Abstract: A diagnostic device is a device for estimating the tension of a belt of an industrial machine for transmitting power, and is provided with a control unit configured to control a diagnostic operation in which the belt is driven, a data acquisition unit configured to acquire at least feedback data at the time of the diagnostic operation, a preprocessing unit configured to analyze frequency-gain characteristics of the feedback data and create, as input data, a range including a resonant frequency and an anti-resonant frequency in the characteristics, and a machine learning device configured to perform processing related to machine learning, based on the data created by the preprocessing unit. The diagnostic device supports inference or abnormality detection of the value of the belt tension.

Abstract: An anomaly classification device determines whether a detected anomaly is a known anomaly or an unknown anomaly and presents to the user how to manage the detected anomaly not only for a case of a known anomaly but also for a case of an unknown anomaly. This anomaly classification device acquires, as anomaly data, data related to a physical quantity detected when an anomaly occurred in an industrial machine, creates a model used for determining whether or not the anomaly data is anomaly data that is based on a known anomaly cause and a model used for classifying which anomaly cause the anomaly data belongs to, and uses the created models to determine whether or not the anomaly data is based on a known anomaly cause and classify which anomaly cause the anomaly data is based on.

Abstract: To provide a vacuum valve pressure diagnosis device for an opening/closing device equipped with a vacuum valve, the diagnosis device which permits downsizing of the vacuum valve and achieves enhanced safety and reliability. Accordingly, the vacuum valve pressure monitoring device is disposed in a vacuum breaker including: a main circuit opening/closing part including the vacuum valve and an insulating frame covering the vacuum valve; and an operation mechanism part for opening/closing a contact of the vacuum valve. The pressure monitoring device for vacuum valve includes: a first electrode disposed on one side surface of the insulating frame and grounded via an impedance; a second electrode disposed on another side surface of the insulating frame and grounded via a switch; and a potential measuring device connected in between the impedance and the first electrode.

Abstract: This information processing device acquires time series quantitative data associated with an industrial machine and divides the quantitative data into sections (i.e., sections associated with an event of interest for data analysis) determined on the basis of a context. Moreover, a coordinate of a first axis indicative of a time within the divided sections and a coordinate of a second axis indicative of a division order of the sections in a display screen are calculated. Then, at the coordinate of the first axis and the coordinate of the second axis in the display screen, a point visually representing the quantity of the quantitative data is plotted.

Abstract: This abnormality detection device: causes a motor that drives a drive unit at a plurality of rotational speeds to perform a detection operation based on a command for diagnosing the state of the drive unit, acquires, as information indicating the operating state of the drive unit, a control command for the motor driving the drive unit or a feedback signal from the motor driving the drive unit, during the detection operation; analyzes the control instruction for the motor driving the drive unit or the feedback signal from the motor; determines the state of the drive unit based on the result of the analysis, and notifies, based on the determination result, that the state of the drive unit is different from that in normal time.

Abstract: A diagnosis device stores a model used for diagnosing the condition of an industrial machine in a storage unit, acquires data related to the condition of the industrial machine, and based on the acquired data, determines the condition of the industrial machine by using the model stored in the storage unit. Then, in response to detecting that a component of the industrial machine has been replaced based on the acquired data and the data related to the determined condition of the industrial machine, the diagnosis device adapts the model stored in the storage unit to the condition of the industrial machine whose component has been replaced.

Abstract: A diagnostic apparatus of the invention acquires normal data related to an operating state during normal operation of an industrial machine, stores the normal data, generates a learning model by learning based on the stored normal data, and performs an estimation process for normality or abnormality of an operation of the industrial machine using the learning model. The diagnostic apparatus of the invention further generates verification data including at least one piece of abnormal data based on the stored normal data to verify validity of the learning model on receiving a result of the estimation process using the learning model based on the verification data.

Abstract: To provide an abnormality monitoring device capable of reducing the size of a mounted device, enabling high-speed response, and achieving high accuracy in detecting abnormality. The abnormality monitoring device includes: at least one filter that extracts state information pertaining to drive of a mechanism of an industrial machine using a motor, as a signal of at least one frequency band; an integrator that integrates an output of the filter in each frequency band; and an abnormality detection unit that detects abnormality based on an integrated value integrated by the integrator. The state information may be control information of the controller that controls the motor or detection information from at least one detector attached to the mechanism.

Abstract: A plurality of learning models generated by performing machine learning on physical quantities observed during respective operations of a plurality of machines are stored in advance. Then, using the stored learning models and a physical quantity observed during an operation of a machine which is an object to be diagnosed, characteristic differences between the machine which is the object to be diagnosed and the plurality of respective machines are calculated, a learning model used in a diagnosis of the operation of the machine which is the object to be diagnosed is selected based on the calculated characteristic differences, and the operation of the machine which is an object to be diagnosed using the selected learning model is diagnosed.

