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.

An abnormality predicting system includes a processor and a memory having instructions. The instructions, when executed by the at least one processor, cause the at least one processor to execute operations including: inputting processing target data acquired from a target device; storing information related to an abnormality prediction of the processing target data; calculating an abnormality degree of the processing target data; executing processing related to the abnormality prediction including a failure occurrence prediction using a latest abnormality degree transition and a past abnormality degree transition of the processing target data; and generating a display screen for displaying a processing result including an abnormality degree transition and a result of the failure occurrence prediction.

A unit space update device, which generates a unit space, includes: a detection value acquisition unit which acquires a bundle of detection values detected at a constant cycle; and a deletion processing unit which deletes one bundle from the bundles of the detection values constituting the unit space, when adding the bundle of the detection values to the unit space. The deletion processing unit includes at least one among: a first processing unit which randomly deletes one bundle from the bundles of the detection values forming the unit space; and a second processing unit which, with respect to the detection value of the predetermined evaluation item among the plurality of evaluation items, deletes any one bundle of the bundles including the detection value with the highest appearance frequency in the unit space, or one of two bundles having the smallest difference between the detected values in the unit space.

A method is described herein, comprising registering an event at a first processing unit of a processing facility comprising a plurality of processing units, using a coincidence probability array and an event probability to identify a second processing unit of the plurality of processing units based on the event, determining whether the second processing unit experienced a coincident event, if the second processing unit experienced a coincident event, remediating a condition of the second processing unit that caused the coincident event, and updating the coincidence probability array based on the event.

Systems, methods, and other embodiments associated with long-term predictions for specific maintenance within a planned maintenance schedule. In one embodiment, a method includes updating a failure probability curve for a component part of a device based at least in part on data obtained from a sensor associated with the component part; determining based at least in part on the updated failure probability curve that a likelihood of failure for the component part following a first upcoming planned maintenance and before a second upcoming planned maintenance exceeds a threshold; and transmitting a work order for specific maintenance to reduce the likelihood of failure of the component part to be performed during the first upcoming planned maintenance.

A computer-implemented method for automating the development of industrial machine learning applications includes one or more sub-methods that, depending on the industrial machine learning problem, may be executed iteratively. These sub-methods include at least one of a method to automate the data cleaning in training and later application of machine learning models, a method to label time series (in particular signal data) with help of other timestamp records, feature engineering with the help of process mining, and automated hyper-parameter tuning for data segmentation and classification.

A method includes training a first control model by utilizing a first set of input data as first input, resulting in a trained first control model; copying the trained first control model to a second control model, wherein, after copying, the second input layer and the plurality of second hidden layers is identical to the plurality of first hidden layers, and the first output layer is replaced by the second output layer; freezing the plurality of second hidden layers; training the second control model by utilizing the first set of input data as second input, resulting in a trained second control model; and running the trained second control model by utilizing a second set of input data as second input, wherein the second output outputs the quality measure of the first control model.

The invention relates to a system for monitoring and controlling a dynamic network such as an oil, gas, or water pipeline. The system includes a plurality of sensors for measuring aspects of a state of the network with each sensor being associated with a segment of the network and connected to a virtual sensor which accumulates and pre-processes measurements from the sensors for each segment of the network. The system further includes a network topology processor for storing the topology of the network and relating sensors and virtual sensors to segments of the network and neighbouring sensors and virtual sensors in accordance with the topology and a reinforcement learning artificial neural network (ANN) based nonlinear state estimation and predictive control model which uses measurements from the sensors and virtual sensors to model the state of the network and estimate sequential states of the network.

The disclosed embodiments relate to a system that automatically adapts a prognostic-surveillance system to account for aging phenomena in a monitored system. During operation, the prognostic-surveillance system is operated in a surveillance mode, wherein a trained inferential model is used to analyze time-series signals from the monitored system to detect incipient anomalies. During the surveillance mode, the system periodically calculates a reward/cost metric associated with updating the trained inferential model. When the reward/cost metric exceeds a threshold, the system swaps the trained inferential model with an updated inferential model, which is trained to account for aging phenomena in the monitored system.

An abnormal irregularity cause identifying device includes a process data acquisition unit that reads process data output by sensors included in a production facility performing a batch stage and a continuous stage, a preprocessing unit that associates a range of a complete timing of the batch stage with an output timing of process data of the process data in the continuous stage based on a residence time of the processing target in the production facility, an abnormality determination unit that calculates an abnormality degree by using process data in the batch stage and process data in the continuous stage associated with each other by the preprocessing unit, and a cause diagnosis unit that determines, for each of the process data output by the corresponding one of the plurality of sensors, whether the abnormality degree calculated by the abnormality determination unit satisfies a predetermined criterion.