Monitoring of a machine is performed by an acoustic monitor which acquires, through an acoustic sensor, acoustic signals from a vicinity of a machine, while the machine is operative. A processor calculates a frequency spectrum of a segment of the acquired acoustic signals, determines boundaries of a frequency band to be analyzed and extracts, from the calculated frequency spectrum, a base frequency window in the determined boundaries, and one or more harmonics windows of harmonics of the determined boundaries. For each of the base and harmonic windows a weight based on a distribution of values of frequencies in the windows is determined and a parameter of operation of the machine is calculated as a function of a weighted sum of the base and harmonic windows. The operation of the machine is evaluated responsive to the calculated parameter.

A diagnostic apparatus includes an acquiring unit that acquires state information indicating an operational state of a motor for driving a shaft of a machine tool; a sensor that measures a physical quantity indicating an environment of the machine tool; a storage unit which stores a normal range of the state information corresponding to a value of the physical quantity; and a judging unit that, when the state information acquired by the acquiring unit is not within the normal range of the state information corresponding to the value of the physical quantity measured by the sensor, judges that the machine tool has an abnormality.

An abnormality determination device includes an input unit to which a device-condition signal output from an operating device is input, a first memory configured to store a determination algorithm for determining a predetermined abnormality of the operating device, an abnormality determination unit configured to determine the predetermined abnormality of the operating device in accordance with the device-condition signal and the determination algorithm, an output unit configured to output a determination result regarding the predetermined abnormality made by the abnormality determination unit to a user, an operation unit configured to receive an input of true-or-false information of whether the determination result is correct from the user, and an update unit configured to update the determination algorithm in accordance with the true-or-false information.

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.

A repair welding control device includes a memory that stores instructions and a processor that executes the instructions. The instructions cause the processor to perform acquiring information indicating a range of a defective portion in main welding of a workpiece, and determining a repair welding start point indicating a start point of repair welding and a repair welding end point indicating an end point of the repair welding such that a repair welding range includes all the range of the defective portion and a range wider than the range of the defective portion.

A control device includes: a machine learning unit that machine-learns a control parameter that determines operating characteristics of a driving unit of a machine serving as a driving target of a motor and sets the control parameter to a motor control device; a health check operation instruction unit that outputs an instruction for a health check operation of driving the motor control device; an operation evaluation unit that acquires information indicating the operating characteristics of the driving unit, calculates an evaluation value on the basis of an evaluation function, and stores the evaluation value in a storage unit; and a deterioration estimation operation unit that estimates deterioration in the operating characteristics of the driving unit of the machine on the basis of the evaluation value stored in the storage unit and the evaluation value calculated by the operation evaluation unit when the health check operation was performed.

An abnormality determination device includes an input unit to which a device-condition signal output from an operating device is input, a first memory configured to store a determination algorithm for determining a predetermined abnormality of the operating device, an abnormality determination unit configured to determine the predetermined abnormality of the operating device in accordance with the device-condition signal and the determination algorithm, an output unit configured to output a determination result regarding the predetermined abnormality made by the abnormality determination unit to a user, an operation unit configured to receive an input of true-or-false information of whether the determination result is correct from the user, and an update unit configured to update the determination algorithm in accordance with the true-or-false information.

It is disclosed a motor identification method for determine possible incorrect connections in a system comprising a plurality of motors corresponding to a plurality of sub-systems, the method comprising: applying an input to at least one of the motors causing its movement; and determining a detection signal corresponding to a position, speed or acceleration of the motor during a detection period; wherein the system comprises a memory storing a set of characteristic signals corresponding to at least some of the sub-systems and wherein the method comprises comparing, by a controller, the detection signal with the set of characteristic signals and correlating the at least one of the motors to a determined sub-system.

An event estimation system includes an upper device, and a lower controller device including first circuitry that acquires operation information of a control target device connected to the lower controller device, estimates a presence or absence of an abnormality based on the operation information, holds the operation information for a certain time period, and transmits, based on the presence or absence of an abnormality and to the upper device, the operation information related to the estimation of the presence or absence of the abnormality. The upper device has second circuitry that receives the operation information from the lower controller device, and operates according to the presence or absence of the abnormality, inputs, using an upper neural network model, the operation information, output event information, and estimates an event.

A motion system and method of managing health of the motion system uses exclusively motion variables used by a servo drive of the motion system to calculate at least one health indication value for the motion system. The health indication value is used to generate a notification for maintenance of the motion system.