A New Neural-based Procedure For
Gear Fault Detection
Author(s)
A. Fernández del Rincón, F. Viadero Rueda, R. Sancibrián Herrera
& P. García Fernández
Abstract
In this work a new gear fault detection procedure based on the time synchronous
averaging (TSA) signal is proposed. In the first place an auto-associative neural
network (AANN) is used to obtain the relationship among the TSA signals from
several channels. During training the AANN is used to map a data vector with
itself so in this way the net provides practically the same output that arrives at
the input and the non-linear relationship between channels is embedded in net
weights and bias. In order to avoid a direct mapping between input and output,
one of the hidden layers must have a lower dimension than the input vector.
Once the AANN is trained it is used with new data registers as a linear prediction
error filter. If the new register contains the same data structure as the training set
the prediction error will be lower and the machine is working properly.
Otherwise, when the new register differs from the training set, as a consequence
of a fault, prediction error will be increased in each channel. At the end the
product of the error signal derivative for each channel is calculated using the
resulting vector for detection and diagnostic purposes. The possibility of
generalizing the net prediction capabilities using a training data set which
contains several load cases is also explored. The technique is validated using
experimental data taken on a laboratory gear set simulating several faults. An
example is presented in order to show the advantages of the application of the
proposed procedure comparing it with the most traditional gear processing tools
based on TSA as residual and regular signals.
Keywords: gear, neural networks, diagnostics, TSA, condition monitoring, data
fusion.
Keywords
gear, neural networks, diagnostics, TSA, condition monitoring, data
fusion.
Related Book
Other papers in this volume
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