Role of Signal Processing, Modeling and Decision Making in the Diagnosis of Rolling Element Bearing Defect: A Review

A Review on Self-Recovery Regulation (SR) Technique for Unbalance Vibration of High-End Equipment

“…According to the correlation analysis of Section 2.2, it is known that the premise of SR is to complete feature extraction and determine whether the unbalance vibration exceeds the standard value. Therefore, the feature extraction and processing of vibration signals is the first step of SR. At present, the methods of feature extraction are generally classified into 3 categories: Time domain type; Frequency domain type; Joint time-frequency type [25][26][27][28][29]. At the same time, with the promotion of intelligent algorithms, feature extraction technology based on intelligent algorithms is gradually becoming a research hotspot.…”

A self-Adaptive CNN with PSO for bearing fault diagnosis

“…Although CNN can auto extract features, to accelerate the evolution of PSO, signal processing is taken as a preprocessing method to extract basic time-frequency information. Among signal processing methods reviewed before, WT analysis is restricted by mutually selected wavelet basis function and the number of decomposition levels (Wang et al, 2018); Empirical wavelet transform (EWT) is restricted on choosing the boundaries of Fourier spectrum appropriately (Yi et al, 2016);HHT and EMD exists deficiencies such as model mixing and the end effect (Kumar & Kumar, 2019;Wang et al, 2018;Yi et al, 2016); VMD is not model-adaptive and heavily relies on the penalty parameter selection and the number of components (Wang et al, 2018;Yi et al, 2016); EEMD is computationally expensive and requires selection of noise parameter (Kumar & Kumar, 2019). Most of them are not adaptable and restricted on the selection of parameters.…”

“…(1) STFT is a simple and efficient method to transform time domain signals into time-frequency space. Its limitation is that it gives constant time and frequency resolution once the window size is fixed (Kumar & Kumar, 2019). Hence, the setting of the window size is important.…”

Discrete Wavelet Design for Bearing Fault Diagnosis Using Particle Swarm Optimization

“…However, time and frequency domain features have been shown to be relatively inappropriate for nonstationary signals which can be better processed using timefrequency domain techniques able to provide simultaneously, both time-domain and frequency-domain information [6]. To deal with non-stationary signals, including mechanical and bearing faulty signals, many time-frequency domains techniques have been used, such as the short-time Fourier transform (STFT), the Wigner-Ville distribution (WVD), the empirical mode decomposition (EMD), and the wavelet transform (WT) [7][8][9][10][11]. More specifically, the WT based approach, due to its effectiveness, has been widely and successfully applied in monitoring mechanical diagnosis problems and bearing fault detection.…”