A plurality of basic simulations independent of one another are carried out, which determine respective remaining service life predictions for the machine. The remaining service life predictions and characteristic data are fed to a neural network, which outputs weights for the remaining service life predictions. A final prediction is calculated from the remaining service life predictions by weighting the remaining service life predictions relative to one another. A hybrid model is produced, which results from the combination of the basic simulations with the neural network. The remaining service life can be predicted not only for a small number of machines for which a specific simulation model has been manually created. The hybrid model enables condition monitoring for any further types and configurations of machines that merely belong to the same machine class. The basic simulations can therefore also be applied to previously unknown machines.

Method and an arrangement for monitoring status of a production device configured to implement an industrial process or industrial production with a control device with sensors and actuators, where at least one further sensor is carried through the production device with a product being processed by the production device, where further signals are wirelessly transmitted to a status-monitoring device by the further sensor, and where the signals and/or status information items and the further signals are placeable in relationship with one another to generate status information item about the production device such that it is possible to plan the status monitoring separately from the production device, and possible to combine information items of the industrial control device with the information items and data of a mobile sensor (further sensor) that passes through the production device, such that more precise status information items about the production device status can be acquired.

Embodiments of systems and methods for supporting predictive and preventative maintenance are disclosed. One embodiment includes manufacturing cells within a manufacturing environment, where each manufacturing cell includes a cell controller and welding equipment, cutting equipment, and/or additive manufacturing equipment. A communication network supports data communications between a central controller and the cell controller of each of the manufacturing cells. The central controller collects cell data from the cell controller of each of the manufacturing cells, via the communication network. The cell data is related to the operation, performance, and/or servicing of a same component type of each of the manufacturing cells to form a set of aggregated cell data for the component type. The central controller also analyzes the set of aggregated cell data to generate a predictive model related to future maintenance of the component type.

One embodiment is a system including a component for installation on a vehicle comprising a central maintenance computer (“CMC”); a configuration/maintenance module (“CMM”) associated with the component and including memory for storing component information, a sensor for detecting a condition and generating data indicative thereof; a microprocessor for processing the sensor data and updating the component information with the processing results; and a communications interface between the CMM and the CMC. The system further includes a remaining useful life (“RUL”) module associated with the component that periodically updates an RUL, the RUL module periodically updating an RUL value for the component and communicating the updated RUL value to the CMM for storage in the memory. The CMC communicates with the CMM to update the component information included in the memory based on information input to the CMC by a user or changes in a condition of the vehicle.

Disclosed is a method of determining the remaining time interval until a measurement characteristic of a field device will have drifted outside of a predetermined tolerance range and a service action is required. The method includes predetermining a maximum tolerance of the measurement characteristic correlated/related to the measuring performance of the field device; registering continuously the measurement characteristic of the field device; estimating a lag time interval wherein the estimated lag time interval depends on the drift of the measurement characteristic of the field device in the process specific application; using a method of Artificial Intelligence to determine, at the end of the estimated lag time interval, the remaining time interval until the measurement characteristic of a field device will have drifted outside the predetermined maximum tolerance; and generating a message informing of the remaining time interval until the service action is required.

A manufacturing management device includes a determination section configured to determine the necessity of maintenance on any of the multiple production devices during production by the production line; and a conveyance management section configured to stop loading circuit boards, at a predetermined time, onto a reference device among the multiple production devices when it is determined that maintenance is necessary, the reference device being positioned upstream in the production line from a maintenance target device having a cause for maintenance.

A robot including a robot mechanism including joints and drive units, a control unit controlling the drive units so that an inspection operation to inspect one target drive unit among the drive units is executed by the robot mechanism, and a notification unit notifying maintenance information of the target drive unit based on a current value of a motor of the target drive unit during the inspection operation, or on information associated with the current value, and the inspection operation includes transmitting, to the motor of the target drive unit, control command to rotate a joint as much as a predetermined rotation angle, and thereby moving a tip of the robot mechanism or a tool at the tip, close to an object at a predetermined position from a predetermined start position, to press the object, and separating the tip of the robot mechanism or the tool away from the object.

A method includes receiving, from one or more sensors associated with manufacturing equipment, current trace data associated with producing, by the manufacturing equipment, a plurality of products. The method further includes performing signal processing to break down the current trace data into a plurality of sets of current component data mapped to corresponding component identifiers. The method further includes providing the plurality of sets of current component data and the corresponding component identifiers as input to a trained machine learning model. The method further includes obtaining, from the trained machine learning model, one or more outputs indicative of predictive data and causing, based on the predictive data, performance of one or more corrective actions associated with the manufacturing equipment.

Provided are: a tension estimation device capable of accurately estimating the tension of a belt; a life evaluation device capable of accurately evaluating the life of a transmission mechanism, from the tension of the belt; and a robot system comprising these. The tension estimation device comprises: a transmission mechanism that transmits power via a belt; at least one motor disposed in the vicinity of the belt; a motor calorific value calculation unit that calculates the motor calorific value on the basis of at least one out of the current value or rotation speed for at least one motor; a frictional calorific value calculation unit that calculates the frictional calorific value of the transmission mechanism, on the basis of at least one among the current value or rotation speed for at least one motor and a friction coefficient for at least one shaft disposed in the vicinity of the belt; and a belt tension estimation unit that estimates the tension of the belt on the basis of the motor calorific value and the frictional calorific value.

In a method for error detection and monitoring an electronically closed-loop or open-loop controlled machine part, operating parameters and monitoring parameters of machine parts are recorded and stored. A comparison group of comparable machine parts and comparable operating parameters is determined based on the recorded and stored operating parameters and a machine part to be compared. A statistical analysis procedure is used for creating a threshold value based on the determined comparison group, and for detecting a variance of at least one state or at least one of the monitoring parameters based on the threshold value. The variance is assigned to the machine part.