A valve state grasping system, a display device and a rotary valve, a valve state grasping program, a recording medium, and a valve state grasping method that enable efficient valve system monitoring and accumulation of information. The system includes a valve V, a sensor unit, a server including a database, a terminal device including a display unit, and a system control unit. The database includes a position information unit, a history information unit, and an inference information unit. The position information unit includes unique information and pipe attachment information, and the history information unit includes at least measurement information and diagnosis information. The system control unit accumulates information of the position information unit and information of the history information unit in association with each other and outputs predetermined inference information from the inference information unit based on information of the position information unit and information of the history information unit.

Provided are an abnormality detection system and an abnormality detection method capable of performing more stable abnormality detection. An abnormality detection system that detects an abnormality of the target machine by a computer includes a communication unit configured to acquire first data from a first sensor attached to the target machine and second data from a second sensor attached to the target machine, an arithmetic unit, and a memory unit. The arithmetic unit includes an encoding unit trained to generate latent expressions including a predetermined latent expression that estimates the second data on the basis of the first data, a decoding unit trained to restore the first data from the latent expressions, and an abnormality detection unit configured to detect the abnormality of the target machine on the basis of a restoration error between the first data and the first data restored by the decoding unit.

An information processing device predicts, using a virtual plant which follows an actual plant, the occurrence of a first-type alarm in the actual plant in the case of implementation of each of a plurality of operation patterns virtually generated in relation to the operation of the actual plant by a worker. Then, the information processing device readjusts a prediction value, which serves as the basis for the prediction that the first-type alarm would be output, within a threshold value range; and performs re-prediction of the occurrence of a second-type alarm, which is different than the first-type alarm, using the virtual plant. Subsequently, based on the result of the re-prediction, the information processing device performs display control of the alarms predicted using the virtual plant.

This disclosure relates generally to a method and system for real time monitoring and forecasting of fouling of an air preheater (APH) in a thermal power plant. The system is deploying a digital replica or digital twin that works in tandem with the real APH of the thermal power plant. The system receives real-time data from one or more sources and provides real-time soft sensing of intrinsic parameters as well as that of health, fouling related parameters of APH. The system is also configured to diagnose the current class of fouling regime and the reasons behind a specific class of fouling regime of the APH. The system is also configured to be used as advisory system that alerts and recommends corrective actions in terms of either APH parameters or parameters controlled through other equipment such as selective catalytic reduction or boiler or changes in operation or design.

An example method comprises receiving historical wind turbine failure data and asset data from SCADA systems, receiving first historical sensor data, determining healthy assets of the renewable energy assets by comparing signals to known healthy operating signals, training at least one machine learning model to indicate assets that may potentially fail and to a second set of assets that are operating within a healthy threshold, receiving first current sensor data of a second time period, applying a machine learning model to the current sensor data to generate a first failure prediction a failure and generate a list of assets that are operating within a healthy threshold, comparing the first failure prediction to a trigger criteria, generating and transmitting a first alert if comparing the first failure prediction to the trigger criteria indicates a failure prediction, and updating a list of assets to perform surveillance if within a healthy threshold.

Methods and systems for fault identification and mitigation in an engine system. A state observer obtains current state information from the engine system, and a feature calculator uses data obtained from the state observer to calculate one or more feature indicators, which are monitored by a health estimator for the occurrence of a change using one or more change probability models. When the health estimator identifies a change, a fault isolator determines a component of the engine system that is subject to fault or health deterioration.

A method and a system for fatigue life prediction of additive manufactured components accounting for localized material properties. The method and the system is employed for prediction of fatigue life properties of an additive manufactured element, with a data collection step in which several data points for maximum stress vs. cycles to failure for different given processing steps of the element are collected, with a training step in which a Machine Learning system is trained with the collected data, and with an evaluation step in which the trained Machine Learning system is confronted with actual processing steps and used to predict the fatigue life properties of the element.

A computer-implemented method and system is provided. The system adaptively switches prediction strategies to optimize time-variant energy demand and consumption of built environments associated with renewable energy sources. The system analyzes a first, second, third, fourth and a fifth set of statistical data. The system derives of a set of prediction strategies for controlled and directional execution of analysis and evaluation of a set of predictions for optimum usage and operation of the plurality of energy consuming devices. The system monitors a set of factors corresponding to the set of prediction strategies and switches a prediction strategy from the set of derived prediction strategies. The system predicts a set of predictions for identification of a potential future time-variant energy demand and consumption and predicts a set of predictions. The system manipulates an operational state of the plurality of energy consuming devices and the plurality of energy storage and supply means.

An unsteadiness detection device (30) is provided which is capable of detecting the operation state of facilities using binary digital signals, the unsteadiness detection device including: a model generation unit (313) to generate a normal model for determining operation states of a plurality of facilities (11) on the basis of operation data which are binary digital signals obtained from the facilities (11) in their steady operation states; an expectation value calculation unit (315) to calculate an expectation value of operation data by applying the normal model to past operation data of the facilities (11); and an unsteadiness detection unit (316) to detect whether or not an operation state of one of the facilities (11) is unsteady by comparing the expectation value of the operation data and a measured value of the operation data.

An industrial safety zone configuration system leverages a digital twin of an industrial automation system to assist in configuring safety sensors for accurate monitoring of a desired detection zone. The system renders a graphical representation of the automation system based on the digital twin and allows a user to define a desired detection zone to be monitored as a three-dimensional volume within the virtual industrial environment. Users can define the locations and orientations of respective safety sensors as sensor objects that can be added to the graphical representation. Each sensor object has a set of object attributes representing configuration settings available on the corresponding physical sensor. The system can identify sensor configuration settings that will yield an estimated detection zone that closely conforms to the defined detection zone, and generate sensor configuration data based on these settings that can be used to configure the physical safety sensors.