A monitoring device is provided herein. The monitoring device is in communication with one or more air data systems. The monitoring device includes a processor and a memory storing program instructions thereon. The program instructions executable by the processor to implement a model. The model includes a monitoring and comparing layer utilizing parameters from the one or more air data systems to measure a performance and determine a drift. The model includes an estimating layer providing a degradation model determining a degradation of health parameters of the one or more air data systems. The model includes a predicting layer monitoring changes and patterns in the drift and the degradation of health parameters.

A fault prediction system includes a machine learning device that learns conditions associated with a fault of an industrial machine. The machine learning device includes a state observation unit that, while the industrial machine is in operation or at rest, observes a state variable including, e.g., data output from a sensor, internal data of control software, or computational data obtained based on these data, a determination data obtaining unit that obtains determination data used to determine whether a fault has occurred in the industrial machine or the degree of fault, and a learning unit that learns the conditions associated with the fault of the industrial machine in accordance with a training data set generated based on a combination of the state variable and the determination data.

A method of controlling a robot system including an articulated robot and a control device is provided. The articulated robot includes links connected by joints, motors configured to drive the joints respectively, and detection devices configured to detect rotation amounts of the joints respectively. The control device controls the motors. The method includes the steps of, by the control device, recording movement information of the joints based on outputs of the detection devices; when detecting an abnormality in the operation of the articulated robot, determining presence or absence of a failure in the articulated robot based on the movement information recorded in at least a period from before detection of the abnormality until detection of the abnormality; and specifying a failure portion of the articulated robot if it is determined that there is a failure in the articulated robot in the step of determining.

A fault prediction system includes a machine learning device that learns conditions associated with a fault of an industrial machine. The machine learning device includes a state observation unit that, while the industrial machine is in operation or at rest, observes a state variable including, e.g., data output from a sensor, internal data of control software, or computational data obtained based on these data, a determination data obtaining unit that obtains determination data used to determine whether a fault has occurred in the industrial machine or the degree of fault, and a learning unit that learns the conditions associated with the fault of the industrial machine in accordance with a training data set generated based on a combination of the state variable and the determination data.

Provided is a device management system to suppress decreases in producibility. A device management system manages a plurality of production-line-configuring devices configuring a production line to produce products, and has a device state data storage part, an analysis part, and a server output part. The production-line-configuring devices include a combination weighing machine to weigh the products, a bag-making and packaging machine to package the products, and a boxing device to box the packaged products into boxes. The device state data storage part accumulates device state data, which is information pertaining to the components included in the production-line-configuring devices. The analysis part performs an analysis process to analyze the device state data accumulated in the device state data storage part. The server output part outputs maintenance information, which pertains to maintenance of the components, on the basis of the results of the analysis process.

An anomality prediction system, which predicts an anomality of a machine, includes: one or more memories; and one or more processors configured to: obtain a state variable including at least one of output data from at least one sensor that detects a state of at least one of the machine or a surrounding environment, internal data of control software controlling the machine, or computational data obtained based on at least one of the output data or the internal data; generate, by inputting the obtained state variable into a machine learning model, a degree of anomality of the machine based on output from the machine learning model; and notify information based on the generated degree of anomality, wherein the notified information includes at least one of the generated degree of anomality at one or more time points, or one or more levels of anomality based on the generated degree of anomality.

A control system of a machine tool includes an analysis device, the analysis device includes acquisition portions which acquire chronological control data when a work is machined and which acquire spatial machined surface measurement data after the machining of the work, a storage portion which stores the control data and the machined surface measurement data, a data-associating processing portion which associates the control data and the machined surface measurement data with each other in two machining directions, a machined surface failure detection portion which detects a failure on the machined surface of the work and a location thereof based on the machined surface measurement data in the two machining directions and an identification portion which identifies a drive axis that causes the failure from the detected failure and the machining direction of the control data corresponding to the detected failure location.

A failure diagnostic device includes a torque detector that detects disturbance torques applied to joint shafts included in a multi-axis robot, a torque grouping circuit that groups the disturbance torques according to a content of an operation executed by the multi-axis robot upon detection of each disturbance torque, a torque correction circuit that obtains a corrected disturbance torque standardized between a plurality of operations with different contents based on a representative value preliminarily set for each grouped disturbance torque and the disturbance torque detected by the torque detector, and a failure diagnostic circuit that performs a failure diagnosis on the multi-axis robot by comparing the corrected disturbance torque with a threshold.

A system includes a power generation system and a controller that controls the power generation system. The controller includes a processor that generates a model of the power generation system that estimates a value for a first parameter of the power generation system. The processor also receives a measured value of the first parameter. The processor further adjusts a correction factor of the model such that the estimated value of the first parameter output by the model is approximately equal to the measured value of the first parameter. The processor also generates a transfer function that represents the correction factor as a function of a second parameter of the power generation system. The processor further displays the transfer function along with one or more previously generated transfer functions to quantify degradation of the power generation system.

A method of controlling a robot system including an articulated robot and a control device is provided. The articulated robot includes links connected by joints, motors configured to drive the joints respectively, and detection devices configured to detect rotation amounts of the joints respectively. The control device controls the motors. The method includes the steps of, by the control device, recording movement information of the joints based on outputs of the detection devices; when detecting an abnormality in the operation of the articulated robot, determining presence or absence of a failure in the articulated robot based on the movement information recorded in at least a period from before detection of the abnormality until detection of the abnormality; and specifying a failure portion of the articulated robot if it is determined that there is a failure in the articulated robot in the step of determining.