Abstract
An extracted fault component of an abnormal sound is useful for faulty diagnosis. The existing fault component extracting approaches based on time–frequency analysis should filter the original signal to eliminate the background noise. However, these approaches will significantly change the fault component. In this article, a method for extracting the fault component of an abnormal sound signal is presented. This method is based on the linear superposition method and cross-correlation analysis. The method can eliminate the background noise and acquire the waveform of fault component. According to the feature of the shocking fault component, the acquired signal was intercepted into several segments, and the cross-correlation analysis was adopted to remove the wrong segment without the fault shocking component. The correct components were then linearly superposed together to eliminate the background noise. Finally, two experiments were performed to evaluate the effectiveness of the proposed fault component extracting method. The results show that the approach satisfactorily extracts the fault shocking component. The precise faulty component can be extracted by this method, which judges the engine condition precisely. The fault type can be diagnosed easily by this method. This method can be used in other fields to extract a particular component from a complicated signal.
Highlights
The acoustic and vibration signals of an engine often provide considerable dynamic information about an engine’s condition, which is a mixed signal that contains fault ingredient, other normal acoustic signals, and background noise
Fourier transform is a simple method for extracting the frequency feature of a fault engine acoustic signal or a vibration signal to diagnose the engine fault
A new extracting shocking fault signal method is proposed by cross-correlation analysis and linearly superposing several intercepted segments
Summary
The acoustic and vibration signals of an engine often provide considerable dynamic information about an engine’s condition, which is a mixed signal that contains fault ingredient, other normal acoustic signals, and background noise. The maximum value of the correlation coefficient and symmetry of cross-correlation function can be used to choose the intercepted segments with shocking fault components. When the intercepted segments contain a shocking fault component, the maximum value of crosscoefficient function is larger than others and the symmetry is perfect (Figure 6). The sum value between another extreme point’s value and the value of the corresponding background noise is larger than the extreme maximum point of the shocking fault component in this segment, leading to the wrong starting point being chosen. Cross-correlation coefficient function (each segment contain 250 sampling points, figure (a) and (b) respectively represent the cross-correlation coefficient functions between the first segment and the other nine segments intercepting from cylinder misfire fault signal and loose bearing cap bolt fault).
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