Abstract: Provided is an apparatus determining values of N and K for an abnormality diagnostic logic which makes a diagnosis N times for each diagnosis target by using observation values collected therefrom, and generates a diagnosis result showing that the diagnosis target is abnormal if the diagnosis target is judged to be abnormal K or more times. A calculator calculates average false detection rate PFP, average overlooking rate PFN, bias level of false detection MFP, and bias level of overlooking MFN, based on diagnosis result data and inspection result data. A determiner calculates an optimization metric for each combination of values N and K by using the average false detection rate, the bias level of false detection, the average overlooking rate, and the bias level of overlooking, and selects a pair of N and K by which the optimization metric becomes minimum or a threshold value or less.

Abstract: There is provided an apparatus, including a first storage storing first time-series data including instruction values given to first to K-th control devices and measured values from first to K-th sensors during a first period, a model optimizer, for each of combinations of two of the control devices, generating a diagnostic model instance of a predetermined target model and obtaining an optimized diagnostic model instance in which parameters of the diagnostic mode instance are identified, a second storage storing second time-series data acquired during a second period, a calculator, for each of the optimized diagnostic model instances, calculating a determination score for each of the control devices and sensors using the first and second time-series data, and a determiner determining presence or absence of an abnormality for each of the control devices and the sensors based on each determination score.

Abstract: There is provided an apparatus, including a first storage storing first time-series data including instruction values given to first to K-th control devices and measured values from first to K-th sensors during a first period, a model optimizer, for each of combinations of two of the control devices, generating a diagnostic model instance of a predetermined target model and obtaining an optimized diagnostic model instance in which parameters of the diagnostic mode instance are identified, a second storage storing second time-series data acquired during a second period, a calculator, for each of the optimized diagnostic model instances, calculating a determination score for each of the control devices and sensors using the first and second time-series data, and a determiner determining presence or absence of an abnormality for each of the control devices and the sensors based on each determination score.

Abstract: A shaping machine system is presented. The shaping machine system includes a shaping device including at least one jig mount body for mounting a jig for supporting a workpiece, a shaping tool support body for supporting a shaping tool for shaping the workpiece, a first drive device for rotating the shaping tool, and a second drive device for driving at least one of the shaping tool support body, the jig mount body, or to change a posture of the shaping tool relative to the workpiece. The shaping machine system also includes a computer including an input unit for receiving a jig selection, a shaping tool selection, a shape data reading unit for reading shape data representing a shape of the workpiece after shaping, a shaping data producing unit for producing shaping data for controlling the first and the second drive devices.

Abstract: According to one embodiment, an apparatus includes an first storage unit to store a value output by a communication unit, a second storage unit to store positional data indicating a place the modules, an third storage unit to store an output model indicating the relationship between a sunshine condition and an electrical output, a estimation unit to estimate a sunshine condition for each module based on the value and the output model, a forth storage unit to store the sunshine condition estimated, a correction unit to correct the sunshine condition, and a detection unit to calculate an expected electrical output for each module based on the corrected sunshine condition and the output model, and to detect a fault in the modules.

Abstract: A shaping machine system including a shaping device and a computer is provided. The shaping device includes at least one jig mount body configured for mounting a jig; a shaping tool support body configured for supporting a shaping tool; a first drive device configured for rotating the shaping tool; and a second drive device configured for driving the shaping tool support body or the at least one jig mount body or changing a posture of the shaping tool relative to the workpiece. The computer is configured for controlling the first and second drive devices according to predetermined shaping data. The computer includes a jig position measuring unit configured for measuring a position of the jig; a position error detecting unit configured for detecting a position error of the jig, and a shaping data correcting unit configured for correcting the predetermined shaping data according to a jig position error.

Abstract: A dust collecting case provided on a cutting machine for cutting a target object includes an outer case member and an inner case member. The outer case member includes a bottom plate, a first side member extending from a peripheral edge of the bottom plate, and a suction port. The inner case member includes a second side member positioned inside the first side member, and an upper member provided between an upper portion of the first side member and an upper portion of the second side member such that the upper member closes from above a space surrounded by the first side member and the second side member. The second side member includes an inner peripheral surface that faces and surrounds an outer periphery of the target object. A gap is located in between an outer surface of the second side member and an inner surface of the first side member.

