Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Mechanical measurement system
Reexamination Certificate
2000-07-18
2003-01-14
Hilten, John S. (Department: 2863)
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Mechanical measurement system
C073S162000
Reexamination Certificate
active
06507789
ABSTRACT:
BACKGROUND
The invention relates generally to machine monitoring and more particularly to gear transmission condition monitoring.
Locomotive traction systems include traction motors, gears, gear cases, axles, wheel-sets, and bearings. The gear cases are often lubricated with oil, and the pinion gear is sometimes fitted onto the traction motor shaft using an interference or shrink fit. In some situations, the oil in the gear case also lubricates motor bearings through a passage from the gear case to the bearings. There are several failures that can result in gear problems, including, for example, cracks in gear teeth due to excessive loading and loss of lubrication resulting in gear teeth wear. Gear problems can lead to gear damage, slipping of pinion gear on the motor shaft, damage to bearings from vibrations and/or loss of lubrication that results in motor failures, and ultimately road failures. Prevention of serious gear, bearing, motor and road failures through incipient failure detection would therefore be desirable.
Haynes et al., U.S. Pat. No. 4,965,513, describes a motor current signature analysis method for diagnosing motor operated devices such as motor-operated valves (MOVs). Frequency domain signal analysis techniques are applied to a conditioned motor current signal to identify various operating parameters of the motor-driven device from the motor current signature. Motor current noise is assumed to include the sum of all the mechanical load changes which refer back to the electric motor drive, and the changes are described as being separated on a frequency and amplitude basis such that the source of various changes in load, such as periodic gear mesh loading, friction events at frequencies corresponding to their origin, and other motor load varying characteristics of the device, can be identified. Motor current noise signatures are taken at different periods during the operating life of the device and compared to determine aging and wear or abnormal operating characteristics. The embodiment of Haynes et al. appears to assume a fixed frequency system with a signal-to-noise ratio that is high enough (that is, any interfering signals are low enough) for accurate signal detection. MOVs operate in highly-controlled environments with well-prescribed duty cycles. Typically, MOVs run steadily for long periods and do not experience operating conditions that generate confounding signals. More sophisticated processing techniques are desirable for general industrial environments and are particularly desirable for locomotive environments with varying load conditions and non-uniform track-related signals.
BRIEF SUMMARY
It would be advantageous to have a gear condition monitoring method and apparatus which can detect low-level incipient faults in the presence of varying load conditions and interfering signals.
According to one embodiment of the present invention, a gear transmission monitoring method includes: forming a good operating condition baseline matrix by, for each of a plurality of different gear mesh frequencies, obtaining a good operating condition signal indicative of gear transmission conditions over a segment of time and transforming the obtained good operating condition signal into a good operating condition time-frequency spectrum; and then obtaining a gear mesh frequency and a test signal over a segment of time, transforming the obtained test signal into a test time-frequency spectrum, and using the gear mesh frequency and the good operating condition baseline matrix to examine the test time-frequency spectrum to monitor gear transmission conditions.
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Kliman Gerald Burt
Reddy Suresh Baddam
Yazici Birsen
Agosti Ann M.
Hilten John S.
Patnode Patrick K.
Sun Xiuqin
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