Characteristic analysis on the rubbing of rotor blade-casing of aero-engine based on Hilbert transform

A characteristic analytical method for the rubbing of aero-engine rotor blade-casing based on Hilbert Transform is proposed. The rotor experiment rig of aero-engine was used to simulate rubbing faults including single-point rub and local rub in the conditions of different casing thickness, different rubbing intensities, different rotational speeds and different rubbing positions. The casing acceleration signal was collected and subjected to the analysis by Hilbert envelope spectrum, and the result was compared with traditional spectrum analysis. The result indicates that the Hilbert envelope spectrum can effectively monitor the aero-engine running state in low frequency, and the method is more insensitive in sensor position, rotational speed, casing thickness and rubbing position. But the spectrum cannot efficiently monitor the aero-engine running state in low frequency

Understanding the Role of Bounty Awards in Improving Content Contribution: Bounty Amount and Temporal Scarcity

The bounty award system has been implemented on UGC platforms to address specific issues and improve content contributions. This study aims to assess its effectiveness by examining the bounty amount and temporal scarcity. Based on the optimistic bias theory, we posit that the competition for bounty awards among users can have a positive effect, as users may overestimate their chances of winning and persist in their efforts. Additionally, we hypothesize that the amount of bounty award does not have a linear effect on the quantity and quality of user-generated content, but instead follows an inverted U-shaped relationship. Furthermore, drawing on the stuck-in-the-middle (STIM) effect, we hypothesize that temporal scarcity influences contributors\u27 effort allocation in a U-shaped relationship. By exploring these hypotheses, we aim to advance the understanding of the underlying mechanisms of bounty awards and contribute to the development of effective peer incentive strategies

Aero-engine rotor-static rubbing characteristic analysis based on casing acceleration signal

The rotor experiment rig of aero-engine was used to simulate rubbing faults in different rotational speeds, rubbing intensities, rubbing positions and casing thickness. The casing acceleration signal was collected and subjected to the analysis by auto-correlation function frequency spectrum. The result indicates that the auto-correlation function frequency spectrum shows significant characteristic frequency in rubbing frequency (product between blade number and rotating frequency) and its integer multiple. The location of each characteristic frequency is characterized by band-frequency characteristic with rotating frequency as interval. The characteristic is not affected by sensor installed position, rotational speed, rubbing position and casing thickness

Characteristic analysis on the rubbing of rotor blade-casing of aero-engine based on Hilbert transform

A characteristic analytical method for the rubbing of aero-engine rotor blade-casing based on Hilbert Transform is proposed. The rotor experiment rig of aero-engine was used to simulate rubbing faults including single-point rub and local rub in the conditions of different casing thickness, different rubbing intensities, different rotational speeds and different rubbing positions. The casing acceleration signal was collected and subjected to the analysis by Hilbert envelope spectrum, and the result was compared with traditional spectrum analysis. The result indicates that the Hilbert envelope spectrum can effectively monitor the aero-engine running state in low frequency, and the method is more insensitive in sensor position, rotational speed, casing thickness and rubbing position. But the spectrum cannot efficiently monitor the aero-engine running state in low frequency

Using simulated Tianqin gravitational wave data and electromagnetic wave data to study the coincidence problem and Hubble tension problem

In this paper, we use electromagnetic wave data (H0LiCOW, $H(z)$, SNe) and gravitational wave data (Tianqin) to constrain the interacting dark energy (IDE) model and investigate the Hubble tension problem and coincidences problem. By combining these four kinds of data (Tianqin+H0LiCOW+SNe+$H(z)$), we obtained the parameter values at the confidence interval of $1\sigma$: $\Omega_m=0.36\pm0.18$, $\omega_x=-1.29^{+0.61}_{-0.23}$, $\xi=3.15^{+0.36}_{-1.1}$, and $H_0=70.04\pm0.42$ $kms^{-1}Mpc^{-1}$. According to our results, the best valve of $H_0$ show that the Hubble tension problem can be alleviated to some extent. In addition, the $\xi+3\omega_x = -0.72^{+2.19}_{-1.19}(1\sigma)$ of which the center value indicates the coincidence problem is slightly alleviated. However, the $\xi+3\omega_x = 0$ is still within the $1\sigma$ error range which indicates the $\Lambda$CDM model is still the model which is in best agreement with the observational data at present. Finally, we compare the constraint results of electromagnetic wave and gravitational wave on the model parameters and find that the constraint effect of electromagnetic wave data on model parameters is better than that of simulated Tianqin gravitational wave data.Comment: The article has been accepted by Chinese Physics

