Abstract: The time-series data diagnosis device includes a storage, a difference-degree calculator, a diagnosis-result-information generator, an evaluation information acquirer, and an updater. The difference-degree calculator calculates a difference degree between diagnosis target time-series data and a learned-sample subsequence data item stored in the storage. The diagnosis-result-information generator generates, based on the difference degree calculated by the difference-degree calculator, diagnosis result information about the diagnosis target time-series data. The evaluation information acquirer acquires evaluation information indicating evaluation as to whether a diagnosis result indicated by the diagnosis result information is valid.

Abstract: A diagnosis device (10) includes an acquirer (101) and a diagnoser (140). The acquirer (101) acquires a series of input values as an input signal to be diagnosed as to presence or absence of abnormality. The diagnoser (140) diagnoses the presence or absence of the abnormality from (i) a first index value indicating a distance between an input vector and a predetermined first reference vector, the input vector having components that are the input values of the series acquired by the acquirer (101), and (ii) a second index value indicating an angle between the input vector and a predetermined second reference vector.

Abstract: A data processing apparatus includes a first processing unit that executes real-time processing with respect to data, a second processing unit that executes batch processing with respect to data that is output from the first processing unit as a result of processing by the first processing unit, and a monitor that monitors a status of the processing by the first processing unit and a status of processing by the second processing unit. The first processing unit includes a plurality of subprocessing units and buffers, and the second processing unit also includes a plurality of subprocessing units and buffers. The second processing unit includes a storage. The monitor includes a first monitor that monitors, for each of the buffers included in the first processing unit, an amount of the data stored in the corresponding buffer and a second monitor that monitors a total amount of the data stored in the buffers included in the second processing unit and the data stored in the storage.

Abstract: A machine tool machining dimensions prediction device (100) includes: a data collector (10) to acquire driving state information of a machine tool; a feature amount extractor (211) to extract a feature amount from the driving state information; a data analyzer (311) to analyze the extracted feature amount; and a machining quality prediction model generator (312) to generate, from the analyzed information, a prediction model of a machining dimension of a workpiece. The machine tool machining dimensions prediction device (100) applies the feature amount and the driving state information to the prediction model during machining of the workpiece to predict a machining quality and refers to a machining dimension quality regulation to determine whether the machining quality satisfies a standard.

Abstract: The management device (10) is connected to a plurality of devices (20), and includes a first receiver (150) to receive a setting of processing to be performed on data output from at least one of the plurality of devices (20), an execution controller (120) to cause a processing unit (110) to execute the processing with the setting received by the first receiver (150), a second receiver (170) to receive a setting of a hierarchical relationship of the plurality of devices (20), and a device manager (180) to provide management information for displaying (i) the hierarchical relationship of the plurality of devices (20) according to the setting received by the second receiver (170) and (ii) information related to an execution state of the processing.

Abstract: A data analysis device (1) according to the present disclosure includes a data collector (131) and a data organizer (135). The data collector (131) collects an input signal that is input from an apparatus connected to the data analysis device (1). The data organizer (135) removes, from among waveforms included in the input signal collected by the data collector (131), a waveform that has an appearance frequency lower than a reference frequency. The data organizer (135) generates learning data by collecting, from among the waveforms included in the input signal, waveforms that have an appearance frequency higher than the reference frequency.

Abstract: The time-series data diagnosis device includes a storage, a difference-degree calculator, a diagnosis-result-information generator, an evaluation information acquirer, and an updater. The difference-degree calculator calculates a difference degree between diagnosis target time-series data and a learned-sample subsequence data item stored in the storage. The diagnosis-result-information generator generates, based on the difference degree calculated by the difference-degree calculator, diagnosis result information about the diagnosis target time-series data. The evaluation information acquirer acquires evaluation information indicating evaluation as to whether a diagnosis result indicated by the diagnosis result information is valid.

Abstract: A training device (100) performs training using a neural network. A training condition acquirer (110) of the training device (100) acquires training conditions that indicate prerequisites of the training. A model selector (150) selects, in accordance with the training conditions, a learning model that serves as a framework of a structure of the neural network. A learning model scale determiner (160) determines, in accordance with the training conditions, a scale of the neural network for the selected learning model. A trainer (170) perform training by inputting training data into the neural network in which the selected learning model is configured to the determined scale.

