The PLC is provided that outputs assistance information for assisting elimination of a target abnormality by using abnormality history data having written thereto an abnormality log element containing type information, occurrence time, and elimination time of an abnormality and operation history data having written thereto an operation log element containing content information and execution time of an operation. The output unit extracts, from the abnormality history data, an abnormality log element containing the type information showing a type identical to a type of the target abnormality. The output unit extracts, from the operation history data, an operation log element containing the execution time falling within a period from the occurrence time to the elimination time contained in the abnormality log element thus extracted, and generates the assistance information based on the extracted operation log element.

The present disclosure relates to computer-implemented methods, software, and systems for predicting failure event occurrence for a machine asset. Run-to-failure sequences of time series data that include an occurrence of a failure event for the machine asset are received. One or more candidate cut-off values are determined based on iterative evaluation of a plurality of potential cut-off points. A candidate cut-off value is identified as substantially corresponding to a local peak point for calculated distances between relative frequency distributions of positive and negative sub-sequences. A failure prediction model is iteratively trained to iteratively extract sets of relevant features to determine a prediction horizon for an occurrence of the failure event for the machine asset. A candidate cut-off value associated with a model of highest quality from a set of failure prediction models determined during the iterations is selected to determine the prediction horizon for the machine asset.

Example implementations include aspects for controlling a security system monitoring an environment, comprising collecting sensor data from a plurality of sensors located in the environment, and determining whether a plurality of characteristics of a security event are present in the environment. A determination for each characteristic includes a confidence score indicative of a likelihood that the characteristic is present. The aspects further include identifying, from a plurality of pre-determined environment states, a state of the environment based on a current time and an event schedule of the environment, and generating a combined score that is a function of each respective confidence score and a plurality of weights, wherein each weight corresponds to a respective characteristic and has a value based on the state of the environment. The implementations further include determining that the combined score is greater than a threshold score, and generating an alert indicative of the security event.

A transformer fault diagnosis and positioning system based on a digital twin is provided and includes the following. A communication sensing module, which is configured to transmit bottom-level monitoring data obtained from a device entity to a system support module. The system support module, which is configured to receive and preprocess bottom-level monitoring data, and store various data, models and expert systems. A dynamic twin module, which is configured to analyze a multi-dimensional probability status of a device fault, construct a dynamic degradation model of different health states, and realize model correction through human-computer interaction, real-time measurement, and dynamic update subsequently. A decision-making diagnosis module, which is configured to construct a digital twin of data to be diagnosed, to realize diagnosis and positioning of the health state. A user interface module to display a diagnosis result and related information through a UI interface, to provide further decision-making and human-computer interaction.

A time-series data processing system 100 according to the present invention includes a comparison unit 121 configured to compare a feature amount of each of pieces of partial time-series data in a plurality of specific periods at different positions on a time axis in first time-series data and a feature amount of each of pieces of partial time-series data in a plurality of periods located in correspondence with a positional relationship among the plurality of specific periods in second time-series data, and a detection unit 122 configured to detect a specific state in the second time-series data based on a result of the comparison.

A monitoring apparatus according to the present invention includes: a control unit configured to confirm whether a measured value detected from a monitored object satisfies a preset condition, and execute processing for the monitored object set correspondingly to the condition in a case where the measured value satisfies the condition; and a recording processing unit configured to record an execution status of the processing for the monitored object into preset schedule data.

An embodiment includes duplicating an input dataset being input to a model predictive control (MPC) module for input to a first Hierarchical Temporal Memory (HTM) network. The embodiment also includes generating system behavior data using the MPC module for characteristic data of the input dataset. The embodiment also includes generating first HTM prediction data from the input dataset and the system behavior data using the first HTM network, the first HTM prediction data comprising predictions for respective dimensions of the system. The embodiment also includes generating second HTM prediction data from the first HTM prediction data and system output data using a second HTM network, the second HTM prediction data comprising a distinction between the first HTM prediction and the system output data. Finally, the embodiment includes determining that the distinction of the second HTM prediction data indicates an anomaly and adjusting system input data based on the anomaly.

A system and method for providing a configuration for data extraction from an automation system includes a signal selection agent configured to: receive a user selection of at least one module of the automation system; generate, for display to the user, a user interface identifying one or more selectable signals associated with the selected module and displaying one or more guidance elements comprising data mined from data sources pertaining to the automation system for guiding the user in the selection of relevant signals; receive a user selection of one or more of the selectable signals; and automatically generate the configuration for data extraction on the basis of the selected signals.

A prediction system includes a storage unit that stores a history of an operation state of each of a plurality of target devices and attribute information indicating an attribute of each of the plurality of target devices; a first acquisition unit that acquires an attribute information filter condition in which at least one attribute information included in attribute information of a prediction target device is specified; a second acquisition unit that acquires an operation state filter condition in which at least one operation state included in a history of an operation state of the prediction target device is specified; an extraction unit that extracts a history of an operation state of a target device satisfying the attribute information filter condition and the operation state filter condition; and an estimating unit that predicts the operation state of the prediction target device based on the extracted history of the operation state.

An apparatus and method for establishing triggers for the opening of one or multiple gates to a mold cavity of an injection molding system, followed by a sequence of predetermined valve pin movements over the course on an injection cycle. In one embodiment, the invention provides a graphical user interface and control system enabling a user to select from and arrange a plurality of virtual icons into a user-defined virtual sequence that define associated triggering events and actuator controlled pin movements over the course of an injection cycle. In various embodiments, the apparatus and method allows the system operator to view a simulated profile of such triggers and movements and to compare the simulated profile to an actual profile to access differences and make adjustments to the triggers and sequencing more quickly and efficiently. This is particularly useful in sequential molding systems where multiple gates need to be programmed and adjusted to compensate for variations from a predetermined sequence.