A determiner which learns acceleration measurement data which has been obtained by an accelerated aging test and indicates that a facility changes from a normal state to an aged state, and advance label data which is obtained by giving a label to data indicating characteristics of aging in the acceleration measurement data. Measurement data of aging diagnosis is obtained from the facility which is operating, teacher aging degree label data is found from a record of maintenance of the facility, and additional data is obtained from the measurement data and the teacher aging degree label data. When a difference between predicted aging degree label data and teacher aging degree label data is greater than a predetermined value, learning data is selected as additional learning data. The additional learning data is learned to update the determiner.

A time series data processing system according to the present invention includes a learning unit configured to learn so as to generate a model that takes, of time series data measured from a measurement target, boundary period time series data that is time series data of a boundary period between a normal period and an anomalous period as an input and outputs a teaching signal determined by a preset function in accordance with change of time of the boundary period time series data. The normal period is a period in which the measurement target is determined to be in a normal state. The anomalous period is a period in which the measurement target is determined to be in an anomalous state.

A fault prediction device capable of predicting an accurate deterioration state is provided. A fault prediction device for predicting fault of a target device whose deterioration state transitions with elapse of time includes autoencoders AED1 to AED4 respectively corresponding to deterioration states of the target device. The autoencoder AED2 corresponding to a first deterioration state determines whether the target device exists in the first deterioration state or not based on a state signal indicating a state of the target device. In a case where it is determined that the target device does not exist in the first deterioration state, the autoencoder AED3 corresponding to a second deterioration state determines whether the target device exists in the second deterioration state or not based on the state signal.

In some implementations, a control system may obtain historical data associated with usage of a distillation column during a historical time period. The control system may configure a prediction model to monitor the distillation column for a hazardous condition. The prediction model may be trained based on training data that is associated with occurrences of the hazardous condition. The control system may monitor, using the prediction model, the distillation column to determine a probability that the distillation column experiences the hazardous condition within a threshold time period. The prediction model may be configured to determine the probability based on measurements from a set of sensors of the distillation column. The control system may perform, based on the probability satisfying a probability threshold, an action associated with the distillation column to reduce a likelihood that the distillation column experiences the hazardous condition within the threshold time period.

A method for remaining useful life prediction includes generating parameter data related to a performance of an electro-mechanical element. The method includes generating simulated behavior data of the electro-mechanical element by executing a digital-twin simulation model based on estimated operating conditions, and generating deviation data that characterizes how the parameter data deviates from the simulated behavior data. The deviation data includes a deterministic component and a stochastic component. The method includes generating extrapolated deviation data by extrapolating the deterministic component and the stochastic component of the deviation data forward in time, calculating a remaining useful life of the electro-mechanical element in response to the extrapolated deviation data, and reporting the remaining useful life to a person associated with the vehicle.

A human flow estimation system comprises: a sensor network comprising a plurality of sensors arranged in a to-be-estimated region for detecting the human flow; a model building module configured to build a human flow state model based on arrangement positions of the sensors, and build a sensor network model based on data of the sensors; and a human flow estimation module configured to estimate the human flow and provide a data weight of the estimated human flow based on the human flow state model and the sensor network model. The human flow estimation system further comprises a failure detection module configured to detect whether each sensor in the sensor network is abnormal, and the model building module is further configured to adjust the human flow state model and the sensor network model when an exception exists on the sensor.

A method for predicting an operating anomaly comprises steps of (i) taking an assembly comprising at least a first and a second equipment item, each equipment item comprising a first operating parameter, (ii) recording and storing measurements over time of the first parameters for the first and the second equipment items, (iii) collecting the measurements during or after the completion of at least one part of an operating cycle, (iv) processing the collected measurements to detect a possible malfunction of the first and second equipment items by establishing a coefficient of determination, (v) emitting a first notification indicating the possible malfunction and/or triggering additional steps if the first coefficient of determination is less than a first threshold, and (vi) emitting a second notification and/or adjusting the first threshold if the first coefficient of determination is greater than or equal to the first threshold.

A method for monitoring a condition of a sliding bearing operated with lubricating oil for a rotating component includes calculating, by a control unit as an output variable of a sliding bearing model, a calculated value of a minimum gap thickness of the sliding bearing. The calculated value is calculated by orbit analysis from at least one physical sliding bearing model to which at least a rotational speed of the rotating component, a bearing load, and a temperature of the sliding bearing are supplied as input variables. The method further includes measuring, with at least one sensor, a minimum gap thickness to provide a measured value of the minimum gap thickness, and comparing the measured value of the minimum gap thickness with the calculated value of the minimum gap thickness for the purpose of adjustment.

This evaluation device comprises: a mathematical model acquisition unit that acquires a mathematical model expressing the state of a power storage element; an operation data acquisition unit that acquires operation data which includes time-series input data input during operation of a system constructed on the basis of the numerical model, and time-series output data output by the system on the basis of the time-series input data; a processing unit that inputs the time-series input data to the numerical model and executes processing causing time-series model output data to be output from the numerical model; and an evaluation unit that evaluates the design and the operation of the system on the basis of the time-series output data and the time-series model output data.

In one embodiment, a method is provided for polishing a substrate. The method generally includes receiving a plurality of dwell times of a pad conditioning disk, wherein the plurality of dwell times are to be used in a pad conditioning process performed on a pad disposed on a platen, and each dwell time corresponding to a zone of a plurality of zones of the pad disposed on the platen, determining a plurality of total pad conditioning disk cut times to be used in the pad conditioning process, each total pad conditioning disk cut time corresponding to a zone of the plurality of zones, and generating a first pad wear removal model based on a set of parameters, including the plurality of dwell times and the plurality of total pad conditioning disk cut times.