| 论文简介 |
Recently, Instantaneous Angular Speed (IAS) measurement is successfully established and prevalently
applied to a wide variety of machines due to the hypothesis that the speed fluctuation of rotating machinery
carries plentiful dynamic responses. Nevertheless, exploration and application based on angular
signal are still insufficient. Under the same hypothesis, in this paper, we introduced an extended algorithm
named Instantaneous Angular Phase Demodulation (IAPD), together with the selection of optimal
sideband family to extract the Instantaneous Angular Displacement Fluctuation (IADF) signal. In order to
evaluate the performance of IADF signal, an effective approach was demonstrated using IADF signal to
address the fault detection and diagnosis issue. After extracting the IADF signal, a much effective method
was developed to deal with the large amount of data generated during the signal collection process.
Then, we used the well-developed techniques, i.e., empirical mode decomposition (EMD) and envelope
analysis, to undertake the signal de-noising and feature extraction task. The effectiveness and capability
of the IADF signal were evaluated by two kinds of gearboxes under differentconditions in practice. In
particular, the prevalent IAS signal and vibration signal were also involved and testified by the proposed
procedure. Experimental results demonstrated that by means of the IADF signal, the combination of EMD
and envelope analysis not only provided accurate identification results with a higher signal-to-noise
ratio, but was also capable of revealing the fault characteristics significantly and effectively. In
contrast, although the IAS signal had the potential ability to diagnose the serious fault, it failed for the
slight crack case. Moreover, the same procedure even its improvements, i.e., ensemble empirical mode
decomposition and local mean decomposition, all failed to recognize the faults in terms of vibration
signals.
© 2018 ISA. Published by Elsevier Ltd. All rights reserved. |
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