Provided is a prediction system that predicts whether a prescribed event will occur in a device, without being affected by differences among individual devices. The prediction system comprises: a data acquisition unit which acquires operation data representing the operation status of a device; a probability density estimation unit which estimates the probability density of the operation data; and an abnormality prediction unit which predicts whether an abnormality will occur in the device on the basis of the probability density estimation results of the operation data and a prediction model.

A computer-implemented method is provided. The computer-implemented includes a data-driven model and a robust closure model stored in a memory by using a processor for controlling a system. The computer-implemented method includes steps of acquiring sensor signals from at least one sensor of the system via an interface, computing a state of the system based on the sensor signals, determining a gain of the robust closure model based on the state of the system, reproducing a state of the system based on the determined gain, estimating a physics-based model of the system by combining the data-driven model and the robust closure model, and generating control commands by mapping the state of the system using the estimated physics-based model.

A predictive maintenance system for an irrigation system includes one or more sensors configured to generate a signal indicative of abnormal operation within the irrigation system, the sensors electrically coupled to a drive system, a processor, and a memory. The memory includes instructions stored thereon, which when executed by the processor cause the predictive maintenance system to receive the generated signal, determine abnormal operation of the drive system based on the generated signal, and predict, by a machine learning model, a maintenance requirement of the drive system based on the determined abnormal operation.

Analyzing complex systems by receiving labeled event data describing events occurring in association with a complex system, generating a first machine learning model according to the distribution of labeled event data, receiving state variable transition data describing state variable transitions occurring in association with a complex system, training a second machine learning model according to a combination of a distribution of state variable transitions and the first machine learning model, and using the second machine learning model to predict the effects of events upon state variables within the complex system according to new state variable transition and new labeled event data.

A chemical system for an operation exhibiting steady-state gain inversion is provided herein and includes a reactor configured to receive a feed stream and produce an outlet stream to form a process and a control device configured to control a process. The control device receives inputs indicative of an operational parameter and output variables and, in response to the inputs and output variables, provides a steady-state manipulated input configured to control or optimize the process. The control device includes an input disturbance model, a state estimator, a non-linear steady-state target calculator, and a regulator configured to provide a signal for adjustment of one or more inputs based on the steady-state manipulated input and associated output variables.

Methods, systems, and apparatus, including medium-encoded computer program products, for an end-to-end wireless sensor hub include: configuring a sensor hubs in an order using a sequence established by 5 a time of addition to a network. The sensor hubs is grouped into one or more groups. Sensor data captured by the one or more groups is obtained according to a current group number, wherein the sensor data is obtained from each group of the one or more groups according to a predetermined schedule.

Provided is an information processing device, etc., that provides information which is the basis for quick detection of abnormalities that occur in a device. An information processing device calculates a degree of suitability between observation information and prediction information, the observation information observed for a system suffering an effect from an certain device, the prediction information predicted in accordance with a model for a state of the system; and calculates a difference between manipulation amount to the certain device and predictive manipulation amount predicted for the manipulation amount based on the model, the difference being a difference in case that the degree satisfies a predetermined calculation condition.

A computer-implemented method is provided. The computer-implemented includes a data-driven model and a robust closure model stored in a memory by using a processor for controlling a system. The computer-implemented method includes steps of acquiring sensor signals from at least one sensor of the system via an interface, computing a state of the system based on the sensor signals, determining a gain of the robust closure model based on the state of the system, reproducing a state of the system based on the determined gain, estimating a physics-based model of the system by combining the data-driven model and the robust closure model, and generating control commands by mapping the state of the system using the estimated physics-based model.

An apparatus is provided comprising a first acquisition unit for acquiring a data set including a plurality of types of measurement data indicating a state of an object, a supplying unit for supplying, in response to the data set being input, the data set acquired by the first acquisition unit to a model that outputs a state indication value indicating classification of a state of the object, a first identification unit for identifying, when one of the state indication value is output from the model in response to one of the data set being supplied, at least one type of measurement data, among the plurality of types of measurement data, having a larger influence on the one state indication value than a reference, based on the one data set, and a display control unit for displaying the one state indication value and the at least one type of measurement data.

In an information processing device according to the present invention, a statistics estimation unit estimates a value of a state quantity by using a statistical model constructed based on values of past state quantities of a target device. A physical estimation unit estimates a value of a state quantity by using a physical model constructed based on design data of the target device. A specification unit specifies a value to be used to manage the target device from the value estimated by the statistics estimation unit and the value estimated by the physical estimation unit based on deterioration of the target device with time.