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.

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.

An internal failure detection method of an external failure detection system for industrial equipment, the external failure detection system including an array of transducers, the method including: (a) receiving a plurality of signals, each signal being measured by a corresponding transducer of the transducers array; (b) for each pair of transducers among a number of pairs of transducers, calculating at least one value of a correlation parameter between the pair of signals received at step (a) at the pair of transducers, by correlating at least part of the signals or of invertible transforms thereof; (c) for at least one transducer among the number of pairs of transducers, estimating from the values of the correlation parameters calculated at step (b) if the transducer is working properly.

An internal failure detection method of an external failure detection system for industrial equipment, the external failure detection system including an array of transducers, the method including: (a) receiving a plurality of signals, each signal being measured by a corresponding transducer of the transducers array; (b) for each pair of transducers among a number of pairs of transducers, calculating at least one value of a correlation parameter between the pair of signals received at step (a) at the pair of transducers, by correlating at least part of the signals or of invertible transforms thereof; (c) for at least one transducer among the number of pairs of transducers, estimating from the values of the correlation parameters calculated at step (b) if the transducer is working properly.

According to one embodiment, a sensor module includes a sensor and a diagnosis circuit. The sensor includes piezoelectric transducers and switches. The piezoelectric transducers have different resonance frequencies. The switches are provided to correspond to the piezoelectric transducers, respectively. Each of the switches outputs an output signal corresponding to a voltage generated by an inverse piezoelectric effect of a corresponding piezoelectric transducer of the piezoelectric transducers. The diagnosis circuit diagnoses, based on a difference in pattern of the output signal, whether vibration has newly occurred in the sensor, and switch an output destination of the output signal of the sensor according to a result of the diagnosis.

According to one embodiment, a sensor module includes a sensor and a diagnosis circuit. The sensor includes piezoelectric transducers and switches. The piezoelectric transducers have different resonance frequencies. The switches are provided to correspond to the piezoelectric transducers, respectively. Each of the switches outputs an output signal corresponding to a voltage generated by an inverse piezoelectric effect of a corresponding piezoelectric transducer of the piezoelectric transducers. The diagnosis circuit diagnoses, based on a difference in pattern of the output signal, whether vibration has newly occurred in the sensor, and switch an output destination of the output signal of the sensor according to a result of the diagnosis.

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.

A gear roll-testing method directed to the analysis and display of gear-set performance data, including motion transmission error, as acquired on gear-set rolling testers or gear-box test fixtures and includes the analysis and visualization of this data in the time-domain, frequency domain, and hybrids thereof. The invention further includes fundamental improvements in core signal processing, analytical and signal processing sequences that allow the data to be explored in more insightful ways, methods of visualizing and reporting the data and results, and a man-machine user-interface paradigm to provide these functions and features with greater flexibility and utility.