Abstract: Provided is an apparatus determining values of N and K for an abnormality diagnostic logic which makes a diagnosis N times for each diagnosis target by using observation values collected therefrom, and generates a diagnosis result showing that the diagnosis target is abnormal if the diagnosis target is judged to be abnormal K or more times. A calculator calculates average false detection rate PFP, average overlooking rate PFN, bias level of false detection MFP, and bias level of overlooking MFN, based on diagnosis result data and inspection result data. A determiner calculates an optimization metric for each combination of values N and K by using the average false detection rate, the bias level of false detection, the average overlooking rate, and the bias level of overlooking, and selects a pair of N and K by which the optimization metric becomes minimum or a threshold value or less.

Abstract: A shaping machine system is presented. The shaping machine system includes a shaping device including at least one jig mount body for mounting a jig for supporting a workpiece, a shaping tool support body for supporting a shaping tool for shaping the workpiece, a first drive device for rotating the shaping tool, and a second drive device for driving at least one of the shaping tool support body, the jig mount body, or to change a posture of the shaping tool relative to the workpiece. The shaping machine system also includes a computer including an input unit for receiving a jig selection, a shaping tool selection, a shape data reading unit for reading shape data representing a shape of the workpiece after shaping, a shaping data producing unit for producing shaping data for controlling the first and the second drive devices.

Abstract: There is provided with a method including: acquiring string data made up of a plurality of monitoring items from an apparatus to be monitored at predetermined or arbitrary time intervals; storing each acquired string data in a storage temporarily; calculating an average and variation for each of the monitoring items and correlation information indicating a correlation between the monitoring items by using each string data stored in the storage; normalizing the acquired string data by using the average and the variation of each monitoring item; calculating a distance from the correlation information for normalized string data by carrying out a computation using the normalized string data and the correlation information; and deciding whether or not there is an abnormal sign in the apparatus to be monitored depending on whether or not calculated distance falls within a confidence interval set in advance for a certain probability distribution.

Abstract: A time series pattern generation unit extracts a key concept corresponding to a text of each data element of a plurality of time series data by referring to an extraction rule memory, and generates a plurality of time series patterns. A colony generation unit unifies a plurality of data elements having the same key concept and substantially the same time duration, and generates colonies of unified elements. In response to a selection of a first colony, a colony extraction unit specifies a second colony having a key concept contrary to the first colony, and extracts colonies earlier than the first colony and the second colony. A time reason extraction unit extracts a time reason as a divergence condition of the first colony from the second colony from the extracted colonies.

Abstract: There is provided with a data recording apparatus including: an observed data storage configured to store values observed by a sensor in association with an observed time; an intersection processing unit configured to plot each observed value in the observed data storage on a plane coordinate system formed by a time axis and an axis representing a value of the sensor, configured to determine whether or not a line segment connecting between observed values at adjacent times intersects a base line preset on the plane coordinate system and configured to calculate an intersection time of the line segment and the base line when the line segment intersects the base line; an intersection data storage configured to store the intersection time calculated by the intersection processing unit; and a deleting unit configured to delete observed values subjected to a processing of the intersection processing unit from the observed data storage.

Abstract: A method for displaying multidimensional data on a screen of a display device, includes assigning a first dimension element, having been selected by a user from among the multidimensional data, to an axis; giving weights to a plurality of member regions using a function of number of data belonging to respective members of the first dimension element; defining the weighted member regions as a plurality of regions on the axis, which are partitioned for the respective members of the first dimension element; arranging the weighted member regions within the screen to form a table; and displaying, in each cell region corresponding to the weighted member region, data belonging to each cell region and belonging to a second dimension element having been selected by the user from among the multidimensional data. The second dimension element is different from the first dimension element.

