The application of matching pursuit based on multi feature pattern set in the signal processing of rotating machinery

Abstract

The vibration signal containing the fault feature is buried by the strong background noise usually (the noise is referring to the other signals not containing the fault feature in the general sense) when a fault arises in rotating machinery, so the collected vibration signal needs to be processed normally in order to get a correct diagnosis result. Sparse representation theory is a relatively new signal processing method and matching pursuit (MP) is the classical sparse representation method. However, in the traditional MP method only one kind of atom dictionary is constructed to match the fault feature hidden in the vibration signal, which will cause an unsatisfactory analysis result usually when several different kinds of components arise in the vibration signal synchronously. This paper proposes a novel fault feature strengthening method basing on MP to solve the above problem, and the proposed method constructs several different kinds of atom dictionary based on a multi feature pattern set to match the multi-components buried in the collected vibration signal of rotating machinery. Firstly, the effectiveness and advantages of the proposed method are verified by kinds of typical simulation signals of rotating machinery. Then the proposed method is extended to preprocess the vibration signals of bearing and gearbox, and the corresponding analysis results prove that the proposed method has obviously a better fault feature extraction effect on the vibration signal of rotating machinery than the traditional MP method, and the proposed method lays the foundation for further correct fault analysis.

Multisource Fault Signal Separation of Rotating Machinery Based on Wavelet Packet and Fast Independent Component Analysis

“…In order to effectively identify the feature information contained in the fault signal of rotating machinery and reveal its inherent characteristics, many fault feature extraction methods of rotating machinery have been proposed, such as empirical mode decomposition (EMD) [7,8], mathematical morphology filtering [9,10], wavelet decomposition [11,12], adaptive filtering [13,14], matching pursuit [15,16], cyclostationary signal analysis [17,18], Wiener filter [19], Kalman filter [20,21], and stochastic resonance [22,23] that are widely used in early fault diagnosis of rotating machinery. The EMD proposed by Huang et al [7] is a nonstationary signal analysis method, which can find the hidden characteristic information in the signal, and has been widely used in the extraction and noise reduction of the impact signal of rotating machinery.…”

Fault Diagnosis of Rotating Machinery Based on Multi-Sensor Signals and Median Filter Second-Order Blind Identification (MF-SOBI)

“…Therefore, more accurate and comprehensive extraction of vibration signal characteristics has been the pursuit of researchers in this field. The methods of feature extraction of vibration signal include empirical mode decomposition (EMD) [6,7], minimum entropy deconvolution [8,9], an adaptive filter [10,11], matching tracking [12,13], mathematical morphology analysis [14][15][16], cyclostationary signal analysis [17,18], a Wiener filter [19,20], wavelet transform [21][22][23], a Kalman filter [24,25], and stochastic resonance [26,27]-all of which help with the development of mechanical fault diagnosis. The above methods can effectively eliminate background noise and interference components, and extract a fault signal in a specific environment, but are not suitable for complex interference situations.…”

Bearing performance degradation assessment based on topological representation and hidden Markov model

“…As to the first step, many signal processing methods have been proposed and applied (Cong et al, 2013; Ocak et al, 2007; Rai and Upadhyay, 2016; Wang and Sun, 2019). In general, extracted features can be divided into three categories: time-domain features, frequency-domain features, and time–frequency-domain features.…”