A combined generalized Warblet transform and second order synchroextracting transform for analyzing nonstationary signals of rotating machinery.

In recent years, considerable attention has been paid in time-frequency analysis (TFA) methods, which is an effective technology in processing the vibration signal of rotating machinery. However, TFA techniques are not sufficient to handle signals having a strong non-stationary characteristic. To overcome this drawback, taking short-time Fourier transform as a link, a TFA methods that using the generalized Warblet transform (GWT) in combination with the second order synchroextracting transform (SSET) is proposed in this study. Firstly, based on the GWT and SSET theories, this paper proposes a method combining the two TFA methods to improve the TFA concentration, named GWT-SSET. Secondly, the method is verified numerically with single-component and multi-component signals, respectively. Quantized indicators, Rényi entropy and mean relative error (MRE) are used to analyze the concentration of TFA and accuracy of instantly frequency (IF) estimation, respectively. Finally, the proposed method is applied to analyze nonstationary signals in variable speed. The numerical and experimental results illustrate the effectiveness of the GWT-SSET method.

Modeling and prediction for diesel performance based on deep neural network combined with virtual sample.

The performance models are the critical step for condition monitoring and fault diagnosis of diesel engines, and are an important bridge to describe the link between input parameters and targets. Large-scale experimental methods with higher economic costs are often adopted to construct accurate performance models. To ensure the accuracy of the model and reduce the cost of the test, a novel method for modeling the performances of marine diesel engine is proposed based on deep neural network method coupled with virtual sample generation technology. Firstly, according to the practical experience, the four parameters including speed, power, lubricating oil temperature and pressure are selected as the input factors for establishing the performance models. Besides, brake specific fuel consumption, vibration and noise are adopted to assess the status of marine diesel engine. Secondly, small sample experiments for diesel engine are performed under multiple working conditions. Moreover, the experimental sample data are diffused for obtaining valid extended data based on virtual sample generation technology. Then, the performance models are established using the deep neural network method, in which the diffusion data set is adopted to reduce the cost of testing. Finally, the accuracy of the developed model is verified through experiment, and the parametric effects on performances are discussed. The results indicate that the overall prediction accuracy is more than 93%. Moreover, power is the key factor affecting brake specific fuel consumption with a weighting of 30% of the four input factors. While speed is the key factor affecting vibration and noise with a weighting of 30% and 30.5%, respectively.

Characterization of the deubiquitination activity and substrate specificity of the chicken ubiquitin-specific protease 1/USP associated factor 1 complex.

Deubiquitinases (DUBs) are essential regulators of intracellular processes involving ubiquitin (Ub) modification. The human DUB ubiquitin-specific protease 1 (hUSP1) interacts with human USP-associated factor 1 (hUAF1), and helps to regulate processes such as DNA damage repair. Previously, we identified a chicken USP1 homologue (chUSP1) during an investigation into the properties of Marek's disease virus (MDV). However, chUSP1's deubiquitination activity, interaction with chUAF1, and substrate specificity remained unknown. In the present study, we expressed and purified both chUAF1 and chUSP1 with or without putative catalytic core mutations using the Bac-to-Bac system, before investigating their deubiquitination activity and kinetics using various substrates. chUSP1 was shown to interact with chUAF1 both in cellular assays in which the two proteins were co-expressed, and in in vitro assays using purified proteins. Heterodimerization with chUAF1 increased the deubiquitination activity of chUSP1 up to 54-fold compared with chUSP1 alone. The chUSP1 mutants C91S, H603A, and D758A reduced the deubiquitination activity of the chUSP1/chUAF1 complex by 10-, 7-, and 33-fold, respectively, while the C91A and H594A chUSP1 mutants eliminated deubiquitination activity of the chUSP1/chUAF1 complex completely. This suggests that C91 and H594, but not D758, are essential for chUSP1 deubiquitination activity, and that a nucleophilic group at position 91 is needed for the deubiquitination reaction. The chUSP1/chUAF1 complex was found to have distinct substrate preferences; efficient hydrolysis of Ub dimers with K11-, K48-, and K63-linkages was seen, with weaker hydrolysis observed with K6-, K27-, and K33-linkages and no hydrolysis seen with a K29-linkage. Furthermore, other Ub-like substrates were disfavored by the complex. No activity was seen with SUMO1-GST, SUMO2- and SUMO3-dimers, ISG15-Rho, FAT10-Rho, or Ufm1-Rho, and only weak activity was observed with NEDD8-Rho. Overall, the data presented here characterize the activity and substrate preferences of chUSP1, and thus may facilitate future studies on its in vivo role.