This invention provides a monitoring device and the like which exhibit high capability of detecting a state related to a system even when the system allows a plurality of objects to be monitored to complexly cooperate with each other. A monitoring device 1 includes a first model generation unit which divides, of first time-series information related to an object to be monitored, second time-series information associated with each item and generates models related to the pieces of divided information, and a determination unit which by applies third time-series information to the models to determine whether a correlation is maintained and performs determination related to each item based on results of the determining.

A method and system for creating a design plan to test a product characteristic are described. One or more factors, level corresponding to the factors, and partitions for testing the product characteristic are determined. For each partition, an active matrix is generated. The product characteristic can be tested at each partition using the levels for the factors specified by the corresponding active matrix.

The present application generally pertains to scaling of a production process to produce a chemical, pharmaceutical and/or biotechnological product and/or of a production state of a respective production equipment. Particularly, there is provided a computer-implemented method of scaling a production process to produce a chemical, pharmaceutical and/or biotechnological product, the scaling being from a source scale to a target scale, wherein the production process is defined by a plurality of steps specified by one or more process parameters controlling an execution of the production process, the method comprising: (a) retrieving: parameter evolution information that describes the time evolution of the process parameter(s); a plurality of recipe templates, wherein a recipe comprises the plurality of steps defining the production process, and wherein a recipe template is a recipe in which at least one of the process parameters specifying the plurality of steps is a parameter being variable and having no predetermined value at the outset; (b) receiving: a source setup specification of a source setup to be used for executing the production process at the source scale, the source setup specification comprising the source scale value: a target setup specification of a target setup to be used for executing the production process at the target scale, the target setup specification comprising the target scale value; a source recipe defining the production process at the source scale: at least one acceptability function defining conditions for the values of the process parameter(s) at the source scale and/or at the target scale; (c) simulating the execution of the production process at the source scale using the source setup specification, the source recipe and the parameter evolution information: (d) determining, from the simulation, one or more source trajectories for the process parameters), wherein a trajectory corresponds to a time-based profile of values recordable during the simulated execution of the production process; (e) performing a target determination step comprising: selecting a recipe template pertinent to the production process out of the plurality of recipe templates; providing an input value for the at least one variable parameter in the selected recipe template; simulating the execution of the production process at the target scale using the target setup specification, the selected recipe template, the input value for the at least one variable parameter and the parameter evolution information; determining, from the simulation, one or more target trajectories for the process parameters; comparing the source trajectory(ies) and the target trajector

Disclosed herein is a computer architecture and software that is configured to modify handling of predictive models by an asset-monitoring system based on a location of an asset. In accordance with example embodiments, the asset-monitoring system may maintain data indicative of a location of interest that represents a location in which operating data from assets should be disregarded. The asset-monitoring system may determine whether an asset is within the location of interest. If so, the asset-monitoring system may disregard operating data for the asset when handling a predictive model related to the operation of the asset.

The present invention relates to a method for analyzing energy used for producing a unit of mass or volume of compressed gas (Specific Energy Consumption) in relation to a common output flow in a compressor system, said method comprising the following pot steps:—for time interval, T.sub.ref, collecting reference measured data points of common output flow F.sub.ref and energy (or power) consumption E.sub.ref (or P.sub.ref) in the compressor system;—calculating energy (or power) use as a function of the common output flow E.sub.ref (F) (or <P.sub.ref>.sub.t(F)) from the measured data points and calculating volume output as a function of the common output flow V.sub.ref(F);—calculating average energy consumed for producing a unit of mass or volume of compressed gas as a function of the common output

flow<SEC.sub.ref>.sub.t(F) from equation E.sub.ref(F)/V.sub.ref(F) (Or <P.sub.ref.sub.t)/P.sub.ref);—for time interval, T.sub.sav, collecting measured data points of common output flow F.sub.sav and energy (or power) consumption E.sub.sav (P.sub.sav) in the compressor system;—calculating energy consumed for producing a unit of mass or volume of compressed gas as a function of the common output flow <SEC.sub.sav>.sub.t(F) from equation E.sub.sav(F)/V.sub.sav (F) (or <P.sub.sav>.sub.t(F)/F say) or SEC.sub.sav(t,F) from P.sub.sav/F.sub.sav;—calculating the difference between <SEC.sub.ref>.sub.t(F) and <SEC.sub.sav>.sub.t(F) or SEC.sub.sav(t,F) over a range of common output flow F in the compressor system.

An evaluation device of an embodiment includes a storage, a data generator, a class definer, a characteristic data divider, and an evaluator. The data generator is configured to generate a set of characteristic data from both a set of first data and at least a set of second data, the at least set of second data being associated in time information with the set of first data, the set of characteristic data representing a plurality of characteristics. The characteristic data divider is configured to divide the plurality of sets of characteristic data into a plurality of groups on the basis of the plurality of classes defined by the class definer and condition of operations included in the set of first data. The evaluator is configured to evaluate a operating state using a first model defined for each of the plurality of groups divided by the characteristic data divider.

A controller for controlling a system having uncertainties in its dynamics subject to constraints on an operation of the system is provided. The controller is configured to acquire historical data of the operation of the system, and determine, for the system in a current state, a current control action transitioning a state of the system from the current state to a next state. The current control action is determined according to a robust and constraint Markov decision process (RCMDP) that uses the historical data to optimize a performance cost of the operation of the system subject to an optimization of a safety cost enforcing the constraints on the operation, wherein a state transition for each of state and action pairs in the performance cost and the safety cost is represented by a plurality of state transitions capturing the uncertainties of the dynamics of the system.

Provide an information processing apparatus including a determining unit configured to determine whether to generate a learned model that estimates a value relating to a component included in a device, based on at least one of a measurement value measured by each sensor of the device, an anomaly diagnosis result of the device, a result of controlling the device, or a condition relating to the device.

Provide an information processing apparatus including a determining unit configured to determine whether to generate a learned model that estimates a value relating to a component included in a device, based on at least one of a measurement value measured by each sensor of the device, an anomaly diagnosis result of the device, a result of controlling the device, or a condition relating to the device.

A monitoring system that monitors a monitoring-target system is disclosed. The monitoring system includes one or more storage apparatuses that store a program, and one or more processors that operate according to the program. The one or more processors determine an estimated value of a monitoring-target response variable of the monitoring-target system on a basis of measurement data included in test data of the monitoring-target system and a causal structure model of the monitoring-target system. The one or more processors decide whether an abnormality has occurred in the monitoring-target system on a basis of a result of a comparison between a measurement value of the monitoring-target response variable included in the test data, and the estimated value.