Although traditional waveform analysis in time domainplays an important role in realizing engine non-disintegration fault diagnosis, this method fails to make an accurate fault diagnosis when fault waveform and normal waveform are very close. To solve this problem, a new method based on frequency space subdivision (FSS) in wavelet transform (WT) is proposed and applied in this paper. Meanwhile, a processing approach of engine data stream is introduced, which makes further waveform analysis possible. This method is applied to an injector-pulse-width waveform analysis. As for the No. 12 fault analysis, firstly a biorthogonal wavelet base with good characteristics is selected, then three-layer wavelet decomposition is used to analyze injector-pulse-width in both time domain and frequency domain , and finally the accurate fault band is located through calculation of the sum of the difference between fault and normal wavelet coefficients. The result obtains that the fault comes from the oxygen sensor, which is completely coincident with the experimental fault hypothesis. Injector-pulse-width waveform of No.1, 8, 11 and 19 faults are also analyzed similarly. The results show that the proposed waveform analysis method improves the accuracy of the engine fault diagnosis. This method provides a supplement for the known non-disintegration engine fault diagnosis and supplies a good reference for fault diagnosis of the other large machines.
Traditional engine waveform analysis in time-domain fails to perform an accurate fault diagnosis when the fault waveform is very close to the normal waveform in time domain. A novel engine waveform analysis method is presented. In this paper, the aim is to perform fault diagnosis efficiently under such circumstances. This method is proposed by combining a new technique, called sensitive frequency band (SFB) selection, with the developed Hilbert-Huang transform (HHT). This can alleviate "mode mixing" by removing noise from the engine waveforms and reveal the time-frequency characteristics for a signal by deriving its time-frequency spectrum (TFS) distribution. The method is then applied to analyze the engine injector-pulse-width waveforms, and it works well for signal noise reduction and fault diagnosis.
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