Aspects of the disclosure relate to computing platforms that utilize improved techniques for dynamic event verification. A computing platform may receive first source data comprising driving data associated with a vehicle over a time period. Based on the first source data, the computing device may determine that the vehicle experienced an event, resulting in an event output. In response to determining the event output, the computing device may generate a request for second source data associated with the vehicle over the time period. The computing device may receive, from a sensor device, the second source data. Based on a comparison of the first source data to the second source data, the computing platform may determine an event comparison output. The computing platform may determine that the event comparison output exceeds a predetermined comparison threshold, and may send an indication of an event in response.

The present disclosure relates to systems and methods for detecting anomalies in a semiconductor processing system. According to certain embodiments, one or more external sensors are mounted to a sub-fab component, communicating with the processing system via a communication channel different than a communication channel utilized by the sub-fab component and providing extrinsic sensor data that the sub-fab component is not configured to provide. The extrinsic sensor data may be combined with sensor data from a processing tool of the system and/or intrinsic sensor data of the sub-fab component to form virtual sensor data. In the event the virtual data exceeds or falls below a threshold, an intervention or a maintenance signal is dispatched, and in certain embodiments, an intervention or maintenance action is taken by the system.

An abnormality diagnosis method for diagnosing an abnormality in equipment includes acquiring multivariate time-series data for a plurality of measurement items from the equipment, diagnosing an abnormality in operational state of the equipment based on the multivariate time-series data, and diagnosing a cause of the abnormality. The diagnosing a cause of the abnormality includes extracting a feature of a first section before the occurrence of the abnormality from the multivariate time-series data of the first section, extracting a feature of a second section after the occurrence of the abnormality from the multivariate time-series data of the second section, obtaining an amount of change in feature from a difference between the feature of the first section and the feature of the second section, and diagnosing a measurement item that is the cause of the abnormality based on the amounts of change in features of the plurality of measurement items.

An apparatus for connecting and monitoring processes of a production machine includes a process monitoring device designed to capture sensor data relating to the production machine and process data relating to the production machine, and a connection device designed to capture control data relating to the production machine. The apparatus is designed to change a hardware function of the apparatus based on the sensor data, the process data, and the control data. The process monitoring device may be designed to carry out data processing for the sensor data and the process data, or the connection device may be designed to carry out data processing of the control data. The apparatus may include a field-programmable logic gate array device designed to change the hardware function.

A system and method for operating measuring devices in a process plant includes providing at least one measuring device for detecting at least one measuring variable, the measuring device having comprises at least one memory means for storing actual configuration data; providing at least one controller unit having at least one additional memory means in which projected configuration data is stored; the controller unit does not have write access to the measuring device with respect to the actual configuration data; and wherein the at least one measuring device and the at least one controller unit are connected via at least one network; comparing the actual configuration data and the projected configuration data, and when the configuration data correspond, allowing a data communication.

A predictive maintenance system and method for intelligent manufacturing equipment is provided. The predictive maintenance system includes a first-stage predictive maintenance module, a second-stage predictive maintenance module, and a maintenance decision module. The first-stage predictive maintenance module includes an acquisition module, a human-computer interaction module, a calculation module, and a storage module. The second-stage predictive maintenance module includes a communication module, a setting module, and a prediction module. The maintenance decision module is configured to receive a first-stage remaining service life calculated by the first-stage predictive maintenance module and a second-stage remaining service life predicted by the second-stage predictive maintenance module, and determine a predictive maintenance strategy of the intelligent manufacturing equipment according to the first-stage remaining service life and the second-stage remaining service life. The present disclosure may reduce unexpected shutdown, reduce the costs of operation and maintenance, and improve the efficiency of operation and maintenance.

In sensor data analytics, physics-based models generate high quality data. However, these models consume lot of time as they rely on physical simulations. On the other hand, generative learning takes much less time to generate data, and may be prone to error. Present disclosure provides system and method for generation of synthetic machine data for healthy and abnormal condition using hybrid of physics based and generative model-based approach. Finite Element Analysis (FEA) is used for simulating healthy and faulty parts in machinery with set of parameters and pre-condition(s). Small output data from FEA is fed into a generative model for generating synthesized data by learning data distribution knowledge and representing into latent space. Rule engine is built using statistical features wherein realistic bounds serve as faulty data indicators. Synthesized data which does not satisfies features bounds are discarded. Further, AI-based validation framework is used to analyze quality of synthesized data.

The present invention provides a machine abnormality marking and abnormality prediction system connected with a factory host and including a parameter streaming unit connected with the machines, an abnormality reporting unit, a prediction analysis unit, and a neural network classifier. The parameter streaming unit and the abnormal reporting unit collect data of each machine in the factory, and the collected data are compared and analyzed with historical records by the prediction analysis unit. The generated parameter values can be continuously compared with the collected data to predict the state of each machine in the factory, and provide an early warning of possible abnormality or need of maintenance, so that the personnel in the factory can arrange production line maintenance or capacity adjustment in advance or adjust the machine of the factory production line, to avoid occasional shutdown and reduce factory losses.

An apparatus for detecting a fault state of an aircraft is provided. The apparatus accesses a training set of flight data for the aircraft. The training set includes observations of the flight data, each observation of the flight data includes measurements of properties selected and transformed into a set of features. The apparatus builds a generative adversarial network including a generative model and a discriminative model using the training set and the set of features, and builds an anomaly detection model to predict the fault state of the aircraft. The anomaly detection model is trained using the training set of flight data, simulated flight data generated by the generative model, and a subset of features from the set of features. The apparatus deploys the anomaly detection model to predict the fault state of the aircraft using additional observations of the flight data.

A method for derivation of a machine signature includes receiving sensor information from a primary sensor, where the primary sensor is positioned to receive information from a portion of an industrial operation, and receiving sensor information from one or more secondary sensors. The secondary sensors are arranged to provide additional information about the industrial operation indicative of current operating conditions of the industrial operation. The method includes using the sensor information from the secondary sensors and machine learning to determine if the portion of the industrial operation is operating in a normal condition and, in response to determining that the portion of the industrial operation is operating normally, using sensor information from the primary sensor during the normal operating condition to derive a primary sensor signature for the sensor information from the primary sensor.