Weighted envelope spectrum based on the spectral coherence for bearing diagnosis.

The key idea behind demodulation analysis for bearing diagnosis is to determine the fault-induced frequency band and directly detect the potential bearing fault characteristic frequency (FCF) in the demodulated spectrum. Till now, most demodulation methods are based on the optimal selection of only one informative frequency band. However, the unwanted in-band noise will be retained or some fault information may be ignored in the case of the discrete resonant frequency band or multiple informative frequency bands. To address the issue, a FCF-oriented criterion is proposed to determine all the informative frequency bands rather than only one specified frequency band. A new weighting vector is obtained to control the contribution of each spectral frequency in the demodulated spectrum. Subsequently, a weighted envelope spectrum (WES) is introduced by integrating the spectral correlation over the full spectral frequency band and assigning the new weighting vector on each spectral frequency. In this way, all frequency components with fault information are enhanced while other components are inhibited. Furthermore, expanded to the diagnosis of compound-fault, the FCF-oriented criterion can provide the different weighting vectors relevant to the different potential faults, and the separated fault features can be identified directly in the generated WESs. Finally, the advantages of WES over the traditional methods are testified by the simulated signal and experimental data.

Bacteriophage SRD2021 Recognizing Capsular Polysaccharide Shows Therapeutic Potential in Serotype K47 Klebsiella pneumoniae Infections.

Klebsiella pneumoniae is an opportunistic pathogen posing an urgent threat to global public health, and the capsule is necessary for K. pneumoniae infection and virulence. Phage-derived capsule depolymerases have shown great potential as antivirulence agents in treating carbapenem-resistant K. pneumoniae (CRKP) infections. However, the therapeutic potential of phages encoding depolymerases against CRKP remains poorly understood. In this study, we identified a long-tailed phage SRD2021 specific for mucoid CRKP with capsular K47 serotype, which is the predominant infectious K-type in Asia. Genome sequencing revealed that ΦSRD2021 belonged to the Drulisvirus genus and exhibited a capsular depolymerase domain in its tail fiber protein. A transposon-insertion library of host bacteria was constructed to identify the receptor for ΦSRD2021. We found that most phage-resistant mutants converted to a nonmucoid phenotype, including the mutant in wza gene essential for capsular polysaccharides export. Further knockout and complementation experiments confirmed that the Δwza mutant avoided adsorption by ΦSRD2021, indicating that the K47 capsular polysaccharide is the necessary receptor for phage infection. ΦSRD2021 lysed the bacteria mature biofilms and showed a therapeutic effect on the prevention and treatment of CRKP infection in the Galleria mellonella model. Furthermore, ΦSRD2021 also reduced the colonized CRKP in mouse intestines significantly. By recognizing the host capsule as a receptor, our results showed that ΦSRD2021 may be used as a potential antibacterial agent for K47 serotype K. pneumoniae infections.

Period-oriented multi-hierarchy deconvolution and its application for bearing fault diagnosis.

Deconvolution methods have been proven to be effective tools to extract excitation sources from the noisy measured signal. However, its application is confined by the extraction of incomplete information. To tackle this problem, a new deconvolution method, named period-oriented multi-hierarchy deconvolution (POMHD) is proposed in this paper. Various filters are designed adaptively by the iterative algorithm to update the filter coefficient using the harmonic-to-noise ratio as the deconvolution orientation. Additionally, a novel index, called normalized proportion of harmonics, is proposed as the evaluation criteria for the fault feature. Based on upon, a harmonics proportion diagram is constructed for the diagnostic decisions. The new deconvolution method overcomes the disadvantages of the traditional methods. More importantly, without an accurate fault period as the prior knowledge, the proposed POMHD can simultaneously extract multiple latent fault components by using the adaptive filter and intuitively present different fault information in one diagram. Finally, the simulated and experimental data which includes the signals collected from bearings with both single faults and compound faults is used to evaluate the new method. The results validate the feasibility and robustness of the proposed POMHD.

