A failure diagnosing device diagnoses failure of a plurality of reduction gears of a mechanical apparatus which includes a plurality of operation parts, a plurality of motors, and the plurality of reduction gears. The failure diagnosing device includes an acceleration/deceleration period identifying module configured to identify an acceleration/deceleration period of operation of one of the plurality of operation parts, a motor current processing module configured to acquire a peak value of an amplitude of a frequency component of motor current in a specific frequency band of one motor that drives the one operation part, in the acceleration/deceleration period, and a determining module configured to determine whether a sign of failure exists in one of the reduction gears.

A failure diagnosing device diagnoses failure of a plurality of reduction gears of a mechanical apparatus which includes a plurality of operation parts, a plurality of motors, and the plurality of reduction gears. The failure diagnosing device includes an acceleration/deceleration period identifying module configured to identify an acceleration/deceleration period of operation of one of the plurality of operation parts, a motor current processing module configured to acquire a peak value of an amplitude of a frequency component of motor current in a specific frequency band of one motor that drives the one operation part, in the acceleration/deceleration period, and a determining module configured to determine whether a sign of failure exists in one of the reduction gears.

Conditioning monitoring is provided for rotating components in gearboxes that accounts for gear system dynamics, allowing for improved analysis. A rotation rate for the component is generated from vibration data by estimating the rotation rate based on a tachometer measurement of another shaft and the shaft ratio. This estimated rotation rate is used, together with the known configuration of the component, to estimate a known gear mesh frequency of the component. By filtering for a range of frequencies around the gear mesh frequency based on variation in the shaft rate, the gear mesh frequency can be determined and from that signal, an actual rotation rate for the component can be determined. The actual or determined rotation rate can then be used in deriving an analytic vibration spectrum for the component that is not degraded due to gear system dynamics effects.

An estimation device includes mixed amount acquisition part 110, 201 configured to acquire a foreign matter mixed amount in a fluid that lubricates meshing elements G1 to G4, a fatigue damage degree estimation unit 202 configured to estimate fatigue damage degrees received by the meshing elements G1 to G4 per unit traveling of a vehicle based on the acquired foreign matter mixed amount, and a cumulative fatigue damage degree estimation unit 203 configured to estimate cumulative fatigue damage degrees of the meshing elements G1 to G4 based on the estimated fatigue damage degrees and at least one of a traveling distance and traveling time of the vehicle.

An estimation device includes mixed amount acquisition part 110, 201 configured to acquire a foreign matter mixed amount in a fluid that lubricates meshing elements G1 to G4, a fatigue damage degree estimation unit 202 configured to estimate fatigue damage degrees received by the meshing elements G1 to G4 per unit traveling of a vehicle based on the acquired foreign matter mixed amount, and a cumulative fatigue damage degree estimation unit 203 configured to estimate cumulative fatigue damage degrees of the meshing elements G1 to G4 based on the estimated fatigue damage degrees and at least one of a traveling distance and traveling time of the vehicle.

A method for predicting and updating gearbox lifetime expectancies is provided. The method includes: determining, by a computing system, a lifetime expectancy of a gearbox located at a first location; obtaining, by the computing system and from a computing device at the first location, sensor measurements associated with the gearbox; updating, by the computing system, the lifetime expectancy of the gearbox based on the sensor measurements; and causing, by the computing system, display of the updated lifetime expectancy of the gearbox.

A method for predicting and updating gearbox lifetime expectancies is provided. The method includes: determining, by a computing system, a lifetime expectancy of a gearbox located at a first location; obtaining, by the computing system and from a computing device at the first location, sensor measurements associated with the gearbox; updating, by the computing system, the lifetime expectancy of the gearbox based on the sensor measurements; and causing, by the computing system, display of the updated lifetime expectancy of the gearbox.

Provided is a transmission device monitoring system including: a diagnostic frequency estimation unit that extracts a plurality of diagnostic frequency candidate groups from a frequency region separated by a specific frequency or more using at least current information on a motor, a gear ratio of a transmission device, and the number of stages of the transmission device, and estimates a frequency satisfying a specific relationship from frequencies obtained in the plurality of diagnostic frequency candidate groups as a diagnostic frequency; and an abnormality diagnosis unit that diagnoses abnormality of the transmission device using at least the one diagnostic frequency estimated by the diagnostic frequency estimation unit.

A statistical method is used to separate periodic from non-periodic vibration peaks in machine vibration spectra. Generally, a machine vibration spectrum is not normally distributed because the amplitudes of periodic peaks are significantly large and random relative to the generally Gaussian noise. In a normally distributed signal, the statistical parameter Kurtosis has a value of 3. The method sequentially removes each largest amplitude peak from the peaks in a frequency region of interest in the spectrum until the Kurtosis has a value of three or less. The removed peaks, which are all considered to be periodic, are placed into a candidate peak list. As the process of building the candidate peak list proceeds, if the kurtosis of the remaining peaks in the frequency region of interest falls to three or less, the process stops and the candidate peak list is defined.

A statistical method is used to separate periodic from non-periodic vibration peaks in machine vibration spectra. Generally, a machine vibration spectrum is not normally distributed because the amplitudes of periodic peaks are significantly large and random relative to the generally Gaussian noise. In a normally distributed signal, the statistical parameter Kurtosis has a value of 3. The method sequentially removes each largest amplitude peak from the peaks in a frequency region of interest in the spectrum until the Kurtosis has a value of three or less. The removed peaks, which are all considered to be periodic, are placed into a candidate peak list. As the process of building the candidate peak list proceeds, if the kurtosis of the remaining peaks in the frequency region of interest falls to three or less, the process stops and the candidate peak list is defined.