The present disclosure relates to an apparatus and a method for detecting an abnormality of a tool of a machine tool, and more particularly, to an apparatus and a method for correcting backlash of a machine tool, which are capable of repeatedly measuring a load of a workpiece transferring unit or a workpiece machining unit, and automatically detecting an abnormality of a tool based on a normal range of load data that are proportional to a standard deviation of the load data measured based on any one of a mode value, a mean value, or a median value of the repeatedly measured load data.

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.

An abnormality factor identification apparatus includes a sensor signal obtaining unit that obtains sensor signals associated with the physical state of a machine, an operating state determination unit that determines operating states of the machine based on information obtained from the machine, an abnormality level calculation unit that calculates the abnormality levels of the sensor signals for each operating state of the machine determined by the operating state determination unit, and a factor identification unit that determines a factor in an abnormality in the machine from historical data being a series of the abnormality levels for each operating state.

A determination apparatus includes circuitry to receive operation information corresponding to an action being performed by a machine to be diagnosed and a detection signal of a physical quantity that changes according to the action of the machine; take out, from the detection signal, an operation detection signal indicating that the machine is operating, based on the operation information; extract feature information of the operation detection signal; select, from the feature information, particular feature information to be compared with a plurality of reference feature information; and determine a machining section of the machine in the feature information, based on the plurality of reference feature information and the particular feature information.

A system for diagnosing performance degradation of a machine element includes an AE sensor that generates an AE waveform signal; a vibration sensor that generates an acceleration signal; a signal processing unit including an AE signal processing system that performs predetermined signal processing on the plurality of AE waveform signals, a vibration signal processing system that performs predetermined signal processing on the acceleration signal, and a switching parameter generating section that generates a predetermined numeric parameter to switch a measuring mode from the AE method to the vibration method; and controlling unit that selects a result of the processing in performed by the AE signal processing system at the initial stage and selects, after the numeric parameter successively turns from increasing to decreasing, a result of the processing by the vibration signal processing system.

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.

An abnormality-detecting device for detecting abnormalities of a tool of a machine tool comprises: an acquiring unit for acquiring multiple measured values relating to the tool as measurement data (vibration information, cutting force information, sound information, main shaft load, motor current, power value); a normal model unit for learning the measurement data acquired during normal machining by one class machine learning and creating a normal model; an abnormality-diagnosing unit for acquiring measurement data during machining after creation of the normal model while diagnosing whether said measurement data is normal or abnormal on the basis of the normal model; and a re-diagnosing unit for re-diagnosing measurement data, which has been diagnosed to be abnormal by the abnormality-diagnosing unit, by a method different from the abnormality-diagnosing unit.

A method for predicting a service state of a printing machine at a defined point in time includes: measuring a plurality of successive process values of a process parameter which is an indicator of the functionality of the printing machine; determining a plurality of successive scatter values which describe the spread of the measured process values within a predetermined time range; determining a local scatter minimum of the scatter values; determining a baseline, in that a baseline value is established that correlates with the value of the process parameter at the point in time of the local minimum and that does not change up until a new determination of a baseline; and determining a health value at a specific point in time, with a predetermined relation of the process value to the baseline value at this point in time. A service state is assessed upon the health value exceeding a predetermined threshold.

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.

An abnormality factor identification apparatus includes a sensor signal obtaining unit that obtains sensor signals associated with the physical state of a machine, an operating state determination unit that determines operating states of the machine based on information obtained from the machine, an abnormality level calculation unit that calculates the abnormality levels of the sensor signals for each operating state of the machine determined by the operating state determination unit, and a factor identification unit that determines a factor in an abnormality in the machine from historical data being a series of the abnormality levels for each operating state.