Abstract: A learning device (10) includes an acquirer (11), a learner (12), and a generator (14). The acquirer (11) acquires a learning signal. The learner (12) performs, in accordance with similarities indicating degrees of similarity between waveforms, clustering of partial signals cut out from the learning signal acquired by the acquirer (11), and learns reference waveforms that each indicate a waveform of a corresponding partial signal of the clustered partial signals. The generator (14) generates, based on at least one of a distribution of the similarities or characteristics of clusters that each include a corresponding partial signal of the clustered partial signals, progress information indicating a progress status of the learning by the learner (12), and outputs the progress information.

Abstract: An index outputter (120) of a learning processor (100) outputs an index for selection of an analysis procedure based on attributes of an analysis target. The attributes to be inputted include features of the analysis target itself and features of the environment surrounding the analysis target. The analysis procedure selector (170) selects an analysis procedure among analysis procedures as an analysis procedure for which diagnosis target data to be outputted by the analysis target is to be analyzed. The selected analysis procedure is a procedure in which an evaluation value of a property of data analysis satisfies a preset condition in connection with the index.

Abstract: A learning device (10) includes an acquirer (11), a learner (12), and a generator (14). The acquirer (11) acquires a learning signal. The learner (12) performs, in accordance with similarities indicating degrees of similarity between waveforms, clustering of partial signals cut out from the learning signal acquired by the acquirer (11), and learns reference waveforms that each indicate a waveform of a corresponding partial signal of the clustered partial signals. The generator (14) generates, based on at least one of a distribution of the similarities or characteristics of clusters that each include a corresponding partial signal of the clustered partial signals, progress information indicating a progress status of the learning by the learner (12), and outputs the progress information.

Abstract: An index outputter (120) of a learning processor (100) outputs an index for selection of an analysis procedure based on attributes of an analysis target. The attributes to be inputted include features of the analysis target itself and features of the environment surrounding the analysis target. The analysis procedure selector (170) selects an analysis procedure among analysis procedures as an analysis procedure for which diagnosis target data to be outputted by the analysis target is to be analyzed. The selected analysis procedure is a procedure in which an evaluation value of a property of data analysis satisfies a preset condition in connection with the index.

Abstract: A data processing device (10) connectable to a machine (20) includes a communicator (130) that communicates with the machine (20), a receiver (140) that receives a setting on a process flow for processing data received by the communicator (130), and a controller (110) that (i) transmits the data received by the communicator (130) to one processor (160) or one of a plurality of processors (160) for performing subprocesses included in the process flow, (ii) causes the one processor (160) or the plurality of processors (160) to perform the subprocesses in an order corresponding to the process flow, and (iii) transmits a processing result obtained from the execution of the subprocesses.

Abstract: A data processing apparatus (10) includes a receiver (120), a specifier (142), and a task controller (141). The receiver (120) receives a setting of a process flow defining subprocesses that are sequentially executed with respect to data output from a device (21). The specifier (142) specifies, based on the setting received by the receiver (120), processing units (130) for execution of the subprocesses. The task controller (141) determines, based on the setting received by the receiver (120), and launches the tasks in accordance with the order, an order for launching tasks for achievement of the processing units (130) specified by the specifier (142).

Abstract: An information processing device includes: a first processing unit that processes data on a real-time operating system that executes processing within a specified time; a second processing unit that processes data on a non-real-time operating system; and a transmission unit that adjusts a data amount of transmission data to be transmitted at a time on the basis of an accumulation amount of transmission data transmitted between the first processing unit and the second processing unit, and transmits the transmission data.

Abstract: A management apparatus includes a display controller to display a configuration of a system including a management target device and another management target device managed by another management apparatus such that a relationship between the management target devices is indicated.

Abstract: An information processing device includes a data input unit that receives time-series data from an external device, a first processing unit that adds time information to data acquired from the data input unit and processes data to which the time information has been added, on a real-time operating system that performs a process within a specified time period, and a second processing unit that processes data to which the time information has been added, on a non-real-time operating system.

Abstract: A display data generation device (10) includes: an acquisition unit (11) to acquire an input signal; a waveform generation module (161) (i) to extract, from waveform patterns predetermined as normal waveform patterns, a waveform pattern having the highest degree of similarity to the input signal for each interval of the input signal by using as the degree of similarity a degree to which waveforms are similar to each other and (ii) to generate a comparison waveform based on the waveform pattern extracted for each interval; and a data generation module (162) to generate and outputting display data for displaying a waveform of the input signal and the comparison waveform.

Abstract: An information processing device includes: a first processing unit that processes data on a real-time operating system that executes processing within a specified time; a second processing unit that processes data on a non-real-time operating system; and a transmission unit that adjusts a data amount of transmission data to be transmitted at a time on the basis of an accumulation amount of transmission data transmitted between the first processing unit and the second processing unit, and transmits the transmission data.