Self-matching extraction fractional wavelet transform for mechanical equipment fault diagnosis

Abstract

Time-frequency analysis (TFA) of vibration signals is an important way for fault diagnosis and predictive maintenance of mechanical equipment. However, the existing TFA methods, such as wavelet transform (WT) and synchrosqueezing transform, are incapable to describe local details of strongly time-varying signals, bringing such problems as time-frequency energy dispersion. Theoretically, it is more beneficial for time-varying band-limited signals to make multi-resolution analysis in the time-fraction frequency domain because such action can increase the selection chances of mother wavelets in any direction on the time-frequency plane by changing the angle α . In order to improve the ability of analyzing strongly time-varying signals and the readability of the time-frequency plane, this paper has proposed a novel TFA method called self-matching extraction fractional WT (SMEFRWT). Firstly, within the theoretical framework of fractional WT (FRWT), a minimum entropy-based method is proposed to choose an optimal angle in the fractional domain. Such action can obtain the best aggregation angle of time-frequency energy and facilitate the separation of multi-component signals. Then, self-matching extraction operator, one instantaneous-frequency estimated operator that match the time-frequency structure of amplitude modulation signals, is constructed. The operator allows for the distribution of time-frequency energy of fast-changing signals in both frequency and time directions, making it possible to match the time-varying vibration signals from mechanical equipment more accurately. Finally, SMEFRWT is adopted to make TFA and extract fault features of the faulty experiment benches on gear and bearings, with its feasibility validated by experimental results.

Multi-rolling element faults diagnosis of rolling bearing based on time-frequency analysis and multi-curves extraction

“…] for candidate points is calculated by iterating along the frequency axis from f 1 to f n , and the values of AF [. ] are accumulated as equation (25):…”

“…As shown in figure 3(a), the circles represent the local peak points in the TFR. When the algorithm calculates the frequency f s , it computes the S value between the solid circle and the candidate points using equation (25) and calculates the corresponding path index r using equation (26), represented by the solid arrows below the black solid circle in figure 3(a).…”

“…To enhance fault diagnosis accuracy, the time-frequency (TF) analysis (TFA) technique can extract comprehensive characteristic information from vibration signals [24]. Diagnosing multi-rolling element faults using only TFA, such as wavelet transform (WT) [25], short-time Fourier transform (STFT) [26], Wigner-Ville distribution (WVD) [27], and some improved TFA methods [28][29][30], is challenging.…”

Gearbox fault diagnosis method based on lightweight channel attention mechanism and transfer learning

“…The key points of gearbox fault diagnosis methods are signal feature extraction and fault pattern recognition. The main methods for feature extraction embrace Short Time Fourier Transform (STFT) 3 , Variational Mode Decomposition (VMD) 4 and Wavelet Transform (WT) 5 , etc. Traditional pattern recognition algorithms mainly include Support Vector Machine (SVM) 6 , Sparse Representation Classification (SRC) 7 , Artificial Neural Network (ANN) 8 , etc.…”