Abstract: An abnormality diagnosis device includes: a feature quantity calculation unit which calculates a plurality of feature quantities from the time-series data of the current values; an operation mode determination unit which determines an operation mode of a compressor on the basis of the load torque and the drive frequency; a feature quantity distribution generation unit which generates a feature quantity distribution from values of the plurality of feature quantities; a reference region generation unit which generates a reference region on the basis of the feature quantity distribution that is obtained in a normal case; and a determination unit which compares the feature quantity distribution that is obtained during an abnormality diagnosis and the reference region corresponding to the operation mode that is applied during the abnormality diagnosis, to determine whether an abnormality is present or absent in either of the compressor and the electric motor.

Abstract: An abnormality diagnosis device includes: a data acquirer to acquire a current waveform and a driving frequency of an electric motor; a storage which stores a combination of a current value of the current waveform and the driving frequency at an identical time by the data acquirer; a data determiner to determine whether or not a current value of the current waveform and the driving frequency as a diagnosis target at an identical time by the data acquisition match the combination stored in the storage; an analyzer to perform frequency analysis for the current waveform determined to be matched by the data determiner, to extract sideband waves, and calculate a spectrum intensity of the sideband waves; and an abnormality diagnosis processor to make a diagnosis that abnormality has occurred, when the spectrum intensity of the sideband waves is equal to or greater than a threshold.

Abstract: Provided is an abnormality diagnosis device including: a physical quantity measurement processor configured to acquire, as time-series data, a physical quantity of an electric motor or a load; a feature parameter calculation processor configured to calculate feature parameters; a correlation function creation processor configured to create a correlation function for two or more feature parameters, and calculate a correlation coefficient corresponding to a measurement result of the physical quantity that changes depending on an abnormality occurrence state; a database configured to store in advance reference data in which a remaining time until a failure is caused and the correlation coefficient are associated with each other; and a diagnosis processor configured to extract from the reference data the remaining time corresponding to the correlation coefficient, and identify the remaining time until the electric motor or the load causes a failure or a part that has caused a failure.

Abstract: In a DC feed system configured by a plurality of DC electrical circuits being combined, when an arc fault occurs, it is difficult to identify a DC electrical circuit in which an arc has occurred. For this reason, a DC electrical circuit protection apparatus includes: a plurality of DC electrical circuits, each of which has a current sensor provided in at least one of a positive or a negative electrical path and has arc noise absorbing mechanism on the upstream side of the current sensor; and an arc detection device which compares respective arc noise signal strengths of the DC electrical circuits from current signals detected by the current sensors of the DC electrical circuits, and based on the signal strengths, identifies a circuit in which an arc has occurred, wherein a DC electrical circuit in which an arc has occurred is identified.

Abstract: Provided is an abnormality diagnosis device including: a physical quantity measurement processor configured to acquire, as time-series data, a physical quantity of an electric motor or a load; a feature parameter calculation processor configured to calculate feature parameters; a correlation function creation processor configured to create a correlation function for two or more feature parameters, and calculate a correlation coefficient corresponding to a measurement result of the physical quantity that changes depending on an abnormality occurrence state; a database configured to store in advance reference data in which a remaining time until a failure is caused and the correlation coefficient are associated with each other; and a diagnosis processor configured to extract from the reference data the remaining time corresponding to the correlation coefficient, and identify the remaining time until the electric motor or the load causes a failure or a part that has caused a failure.

Abstract: A system for estimating a severity of a bearing fault in an induction motor, uses a set of filters and a set of quantitative models designed for a set of fault frequencies. The system, upon receiving the measurements of the stator current, extracts the first fault current from the stator current using the first filter, determine the first mutual inductance variation from the first fault current using the first quantitative model, and classify the first mutual inductance variation with the fault severity classifier to determine the severity of a first type of fault in the induction motor. Similarly, the system classifies a second type of fault using the second filter and the second quantitative model. The system outputs one or combination of the severity of the first type of fault in the induction motor and the severity of the second type of fault in the induction motor.

