A system for monitoring cutting devices in a packaging production line having a line for supplying a material to be cut, an area of a predetermined type of cutting device, and a packaging output line is provided. The system includes means for counting cutting actions of the cutting device, configured to provide a time series of cutting action counting data, a video camera to frame an area of the output line, the video camera configured to provide video data of packaging elements in the output line, first code means, configured to run, on a computer, a first algorithm for recognizing cutting defects starting from video data, the first algorithm providing defect recognition data, and second code means, configured to run, on the computer, a trained expert algorithm to predict cutting performance degradation based on historical defect recognition data, time series of cutting action counting data, and type of cutting device.

A system and method for outlier detection based on process fingerprints from robot cycle data includes a data collection component, which is configured to collect cyclic data, wherein the cyclic data comprises multiple vectors each of which comprises data from one individual cycle of the robot cycle data; a data storage component, wherein which is configured to store the collected cyclic data; and a data processing component, which is configured to perform cloud processing of the stored cyclic data triggered by a cycle-start signal, wherein the data processing component is configured to parse the stored cyclic data and to process the stored cyclic data based on a configuration file defining metadata of the stored cyclic data, wherein the data processing component is configured extract process fingerprints from the stored cyclic data using the metadata.

A method (1000) of diagnosis of operation of a machine tool (10, 100) that includes one or more axes (X, Y, Z) moved by one or more actuators (101, 102, 104) and at least one sensor (30) coupled to the machine tool (10, 100), the method (1000) comprising operations of: generating (1200) a programming sequence of movement of the axes (X, Y, Z) of the machine tool (10, 100); controlling (1210) the movement of the axes (X, Y, Z) of the machine tool (10, 100) according to the programming sequence; receiving (1220) a read-out signal (S) of the at least one sensor (30) coupled to the machine tool (10, 100); and processing (1230) the read-out signal (S) of the at least one sensor (30) coupled to the machine tool (10, 100). The programming sequence comprises instructions that are such as to apply (T) at least one single impulsive variation of a kinematic quantity that regards one or more actuators (101, 102, 104). The operation (1230) of processing the read-out signal (S) comprises processing a response of the machine tool (10, 100) to at least one single impulsive variation. The operation (1230) of processing the read-out signal (S) comprises artificial-neural-network processing (206) via one or more artificial neural networks (206, 2060) configured for analysing operating profiles in particular, one or more signals indicative of the status of the machine tool (W) in the read-out signal (S).

An abnormality determination device includes a control unit for determining an abnormality of a robot, the control unit being configured to calculate a measurement probability distribution which is a probability distribution using disturbance torque acquired during a predetermined period as a random variable. The control unit causes an average of the calculated measurement probability distribution to conform to an average of an evaluation normal model which is a predetermined probability distribution, compares the measurement probability distribution with the evaluation normal model of which the respective averages conform to each other, and determines an abnormality of the robot in accordance with a result of the comparison.

A diagnostic apparatus includes a control unit configured to control a diagnostic operation for driving a belt, a first tension calculation unit configured to perform, based on data obtained from the diagnostic operation, a calculation to estimate a first belt tension value that is a tension value of the belt when the belt is not worn, a second tension calculation unit configured to calculate a second belt tension value in a case where a tension reduction factor of the belt and a wear factor of the belt are included, and a third tension calculation unit configured to calculate the degree of wear of the belt based on the first belt tension value and the second belt tension value. Accordingly, the diagnostic apparatus can support estimation of the degree of wear of a belt or abnormality diagnosis.

A diagnostic apparatus includes a control unit configured to control a diagnostic operation for driving a belt, a first tension calculation unit configured to perform, based on data obtained from the diagnostic operation, a calculation to estimate a first belt tension value that is a tension value of the belt when the belt is not worn, a second tension calculation unit configured to calculate a second belt tension value in a case where a tension reduction factor of the belt and a wear factor of the belt are included, and a third tension calculation unit configured to calculate the degree of wear of the belt based on the first belt tension value and the second belt tension value. Accordingly, the diagnostic apparatus can support estimation of the degree of wear of a belt or abnormality diagnosis.

Example machine usage systems and methods are disclosed. An example system for increasing usage of a machine includes a usage predictor to automatically identify a down period of the machine at a primary location; a usage allocator to automatically assign the machine to a supplemental use during the down period; a cost adjustor to adjust a cost of the machine using the usage value; a transport vehicle to transport the machine from the primary location to the supplemental location; and a transport planner to automatically trigger a transport of the machine to the supplemental location for the supplemental use. The transport planner includes a positioning device to estimate a transportation time between the primary location to the supplemental location and to determine a departure time, and a transport triggering system to trigger an operation of the transport vehicle to transport the machine to the supplemental location at the departure time.

A system for monitoring cutting devices in a packaging production line having a line for supplying a material to be cut, an area of a predetermined type of cutting device, and a packaging output line is provided. The system includes means for counting cutting actions of the cutting device, configured to provide a time series of cutting action counting data, a video camera to frame an area of the output line, the video camera configured to provide video data of packaging elements in the output line, first code means, configured to run, on a computer, a first algorithm for recognizing cutting defects starting from video data, the first algorithm providing defect recognition data, and second code means, configured to run, on the computer, a trained expert algorithm to predict cutting performance degradation based on historical defect recognition data, time series of cutting action counting data, and type of cutting device.

A controller for a wire electrical discharge machine includes: an abnormality location calculating unit that calculates an abnormality location at a time when an abnormality of the wire electrode has occurred on a wire running path; a solution information storage unit that stores abnormality locations and solution information in association with each other; a priority setting unit that sets a degree of priority for each of the abnormality locations according to a history of the abnormality location; and a display control unit that causes a display unit to display the solution information corresponding to each of the abnormality locations by taking the degree of priority into account.

A method (1000) of diagnosis of operation of a machine tool (10, 100) that includes one or more axes (X, Y, Z) moved by one or more actuators (101, 102, 104) and at least one sensor (30) coupled to the machine tool (10, 100), the method (1000) comprising operations of: generating (1200) a programming sequence of movement of the axes (X, Y, Z) of the machine tool (10, 100); controlling (1210) the movement of the axes (X, Y, Z) of the machine tool (10, 100) according to the programming sequence; receiving (1220) a read-out signal (S) of the at least one sensor (30) coupled to the machine tool (10, 100); and processing (1230) the read-out signal (S) of the at least one sensor (30) coupled to the machine tool (10, 100). The programming sequence comprises instructions that are such as to apply (T) at least one single impulsive variation of a kinematic quantity that regards one or more actuators (101, 102, 104). The operation (1230) of processing the read-out signal (S) comprises processing a response of the machine tool (10, 100) to at least one single impulsive variation. The operation (1230) of processing the read-out signal (S) comprises artificial-neural-network processing (206) via one or more artificial neural networks (206, 2060) configured for analysing operating profiles in particular, one or more signals indicative of the status of the machine tool (W) in the read-out signal (S).