An analysis on the sensibility of casing vibration signal and its application to aero-hydraulic pump

Aero-hydraulic pump is a central part of hydraulic system in an aircraft. Acceleration sensors are installed in the axis, tangential and vertical direction for identifying the weak imbalance fault, and meanwhile analysis is made for the sensibility of weak imbalance fault from different direction acceleration signal. The result shows that the signal from vertical acceleration sensor is the most sensitive and the one from axis acceleration sensor is the least sensitive to identify and diagnose weak imbalance fault of aero-hydraulic pump

Condition trend prediction of aero-generator based on particle swarm optimization and fuzzy integral

In order to improve and enhance the prediction accuracy and efficiency of aero-generator running trend, grasp its running condition, and avoid accidents happening, in this paper, auto-regressive and moving average model (ARMA) and least squares support vector machine (LSSVM) which are used to predict its running trend have been optimized using particle swarm optimization (PSO) based on using features found in real aero-generator life test, which lasts a long period of time on specialized test platform and collects mass data that reflects aero-generator characteristics, to build new models of PSO-ARMA and PSO-LSSVM. And we use fuzzy integral methodology to carry out decision fusion of the predicted results of these two new models. The research shows that the prediction accuracy of PSO-ARMA and PSO-LSSVM has been much improved on that of ARMA and LSSVM, and the results of decision fusion based on fuzzy integral methodology show further substantial improvement in accuracy than each particle swarm optimized model. Conclusion can be drawn that the optimized model and the decision fusion method presented in this paper are available in aero-generator condition trend prediction and have great value of engineering application

Characteristic extraction of rolling bearing compound faults of aero-engine

Rolling bearing’s fault mode usually shows compound faults in aero-engine. The compound faults characteristics are more complex than single one, and many signal analysis methods have rather great limitation for compound fault characteristic extraction which leads to the difficulty to monitor the running state of rolling bearing in aero-engine. Based on above analysis, a method of combining wavelet transform with cyclostationary theory, autocorrelation function and Hilbert transform is proposed and applied to extract characteristic frequency of rolling bearing from compound faults mode only according to single-channel vibration acceleration signal of aero-engine. Meanwhile, a consideration is given to the influence of sensor installation position, compound fault types in the extraction of compound faults characteristics. The result indicates that the proposed new method can effectively monitor rolling bearing running state in four different compound fault modes just according to single-channel vibration acceleration signal no matter sensors are installed in horizontal or vertical direction

Optical biopsy identification and grading of gliomas using label-free visible resonance Raman spectroscopy.

Glioma is one of the most refractory types of brain tumor. Accurate tumor boundary identification and complete resection of the tumor are essential for glioma removal during brain surgery. We present a method based on visible resonance Raman (VRR) spectroscopy to identify glioma margins and grades. A set of diagnostic spectral biomarkers features are presented based on tissue composition changes revealed by VRR. The Raman spectra include molecular vibrational fingerprints of carotenoids, tryptophan, amide I/II/III, proteins, and lipids. These basic in situ spectral biomarkers are used to identify the tissue from the interface between brain cancer and normal tissue and to evaluate glioma grades. The VRR spectra are also analyzed using principal component analysis for dimension reduction and feature detection and support vector machine for classification. The cross-validated sensitivity, specificity, and accuracy are found to be 100%, 96.3%, and 99.6% to distinguish glioma tissues from normal brain tissues, respectively. The area under the receiver operating characteristic curve for the classification is about 1.0. The accuracies to distinguish normal, low grade (grades I and II), and high grade (grades III and IV) gliomas are found to be 96.3%, 53.7%, and 84.1% for the three groups, respectively, along with a total accuracy of 75.1%. A set of criteria for differentiating normal human brain tissues from normal control tissues is proposed and used to identify brain cancer margins, yielding a diagnostic sensitivity of 100% and specificity of 71%. Our study demonstrates the potential of VRR as a label-free optical molecular histopathology method used for in situ boundary line judgment for brain surgery in the margins

Fault diagnosis of aero-hydraulic pump based on casing vibration signal

To effectively extract the characteristics of weak imbalance fault of aircraft hydraulic pump, autocorrelation function (AF) is combined with wavelet transform (AFWT) instead of threshold denoising. Meanwhile, power ratio (PR) was obtained by extracted characteristic frequency and applied to the identification of weak imbalance fault. A contrastive analysis was conducted among different signals, including acceleration and displacement signal. The results indicate that displacement signal, rather than acceleration signal, can effectively identify a weak imbalance fault