Abstract: There is provided with an apparatus for monitoring condition of a device by using sensors, including: a data collecting unit configured to collect sensor data; a monitoring executing unit configured to execute monitoring logics to obtain condition values corresponding each of the monitoring logics; an output unit configured to output the values corresponding to a monitoring logic selected by selection information; a distance index calculating unit configured to calculate a distance index between a distribution of values corresponding to selected monitoring logic and each of one or more distribution of values corresponding to each of one or more other monitoring logic; and an information updating unit configured to update the selection information to select one of the other monitoring logic when a number of monitoring logics having the distribution distant more than a first threshold from that of selected monitoring logic is greater than a first threshold or equal.

Abstract: There is provided with a method including: acquiring string data made up of a plurality of monitoring items from an apparatus to be monitored at predetermined or arbitrary time intervals; storing each acquired string data in a storage temporarily; calculating an average and variation for each of the monitoring items and correlation information indicating a correlation between the monitoring items by using each string data stored in the storage; normalizing the acquired string data by using the average and the variation of each monitoring item; calculating a distance from the correlation information for normalized string data by carrying out a computation using the normalized string data and the correlation information; and deciding whether or not there is an abnormal sign in the apparatus to be monitored depending on whether or not calculated distance falls within a confidence interval set in advance for a certain probability distribution.

Abstract: A time series data prediction/diagnosis apparatus includes a first creator creating a model series using time series data items sequentially input, a first calculator calculating a prediction error for the model series at each input of a new time series data item, a second creator creating a plurality of model series candidates when an error between the new time series data item and the model series is larger than a predetermined error, a selector selecting an optimal model series among the plurality of model series candidates and set the optimal model series to a new model series, a second calculator calculating a prediction value using the new model series, and a diagnosis unit diagnosing why the prediction value is led for an output value by the second calculator and add a diagnosis result to the prediction value.

Abstract: A method for displaying multidimensional data on a screen of a display device, includes assigning a first dimension element, having been selected by a user from among the multidimensional data, to an axis; giving weights to a plurality of member regions using a function of number of data belonging to respective members of the first dimension element; defining the weighted member regions as a plurality of regions on the axis, which are partitioned for the respective members of the first dimension element; arranging the weighted member regions within the screen to form a table; and displaying, in each cell region corresponding to the weighted member region, data belonging to each cell region and belonging to a second dimension element having been selected by the user from among the multidimensional data. The second dimension element is different from the first dimension element.

Abstract: A method for displaying multidimensional data on a screen of a display device, includes assigning a first dimension element, having been selected by a user from among the multidimensional data, to an axis; giving weights to a plurality of member regions using a function of number of data belonging to respective members of the first dimension element; defining the weighted member regions as a plurality of regions on the axis, which are partitioned for the respective members of the first dimension element; arranging the weighted member regions within the screen to form a table; and displaying, in each cell region corresponding to the weighted member region, data belonging to each cell region and belonging to a second dimension element having been selected by the user from among the multidimensional data. The second dimension element is different from the first dimension element.

Abstract: A method for displaying multidimensional data on a screen of a display device, includes assigning a first dimension element, having been selected by a user from among the multidimensional data, to an axis; giving weights to a plurality of member regions using a function of number of data belonging to respective members of the first dimension element; defining the weighted member regions as a plurality of regions on the axis, which are partitioned for the respective members of the first dimension element; arranging the weighted member regions within the screen to form a table; and displaying, in each cell region corresponding to the weighted member region, data belonging to each cell region and belonging to a second dimension element having been selected by the user from among the multidimensional data. The second dimension element is different from the first dimension element.

Abstract: A time series pattern generation unit extracts a key concept corresponding to a text of each data element of a plurality of time series data by referring to an extraction rule memory, and generates a plurality of time series patterns. A colony generation unit unifies a plurality of data elements having the same key concept and substantially the same time duration, and generates colonies of unified elements. In response to a selection of a first colony, a colony extraction unit specifies a second colony having a key concept contrary to the first colony, and extracts colonies earlier than the first colony and the second colony. A time reason extraction unit extracts a time reason as a divergence condition of the first colony from the second colony from the extracted colonies.