Abstract: The power transmission mechanism abnormality diagnostic device includes a monitoring diagnosis unit for performing determination as to abnormality of a power transmission mechanism, and a current detector connected to a power supply of an electric motor, wherein the monitoring diagnosis unit includes an analysis unit for analyzing current transmitted from the current detector and an abnormality determination unit for performing determination as to abnormality of the power transmission mechanism on the basis of a result of analysis by the analysis unit.

Abstract: A system for estimating a severity of a bearing fault in an induction motor, uses a set of filters and a set of quantitative models designed for a set of fault frequencies. The system, upon receiving the measurements of the stator current, extracts the first fault current from the stator current using the first filter, determine the first mutual inductance variation from the first fault current using the first quantitative model, and classify the first mutual inductance variation with the fault severity classifier to determine the severity of a first type of fault in the induction motor. Similarly, the system classifies a second type of fault using the second filter and the second quantitative model. The system outputs one or combination of the severity of the first type of fault in the induction motor and the severity of the second type of fault in the induction motor.

Abstract: The power transmission mechanism abnormality diagnostic device includes a monitoring diagnosis unit for performing determination as to abnormality of a power transmission mechanism, and a current detector connected to a power supply of an electric motor, wherein the monitoring diagnosis unit includes an analysis unit for analyzing current transmitted from the current detector and an abnormality determination unit for performing determination as to abnormality of the power transmission mechanism on the basis of a result of analysis by the analysis unit.

Abstract: An abnormality diagnosis device includes: a data acquirer to acquire a current waveform and a driving frequency of an electric motor; a storage which stores a combination of a current value of the current waveform and the driving frequency at an identical time by the data acquirer; a data determiner to determine whether or not a current value of the current waveform and the driving frequency as a diagnosis target at an identical time by the data acquisition match the combination stored in the storage; an analyzer to perform frequency analysis for the current waveform determined to be matched by the data determiner, to extract sideband waves, and calculate a spectrum intensity of the sideband waves; and an abnormality diagnosis processor to make a diagnosis that abnormality has occurred, when the spectrum intensity of the sideband waves is equal to or greater than a threshold.

Abstract: In a DC feed system configured by a plurality of DC electrical circuits being combined, when an arc fault occurs, it is difficult to identify a DC electrical circuit in which an arc has occurred. For this reason, a DC electrical circuit protection apparatus includes: a plurality of DC electrical circuits, each of which has a current sensor provided in at least one of a positive or a negative electrical path and has arc noise absorbing mechanism on the upstream side of the current sensor; and an arc detection device which compares respective arc noise signal strengths of the DC electrical circuits from current signals detected by the current sensors of the DC electrical circuits, and based on the signal strengths, identifies a circuit in which an arc has occurred, wherein a DC electrical circuit in which an arc has occurred is identified.

Abstract: A DC power generation system in which an arc detector, comprising an arc noise analysis unit which detects an arc which occurs in a DC power generation system based on noise of signal of a voltage-current sensor, a voltage-current variation analysis unit for specifying an arc occurrence point in a case where an arc is detected in the arc noise analysis unit, a voltage-current operation point of output of each string is analyzed based on a signal from a voltage-current sensor and based on variation of voltage-current operation point before and after when an arc is detected and a switch control unit for controlling of opening or closing of a switch based on an arc specified result in a voltage-current variation analysis unit, is provided.

Abstract: A DC power generation system in which an arc detector, comprising an arc noise analysis unit which detects an arc which occurs in a DC power generation system based on noise of signal of a voltage-current sensor, a voltage-current variation analysis unit for specifying an arc occurrence point in a case where an arc is detected in the arc noise analysis unit, a voltage-current operation point of output of each string is analyzed based on a signal from a voltage-current sensor and based on variation of voltage-current operation point before and after when an arc is detected and a switch control unit for controlling of opening or closing of a switch based on an arc specified result in a voltage-current variation analysis unit, is provided.

Abstract: An access control apparatus includes: a determination section; and an update section.