Application of sparsity-oriented VMD for gearbox fault diagnosis based on built-in encoder information.

Encoder signal as the built-in information is always used for the speed and motion control. Meanwhile, it has remarkable superiority in the fault diagnosis of gearbox compared with the popular vibration signal. Traditional decomposition method, such as EMD, gradually loses competitiveness with the increase of the complexity of the encoder signal. To solve the problem, with aid of the unique characteristic of encoder signal and the decomposition performance of variational mode decomposition (VMD), a new sparsity-oriented VMD (SOVMD), is originally designed and initially introduced for encoder signal analysis in this paper. Firstly, SOVMD is free from the selection of mode number and initial center frequency (ICF), which troubles seriously the application of VMD. Since a prior ICF which coarsely indicates the location of the fault band can enhance the decomposing efficiency of VMD, ICF = 0 is more appropriate and easier for the extraction of fault information concentrated in the low frequency region. Benefiting from the characteristics of distribution, the optimization of the mode number is unnecessary since the fault mode will generate in the first mode. Secondly, with the proposed selection criterion of the balance parameter, SOVMD can decompose the mode with most fault information more effectively and accurately. Furthermore, a sparsity operation which is originally designed for the encoder signal analysis can further suppress noise and enhance the fault impulses. Through the simulation and experimental cases from the planet gearbox bench, the feasibility and effectiveness of SOVMD can be verified. Therefore, it is reasonable to conclude that the proposed SOVMD is an alternative scheme for gearbox fault diagnosis based on built-in encoder information.

Identification of mechanical compound-fault based on the improved parameter-adaptive variational mode decomposition.

Parameter-adaptive variational mode decomposition (VMD) has attenuated the dominant effect of prior parameters, especially the predefined mode number and balancing parameter, which heavily trouble the traditional VMD. However, parameter-adaptive VMD still encounters some problems when it is applied to the data from industry applications. On one hand, the mode number chosen using parameter-adaptive VMD is not the optimal. Numbers of redundant modes are decomposed. On another hand, parameter-adaptive VMD has much space for the improvement when it is applied to compound-fault diagnosis. To solve these issues and further enhance its performance, an improved parameter-adaptive VMD (IPAVMD) is proposed in this paper. Firstly, a new index, called ensemble kurtosis, is constructed by combining with kurtosis and the envelope spectrum kurtosis. It can simultaneously take the cyclostationary and impulsiveness into consideration. Secondly, the optimization objective function of grasshopper optimization algorithm is improved based on the ensemble kurtosis. The improved method chooses the mean value of the ensemble kurtosis of all modes rather than that of the individual mode as objective function. Thirdly, to extract all potential fault information, an iteration algorithm is used in the new method. Benefiting from these improvements, the proposed IPAVMD outperforms the traditional parameter-adaptive VMD and further expands the application to compound-fault diagnosis. It has been verified by a series of simulated signals and a real dataset from the axle box bearings of locomotive.

Feature Mining and Health Assessment for Gearboxes Using Run-Up/Coast-Down Signals.

Vibration signals measured in the run-up/coast-down (R/C) processes usually carry rich information about the health status of machinery. However, a major challenge in R/C signals analysis lies in how to exploit more diagnostic information, and how this information could be properly integrated to achieve a more reliable maintenance decision. Aiming at this problem, a framework of R/C signals analysis is presented for the health assessment of gearbox. In the proposed methodology, we first investigate the data preprocessing and feature selection issues for R/C signals. Based on that, a sparsity-guided feature enhancement scheme is then proposed to extract the weak phase jitter associated with gear defect. In order for an effective feature mining and integration under R/C, a generalized phase demodulation technique is further established to reveal the evolution of modulation feature with operating speed and rotation angle. The experimental results indicate that the proposed methodology could not only detect the presence of gear damage, but also offer a novel insight into the dynamic behavior of gearbox.