Abstract
The acoustic signal generated by mechanical motion contains the information of its motion state, but when the signal-to-noise ratio (SNR) is low, the accuracy of real-time monitoring mechanical motion state by the acoustic signal is low. This study proposes an adaptive noise reduction method based on the dislocation superposition method (DSM), which can realize the adaptive noise reduction and the extraction of fault a component from the automobile engine abnormal noise signal of low SNR. Firstly, the wavelet coefficients of engine abnormal noise signal are obtained by continuous wavelet transform (CWT), and the fault feature points of the abnormal noise signal in each period are extracted by setting hard threshold function, window function, and feature points extraction algorithm. Then, the signal segments containing fault components are obtained by using the position of feature points to extend the length of the fault component forward and backward, respectively, and Pearson’s correlation is calculated by traversal to determine the starting superposition point of each signal segment containing fault components. Finally, the signal segments of the odd group and even group are selected for superposition calculation. When the superposition stop condition is not satisfied, the number of superpositions increased until the stop condition is satisfied, and the superposition signal can be used as a fault component. The experimental results show that, compared with the improved DSM, this method has a good effect on the noise reduction and extraction of fault components of automobile engine cylinder knocking fault, and the effectiveness of this method is verified. This method is used to reduce the noise and extract the fault components of automobile engine cylinder missing fault and knock fault, and good results are obtained.
Highlights
At present, cars play an important role in human life and are a necessary condition for human travel. e engine is an important part of the automobile, and its structure is complex [1]
To overcome the above problems, this paper proposes an adaptive noise reduction and extraction method of engine abnormal noise signal fault components based on improved dislocation superposition method (DSM). e essence of this method is the superposition calculation in the time domain, which avoids the modal mixing and can better deal with the acoustic signal with low signal-to-noise ratio (SNR)
Many occasions are not suitable for the use of encoders, which limits the applicability of DSM. erefore, based on the improved DSM, this paper proposes an adaptive noise reduction method of automobile engine abnormal noise signal, which cancels the use of the encoder
Summary
Cars play an important role in human life and are a necessary condition for human travel. e engine is an important part of the automobile, and its structure is complex [1]. E experimental results show that the wavelet soft threshold denoising algorithm based on EMD decomposition has a better noise reduction effect when the centrifugal pump vibration signal is used as the noise reduction object. Ren and Liu [18] proposed an adaptive reduction noise and feature extraction algorithm based on improved EMD and verified the effectiveness and feasibility of the method by simulation signals and examples. The improved DSM can automatically extract the fault components of the automotive engine quasiperiodic signal, this method needs to use the pulse number of the encoder to determine the starting superposition point of each quasiperiodic fault signal. To overcome the above problems, this paper proposes an adaptive noise reduction and extraction method of engine abnormal noise signal fault components based on improved DSM. Compared with the improved DSM, it reduces the use of encoders and improves the practicability of DSM and makes it more convenient to extract fault components
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
