An improved extreme-point symmetric mode decomposition method and its application to rolling bearing fault diagnosis

HHT (Hilbert-Huang Transform) which consist of EMD (Empirical Mode Decomposition) and HT (Hilbert Transform) now is the most widely used time-frequency analysis technique for rolling element bearing fault diagnosis, however, its fault characteristic information extraction accuracy is usually limited due to the problem of mode mixing in EMD. ESMD (Extreme-point symmetric mode decomposition) is a novel development of HHT which is promising to alleviate this limitation and it has been applied to some fields successfully, but its application for rolling bearing fault diagnosis has rarely been seen in the literature. In this paper, ESMD is applied to extract the bearing fault characteristics for rolling bearing fault detection, and the results proved that ESMD can have a better fault diagnose effect than EMD and HT. What’s more, for further improving bearing fault characteristic extraction accuracy of rolling bearing vibration signals, the sifting scheme is proposed for selecting the sensitive fault-related IMFs (intrinsic mode functions) generated by ESMD, in which a weighted kurtosis index is introduced for automatic selection and reconstruction of the fault-related IMFs, and then the original and reconstructed bearing fault vibration signal after performing Hilbert transform as the results to diagnose the incipient rolling bearing fault. ESMD combined with the proposed sifting scheme are applied to diagnose the simulated and experimental signals, and the results confirmed that the sifting scheme based ESMD is superior to the other conventional method in rolling bearings fault diagnosis

EEMD Independent Extraction for Mixing Features of Rotating Machinery Reconstructed in Phase Space

Empirical Mode Decomposition (EMD), due to its adaptive decomposition property for the non-linear and non-stationary signals, has been widely used in vibration analyses for rotating machinery. However, EMD suffers from mode mixing, which is difficult to extract features independently. Although the improved EMD, well known as the ensemble EMD (EEMD), has been proposed, mode mixing is alleviated only to a certain degree. Moreover, EEMD needs to determine the amplitude of added noise. In this paper, we propose Phase Space Ensemble Empirical Mode Decomposition (PSEEMD) integrating Phase Space Reconstruction (PSR) and Manifold Learning (ML) for modifying EEMD. We also provide the principle and detailed procedure of PSEEMD, and the analyses on a simulation signal and an actual vibration signal derived from a rubbing rotor are performed. The results show that PSEEMD is more efficient and convenient than EEMD in extracting the mixing features from the investigated signal and in optimizing the amplitude of the necessary added noise. Additionally PSEEMD can extract the weak features interfered with a certain amount of noise

EEMD Independent Extraction for Mixing Features of Rotating Machinery Reconstructed in Phase Space

Empirical Mode Decomposition (EMD), due to its adaptive decomposition property for the non-linear and non-stationary signals, has been widely used in vibration analyses for rotating machinery. However, EMD suffers from mode mixing, which is difficult to extract features independently. Although the improved EMD, well known as the ensemble EMD (EEMD), has been proposed, mode mixing is alleviated only to a certain degree. Moreover, EEMD needs to determine the amplitude of added noise. In this paper, we propose Phase Space Ensemble Empirical Mode Decomposition (PSEEMD) integrating Phase Space Reconstruction (PSR) and Manifold Learning (ML) for modifying EEMD. We also provide the principle and detailed procedure of PSEEMD, and the analyses on a simulation signal and an actual vibration signal derived from a rubbing rotor are performed. The results show that PSEEMD is more efficient and convenient than EEMD in extracting the mixing features from the investigated signal and in optimizing the amplitude of the necessary added noise. Additionally PSEEMD can extract the weak features interfered with a certain amount of noise

Research on a Rotating Machinery Fault Prognosis Method Using Three-Dimensional Spatial Representations

Process models and parameters are two critical steps for fault prognosis in the operation of rotating machinery. Due to the requirement for a short and rapid response, it is important to study robust sensor data representation schemes. However, the conventional holospectrum defined by one-dimensional or two-dimensional methods does not sufficiently present this information in both the frequency and time domains. To supply a complete holospectrum model, a new three-dimensional spatial representation method is proposed. This method integrates improved three-dimensional (3D) holospectra and 3D filtered orbits, leading to the integration of radial and axial vibration features in one bearing section. The results from simulation and experimental analysis on a complex compressor show that the proposed method can present the real operational status and clearly reveal early faults, thus demonstrating great potential for condition-based maintenance prediction in industrial machinery

NSUN2-mediated m5C modification of HBV RNA positively regulates HBV replication.

Chronic hepatitis B virus (HBV) infection is a major cause of liver cirrhosis and liver cancer, despite strong prevention and treatment efforts. The study of the epigenetic modification of HBV has become a research hotspot, including the N6-methyladenosine (m6A) modification of HBV RNA, which plays complex roles in the HBV life cycle. In addition to m6A modification, 5-methylcytosine (m5C) is another major modification of eukaryotic mRNA. In this study, we explored the roles of m5C methyltransferase and demethyltransferase in the HBV life cycle. The results showed that m5C methyltransferase NSUN2 deficiency could negatively regulate the expression of HBV while m5C demethyltransferase TET2 deficiency positively regulates the expression of HBV. Subsequently, we combined both in vitro bisulfite sequencing and high-throughput bisulfite sequencing methods to determine the distribution and stoichiometry of m5C modification in HBV RNA. Two sites: C2017 and C131 with the highest-ranking methylation rates were identified, and mutations at these two sites could lead to the decreased expression and replication of HBV, while the mutation of the "fake" m5C site had no effect. Mechanistically, NSUN2-mediated m5C modification promotes the stability of HBV RNA. In addition, compared with wild-type HepG2-NTCP cells and primary human hepatocytes, the replication level of HBV after NSUN2 knockdown decreased, and the ability of the mutant virus to infect and replicate in wild-type HepG2-NTCP cells and PHHs was substantially impaired. Similar results were found in the experiments using C57BL/6JGpt-Nsun2+/- mice. Interestingly, we also found that HBV expression and core protein promoted the endogenous expression of NSUN2, which implied a positive feedback loop. In summary, our study provides an accurate and high-resolution m5C profile of HBV RNA and reveals that NSUN2-mediated m5C modification of HBV RNA positively regulates HBV replication by maintaining RNA stability