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An extended recurrent neural network was proposed to predict the health condition of gears in Ref. [7].
The experimental analysis results show that the proposed approach, in which IMF AR model and SVM are combined, can identify working condition of gears with a success rate of 100% even in the case of smaller number of samples.
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This paper presents a condition based fault diagnoses technique to detect the condition of gear.
Four fault conditions of gears have been considered.
In the present work the non-linear coupled lateral torsional vibration model of rotor-bearing-gear coupling system is developed based on the engagement conditions of gear couplings.
Based on the kinematics of planar noncircular pinion and rack, the formation mechanism of pitch curves of the undulating face gear is given, with the continuous transmission condition of the gear set discussed.
Therefore, by comparing a given vibration signature to a number of template signatures (i.e., signatures from known gear conditions) it is possible to state which is the most likely condition of the gear under analysis.
defer.add img); Assess the condition of the gear.
Therefore, the AMMGDE filter is a very attractive technique for on-line condition monitoring of gear and other similar rotating machineries.
By means of this typical feature, Renyi entropy, on the basis of which a class of new diagnostic indices are derived in order to carry out condition monitoring of gear units, has introduced a generalized statistical moment concept along with its analytic expression.
For this reason, it is necessary to determine the working conditions of internal gears carefully.
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