A system includes a power generation system and a controller that controls the power generation system. The controller includes a processor that generates a model of the power generation system that estimates a value for a first parameter of the power generation system. The processor also receives a measured value of the first parameter. The processor further adjusts a correction factor of the model such that the estimated value of the first parameter output by the model is approximately equal to the measured value of the first parameter. The processor also generates a transfer function that represents the correction factor as a function of a second parameter of the power generation system. The processor further displays the transfer function along with one or more previously generated transfer functions to quantify degradation of the power generation system.

In a component mounting system, recovery processing is repeated until a recovery count number Nr is larger than or equal to a defined count number Nth in a case where a pickup defect of a component occurs, an elapsed time is measured from error stoppage of a component mounting machine to canceling of the error stoppage in which the component mounting machine is error-stopped when the recovery count number Nr is larger than or equal to the defined count number Nth, the defined count number Nth is increased within a range in which the defined count number does not exceed the upper limit value Nmax in a case where the elapsed time is shorter than a defined time Tth, and the defined count number Nth returns to an initial value in a case where the elapsed time is longer than or equal to the defined time Tth.

A method for determining a dynamic response of a machine having at least one axis, including performing a measurement run for each axis of the machine over an entire work area of each respective axis, capturing and recording data associated with each measurement run, determining a time-frequency representation of recorded data using a data processing unit, and analyzing the time-frequency representation or a related representation using an image processing algorithm.

A fault prediction system includes a machine learning device that learns conditions associated with a fault of an industrial machine. The machine learning device includes a state observation unit that, while the industrial machine is in operation or at rest, observes a state variable including, e.g., data output from a sensor, internal data of control software, or computational data obtained based on these data, a determination data obtaining unit that obtains determination data used to determine whether a fault has occurred in the industrial machine or the degree of fault, and a learning unit that learns the conditions associated with the fault of the industrial machine in accordance with a training data set generated based on a combination of the state variable and the determination data.

Provided are a computer system, and a method and a program for operation verification that easily know the operation of a machine tool more exactly. The computer system acquires operating data while a machine tool operates for a predetermined time, generates computer graphics virtually showing that the machine tool operates for the predetermined time from the acquired data, acquires an image of the machine tool that has taken for the predetermined time, and compares the image with the computer graphics for the predetermined time. The computer system also notifies the abnormality if an abnormality has been detected as the result of the comparison.

An evaluation apparatus includes a storage unit that stores a model modeling a state of a facility provided in a plant, a simulator that adjusts a parameter that is set in the model so that a difference between an actual measurement value based on a process value of the facility in a first state and a first simulate value calculated by using the model is equal to or less than a threshold, and an estimation unit that estimates a first estimated operating point that indicates an operation state of the facility in the first state based on the adjusted parameter.

A monitoring apparatus for the identification of anomalies and degradation paths in a machine tool is disclosed. The monitoring apparatus includes a control system interfaceable with a machine tool and configured for making the machine tool execute a predetermined cycle of operations; a recording system interfaceable with a plurality of sensors in the machine tool, wherein the recording system is configured for collecting operation data of machine tool during the predetermined cycle of operations; and an analysis system configured for receiving the operation data and for executing a statistical and data mining analysis on the operation data, comparing a pattern of the operation data with a predetermined pattern, for identifying any anomalies and degradation paths.

A fault prediction system includes a machine learning device that learns conditions associated with a fault of an industrial machine. The machine learning device includes a state observation unit that, while the industrial machine is in operation or at rest, observes a state variable including, e.g., data output from a sensor, internal data of control software, or computational data obtained based on these data, a determination data obtaining unit that obtains determination data used to determine whether a fault has occurred in the industrial machine or the degree of fault, and a learning unit that learns the conditions associated with the fault of the industrial machine in accordance with a training data set generated based on a combination of the state variable and the determination data.

Provided are a computer system, and a method and a program for operation verification that easily know the operation of a machine tool more exactly. The computer system acquires operating data while a machine tool operates for a predetermined time, generates computer graphics virtually showing that the machine tool operates for the predetermined time from the acquired data, acquires an image of the machine tool that has taken for the predetermined time, and compares the image with the computer graphics for the predetermined time. The computer system also notifies the abnormality if an abnormality has been detected as the result of the comparison.

A device for monitoring an operating condition of a machine is disclosed. The device includes a sound detection device located in proximity to the machine and configured to collect a plurality of sound signals in real-time from the machine. The device also includes a processor electrically coupled to the sound detection device. The processor is configured to acquire one or more predefined sound analytics models associated with the machine. The processor is also configured to analyse the plurality of sound signals based on the one or more predefined sound analytics models. The processor is further configured to identify the operating condition of the machine based on an analysed result and the one or more predefined sound analytics models. The device also includes a sound analytics system which is further configured to update the one or more predefined sound analytics models based on the plurality of sound signals.