A device includes: a first acquiring unit to acquire context information corresponding to running operation among pieces of context information; a second acquiring unit to acquire detection information output from a detecting unit detecting a physical quantity of a target device; an extracting unit to extract, from the detection information, feature information indicating a feature of the detection information in an interval including a specific operation interval of the target device; a selecting unit to select reference feature information used as reference based on the feature information, and sequentially select pieces of target feature information; a calculating unit to calculate a likelihood of a process interval based on a comparison between the reference feature information and each piece of target feature information; a determining unit to determine whether the target feature information corresponding to the likelihood is included in the process interval based on the likelihood; and an estimating unit to estimate the process interval based on a determination result.

The present invention relates to a control device 200 for use on a numerically controlled machine tool 100, comprising a machine control unit 230 for controlling actuators of the machine tool for a machining process for a workpiece to be performed on the machine tool 100, in particular on the basis of control data, and a monitoring unit 250 for monitoring an operating state of the machine tool 100. In accordance with the invention, the monitoring unit 250 has a computer-implemented neural network 253 (NN), which in particular is designed to read input data from the machine control unit 230 and to output output data specifying an operating state of the machine tool 100.

A diagnostic apparatus includes a context information acquisition unit that acquires context information of operation being performed from a plurality of mechanical apparatuses, a sensing information acquisition unit that acquires physical quantity sensing information sensed by each of sensing units of a target machine and other mechanical apparatuses, a sensing information separation unit that separates the physical quantity information of the target machine and the physical quantity information of the other mechanical apparatuses from each other based on the context information and the sensing information of each of the plurality of mechanical apparatuses, a feature information extraction unit that extracts feature information of the physical quantity information, and an anomaly determination unit that determines, from a model corresponding to the acquired context information and the extracted feature information, whether or not operation of the target machine is normal.

This machine tool system uses a plurality of mobile robots to convey workpieces to a plurality of machine tools, the machine tool system being provided with: machine tool control unit that issues work requests to the machine tools; a mobile robot control unit that determines workable times for the mobile robots on the basis of the work requests; and a determining unit that compares the workable times which are for the mobile robots and respectively planned by the mobile robots, and causes the mobile robot with the fastest workable time to execute the requested work.

Provided is a defect occurrence prediction system for a machine tool that makes it possible to identify the factors causing the occurrence of defects efficiently and effectively, and predict the occurrence of the defects accurately with good precision. A defect occurrence prediction system includes an information data accumulation unit that accumulates various types of information and various types of data relating to a machining operation of the machine tool; a defective product occurrence information data extraction unit that extracts from the information data accumulation unit the various types of information and the various types of data when the defective product is produced in the machined products; and a defect occurrence prediction unit that performs a defect occurrence prediction on a basis of the various types of information and the various types of data extracted by the defective product occurrence information data extraction unit and various types of information and various types of data relating to a machining operation of the machine tool obtained in real time.

A method for detecting and compensating CNC tools being implemented in an electronic device, receives from a detector first parameters and second parameters in respect of a first tool. Such first parameters include at least one of service life, blade break information, and blade chipping information of the first tool, and such second parameters include at least one of length extension information, length wear information, radial wear information, and blade thickness wear information of the first tool. Based on the first parameters, instructions to process the workpiece are transmitted or not. Upon receiving the second parameters, instructions to adjust operation of the first tool are transmitted, to compensate for deterioration in normal use.

When the number of machining operations using a tool attached to a machining head of a machine tool has reached a predetermined number of machining operations for replacement, without a determination unit determining that an abnormality has occurred in the tool, then the tool is replaced with a replacement tool that is held in a tool magazine. Further, the tool removed from the machining head is returned to the tool magazine and stored as an auxiliary replacement tool. Then, if the determination unit determines that an abnormality has occurred in the replacement tool, the replacement tool is replaced with the auxiliary replacement tool.

An anomaly detection device includes: an initial-stage detection information acquisition unit that acquires detection information at an initial stage in a series of a machining step performed using one tool; a map-function identification unit that identifies a mapping function for mapping the detection information at the initial stage, on reference detection information; a wear-progress-stage detection information acquisition unit that acquires detection information at a wear progress stage that follows the initial stage in the series of the machining step; a map-function application unit that applies the mapping function to the detection information at the wear progress stage; an anomaly-degree calculation unit that calculates an anomaly degree indicating a degree of difference, from the reference detection information, of the detection information at the wear progress stage to which the mapping function is applied; and an anomaly detecting unit that detects an abnormality of the tool based on determination as to whether the anomaly degree exceeds an anomaly determination threshold value determined in advance.

A diagnostic apparatus includes a context information acquisition unit that acquires context information of operation being performed from a plurality of mechanical apparatuses, a sensing information acquisition unit that acquires physical quantity sensing information sensed by each of sensing units of a target machine and other mechanical apparatuses, a sensing information separation unit that separates the physical quantity information of the target machine and the physical quantity information of the other mechanical apparatuses from each other based on the context information and the sensing information of each of the plurality of mechanical apparatuses, a feature information extraction unit that extracts feature information of the physical quantity information, and an anomaly determination unit that determines, from a model corresponding to the acquired context information and the extracted feature information, whether or not operation of the target machine is normal.

An information processing apparatus includes: a first acquisition unit configured to acquire detection information output from a detection unit; a second acquisition unit configured to acquire a signal indicating a section in which the target device operates; an extraction unit configured to extract characteristic information from the detection information; a data generation unit configured to calculate a processing section of each of the processing processes in the target device based on the signal, and classifies the detection information and the characteristic information; and an output control unit configured to output at least one of the signal of a section including the processing section, the detection information, and the characteristic information, and output at least one of the signal of sections including a plurality of processing sections of which cycles are different from each other, a plurality of the detection information, and a plurality of the characteristic information.