, a similarity degree between deviation sensitivity information indicating sensitivity for each of a plurality of abnormality patterns with respect to each of the plurality of outlier detection methods, and the deviation inclination information calculated, and detects an abnormality of the subject apparatus.

The present disclosure describes a method of controlling a manufacturing system using multivariate time series, the method comprising: recording data from one or more devices in the manufacturing system; storing the recorded data in a data storage as a plurality of time series, wherein each time series has a first recorded value corresponding to a first time and a final recorded value corresponding to an end of the time series; interpolating, within a first time window, missing values in the plurality of time series using a Bayesian model, wherein the missing values fall between the first and end time of the respective time series; storing the interpolated values as prediction data in a prediction storage, wherein the interpolated values include the uncertainty of each interpolated value; loading the recorded data that fall within a second time window from the data storage; loading prediction data from the prediction storage that fall within the second time window and for which no recorded data are available; optimizing the parameters of the Bayesian model using the loaded recorded data and the prediction data; predicting, using the Bayesian model, values for each of the time series for which loaded recorded and prediction data are not available; storing the predicted values as prediction data in the prediction storage, wherein the prediction values include the uncertainty of each prediction value; and adjusting one or more of the devices that generate the recorded data based on the prediction data within the second time window.

Systems and methods for defect detection for vehicle operations, including collecting a multiple modality input data stream from a plurality of different types of vehicle sensors, extracting one or more features from the input data stream using a grid-based feature extractor, and retrieving spatial attributes of objects positioned in any of a plurality of cells of the grid-based feature extractor. One or more anomalies are detected based on residual scores generated by each of cross attention-based anomaly detection and time-series-based anomaly detection. One or more defects are identified based on a generated overall defect score determined by integrating the residual scores for the cross attention-based anomaly detection and the time-series based anomaly detection being above a predetermined defect score threshold. Operation of the vehicle is controlled based on the one or more defects identified.

Systems, methods, non-transitory computer readable media can be configured to access a plurality of sensor logs corresponding to a first machine, each sensor log spanning at least a first period; access first computer readable logs corresponding to the first machine, each computer readable log spanning at least the first period, the computer readable logs comprising a maintenance log comprising a plurality of maintenance task objects, each maintenance task object comprising a time and a maintenance task type; determine a set of statistical metrics derived from the sensor logs; determine a set of log metrics derived from the computer readable logs; and determine, using a risk model that receives the statistical metrics and log metrics as inputs, fault probabilities or risk scores indicative of one or more fault types occurring in the first machine within a second period.

The invention relates to a method of operating a mechanical seal assembly (1) having a mechanical seal (2) with a rotating sliding ring (3) and a stationary sliding ring (4), defining a sealing gap (5) between the sliding surfaces thereof, comprising the steps of: collecting operating data and/or environmental data of the mechanical seal (2), transmitting the collected operating data and/or environmental data to a digital twin (31) of the mechanical seal (2), simulating and monitoring the operation of the mechanical seal (2) using the digital twin (31) based on the transmitted operating data and/or environmental data of the mechanical seal (2), and retransmitting a simulation result and/or data of the digital twin.

Disclosed are systems, methods, and non-transitory computer-readable media for determining diagnostic coverage for achieving functional safety. A diagnostic coverage determination system employs an optimized process for efficiently determining a diagnostic coverage level of an electronic circuit. The diagnostic coverage determination system generates an optimized netlist that includes a reduced number of nodes by applying one or more node reduction techniques. The diagnostic coverage is determined based on the optimized netlist, thereby reducing the number of nodes that are injected with faults.

An industrial automation component may receive a first set of data associated with the industrial automation component, such that the industrial automation component is associated with a first industrial automation system. The industrial automation component may then receive a second set of data associated with one or more other industrial automation components, such that the one or more other industrial automation components are associated with one or more other industrial automation systems. The industrial automation component may then identify one or more similar patterns in the first set of data and the second set of data and adjust one or more operations of the industrial automation component based on the similar patterns.

Methods, computer program products and/or systems are provided that perform the following operations: obtaining digital replica models for equipment at an industrial location; receiving data feeds associated with the equipment; simulating operations of the equipment based on the digital replica models and the data feeds; predicting one or more events associated with areas within the industrial location based, at least in part, on the simulating of operations of the equipment; and determining one or more mitigation procedures based on the one or more predicted events.

Data presented in a visual image is monitored and verified. A processing component receives data from sensors and other sources, and interprets the data to generate processed data. A render component generates a visual image based on the processed data. A projector component projects the image, which is received by a splitter component that splits the light stream of the image and routes a portion of light energy to the display screen viewed by the user and another portion of the light energy to a data verification component (DVC). DVC interprets information presented in the image to determine the data being presented to the user by the image. DVC also receives the processed data from the processing component. DVC compares the data determined from the image to the processed data to determine whether they match and takes appropriate responsive action if they do not match.