Abstract: The data management device capable of calculating an evaluation index based on the sensor data includes: a sensor data acquisition unit that acquires the sensor data from one or more sensors installed in an industrial machine; an evaluation index calculation unit that calculates the evaluation index with use of the sensor data; a sensor data storage unit that saves the sensor data; and a sensor data deletion unit that deletes the sensor data when a total volume of the sensor data in the sensor data storage unit exceeds an upper limit. The sensor data deletion unit determines saving priority of the sensor data based on a degree of change in the evaluation index and deletes the sensor data in accordance with the saving priority.

Abstract: A diagnostic apparatus acquires data relating to a state of a machine, performs preprocessing on the acquired data, and modifies the preprocessed data by using a statistic relating to the state of the machine. Then, the diagnostic apparatus performs a process of machine learning by an auto encoder on the basis of the modified data and learns or diagnoses the state of the machine. Thus, this diagnostic apparatus provides a learning and inference method capable of uniformly handling a scale of data indicating a feature.

Abstract: To provide a vacuum valve pressure diagnosis device for an opening/closing device equipped with a vacuum valve, the diagnosis device which permits downsizing of the vacuum valve and achieves enhanced safety and reliability. Accordingly, the vacuum valve pressure monitoring device is disposed in a vacuum breaker including: a main circuit opening/closing part including the vacuum valve and an insulating frame covering the vacuum valve; and an operation mechanism part for opening/closing a contact of the vacuum valve. The pressure monitoring device for vacuum valve includes: a first electrode disposed on one side surface of the insulating frame and grounded via an impedance; a second electrode disposed on another side surface of the insulating frame and grounded via a switch; and a potential measuring device connected in between the impedance and the first electrode.

Abstract: A numerical control system detects a state amount indicating an operation state of a machine tool, creates a characteristic amount that characterizes the state of a machining operation from the detected state amount, infers an evaluation value of the operation state of the machine tool from the characteristic amount, and detects an abnormality in the operation state of the machine tool on the basis of the inferred evaluation value. The numerical control system generates and updates a learning model by machine learning that uses the characteristic amount, and stores the learning model in correlation with a combination of conditions of the machining operation of the machine tool.

Abstract: A numerical control system detects a state amount indicating a state of machining operation of a machine tool, creates a characteristic amount that characterizes the state of machining operation from the detected state amount, infers an evaluation value of the state of machining operation from the characteristic amount, and detects an abnormality in the state of machining operation on the basis of the inferred evaluation value. The numerical control system generates and updates a learning model by machine learning that uses the characteristic amount, and stores the learning model in correlation with a combination of conditions of the machining operation of the machine tool.

Abstract: A diagnostic device is a device for estimating the tension of a belt of an industrial machine for transmitting power, and is provided with a control unit configured to control a diagnostic operation in which the belt is driven, a data acquisition unit configured to acquire at least feedback data at the time of the diagnostic operation, a preprocessing unit configured to analyze frequency-gain characteristics of the feedback data and create, as input data, a range including a resonant frequency and an anti-resonant frequency in the characteristics, and a machine learning device configured to perform processing related to machine learning, based on the data created by the preprocessing unit. The diagnostic device supports inference or abnormality detection of the value of the belt tension.

Abstract: A diagnostic apparatus of the invention acquires normal data related to an operating state during normal operation of an industrial machine, stores the normal data, generates a learning model by learning based on the stored normal data, and performs an estimation process for normality or abnormality of an operation of the industrial machine using the learning model. The diagnostic apparatus of the invention further generates verification data including at least one piece of abnormal data based on the stored normal data to verify validity of the learning model on receiving a result of the estimation process using the learning model based on the verification data.

Abstract: A collision position estimation device allowing extraction of data related to a torque of a section related to collision of a cushion member includes a machine learning device. The machine learning device includes a state observation unit for observing axis movement data indicating information related to movement of the axis and motor torque data indicating data related to torque of a motor driving the axis as a state variable representing a current state of an environment, a label data acquisition unit for acquiring collision position data indicating the position of the axis during collision of the cushion member as label data, and a learning unit for associating and learning the information related to movement of the axis and the data related to the torque of the motor driving the axis with the position of the axis during collision of the cushion member using the state variable and the label data.

Abstract: A plurality of learning models generated by performing machine learning on physical quantities observed during respective operations of a plurality of machines are stored in advance. Then, using the stored learning models and a physical quantity observed during an operation of a machine which is an object to be diagnosed, characteristic differences between the machine which is the object to be diagnosed and the plurality of respective machines are calculated, a learning model used in a diagnosis of the operation of the machine which is the object to be diagnosed is selected based on the calculated characteristic differences, and the operation of the machine which is an object to be diagnosed using the selected learning model is diagnosed.

Abstract: A diagnostic apparatus acquires data relating to a state of a machine, performs preprocessing on the acquired data, and modifies the preprocessed data by using a statistic relating to the state of the machine. Then, the diagnostic apparatus performs a process of machine learning by an auto encoder on the basis of the modified data and learns or diagnoses the state of the machine. Thus, this diagnostic apparatus provides a learning and inference method capable of uniformly handling a scale of data indicating a feature.

Abstract: 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.