Diagnostics of reciprocating compressor fault based on a new envelope algorithm of empirical mode decomposition

Empirical mode decomposition (EMD), a self-adaptive time-frequency analysis methodology, is particularly suitable for processing the nonlinear and non-stationary time series, which can decompose a complicated signal into a series of intrinsic mode functions. Although it has the attractive features, the approach to construct the envelop-line in EMD has obvious shortcomings. A suggested improvement to EMD by adopting the optimized rational Hermite interpolation is proposed in this paper. In the proposed method, it adopts rational Hermite interpolation to compute the envelope-line, which has a shape controlling parameter compared with the cubic Hermite interpolation. In the meantime, one parameter determining criterion is introduced to guarantee the shape controlling parameter selection performs optimally. Besides the empirical envelope demodulation (EED) is introduced and utilized to analyze the IMFs derived from the improved EMD method. Hence, a new time-frequency method based on the optimized rational Hermite-based EMD combined with EED is proposed and the effectiveness was validated by the numerical simulations and an application to the reciprocating compressor fault diagnosis. The contributions of this paper are three aspects: Firstly, the definition of the best envelope is non-existent, some light is given about which envelope maybe better in this paper. Secondly, the optimal shape controlling parameter selection combined with rational Hermite interpolation is developed, leading to the significant performance enhancement. Thirdly, little research has been carried out on the fault diagnosis of the reciprocating compressor using EMD, the proposed method is a good start

Bearing fault diagnosis based on adaptive mutiscale fuzzy entropy and support vector machine

This paper proposes a new rolling bearing fault diagnosis method based on adaptive multiscale fuzzy entropy (AMFE) and support vector machine (SVM). Unlike existing multiscale Fuzzy entropy (MFE) algorithms, the scales of AMFE method are adaptively determined by using the robust Hermite-local mean decomposition (HLMD) method. AMFE method can be achieved by calculating the Fuzzy Entropy (FuzzyEn) of residual sums of the product functions (PFs) through consecutive removal of high-frequency components. Subsequently, the obtained fault features are fed into the multi-fault classifier SVM to automatically fulfill the fault patterns recognition. The experimental results show that the proposed method outperforms the traditional MFE method for the nonlinear and non-stationary signal analysis, which can be applied to recognize the different categories of rolling bearings

Preparation of Green Biosorbent using Rice Hull to Preconcentrate, Remove and Recover Heavy Metal and Other Metal Elements from Water

Sodium hydroxide treated rice hulls were investigated to preconcentrate, remove, and recover metal ions including Be2+, Al3+, Cr3+, CO2+, Ni2+, Cu2+, Zn2+, Sr2+, Ag+, Cd2+, Ba2+, and Pb2+ in both batch mode and column mode. Sodium hydroxide treatment significantly improved the removal efficiency for all metal ions of interest compared to the untreated rice hull. The removal kinetics were extremely fast for Co, Ni, Cu, Zn, Sr, Cd, and Ba, which made the treated rice hull a promising economic green adsorbent to preconcentrate, remove, and recover low-level metal ions in column mode at relatively high throughput. The principal removal mechanism is believed to be the electrostatic attraction between the negatively charged rice hulls and the positively charged metal ions. pH had a drastic impact on the removal for different metal ions and a pH of 5 worked best for most of the metal ions of interest. Processed rice hulls provide an economic alternative to costly resins that are currently commercially available products designed for metal ion preconcentration for trace metal analysis, and more importantly, for toxic heavy metal removal and recovery from the environment

Joint Scattering Environment Sensing and Channel Estimation Based on Non-stationary Markov Random Field

This paper considers an integrated sensing and communication system, where some radar targets also serve as communication scatterers. A location domain channel modeling method is proposed based on the position of targets and scatterers in the scattering environment, and the resulting radar and communication channels exhibit a two-dimensional (2-D) joint burst sparsity. We propose a joint scattering environment sensing and channel estimation scheme to enhance the target/scatterer localization and channel estimation performance simultaneously, where a spatially non-stationary Markov random field (MRF) model is proposed to capture the 2-D joint burst sparsity. An expectation maximization (EM) based method is designed to solve the joint estimation problem, where the E-step obtains the Bayesian estimation of the radar and communication channels and the M-step automatically learns the dynamic position grid and prior parameters in the MRF. However, the existing sparse Bayesian inference methods used in the E-step involve a high-complexity matrix inverse per iteration. Moreover, due to the complicated non-stationary MRF prior, the complexity of M-step is exponentially large. To address these difficulties, we propose an inverse-free variational Bayesian inference algorithm for the E-step and a low-complexity method based on pseudo-likelihood approximation for the M-step. In the simulations, the proposed scheme can achieve a better performance than the state-of-the-art method while reducing the computational overhead significantly.Comment: 15 pages, 13 figures, submitted to IEEE Transactions on Wireless Communication

Feedback between carbon and nitrogen cycles during the Ediacaran Shuram excursion

This research is supported by the National Natural Science Foundation of China (41872032, 41830215, 41930320) and the Chinese ‘111’ project (B20011).The middle Ediacaran Period records one of the deepest negative carbonate carbon isotope (δ13Ccarb) excursions in Earth history (termed the Shuram excursion). This excursion is argued by many to represent a large perturbation of the global carbon cycle. If true, this event may also have induced significant changes in the nitrogen cycle, because carbon and nitrogen are intimately coupled in the global ocean. However, the response of the nitrogen cycle to the Shuram excursion remains ambiguous. Here, we reported high resolution bulk nitrogen isotope (δ15N) and organic carbon isotope (δ13Corg) data from the upper Doushantuo Formation in two well-preserved sections (Jiulongwan and Xiangerwan) in South China. The Shuram-equivalent excursion is well developed in both localities, and our results show a synchronous decrease in δ15N across the event. This observation is further supported by bootstrapping simulations taking into account all published δ15N data from the Doushantuo Formation. Isotopic mass balance calculations suggest that the decrease in δ15N during the Shuram excursion is best explained by the reduction of isotopic fractionation associated with water column denitrification (εwd) in response to feedbacks between carbon and nitrogen cycling, which were modulated by changes in primary productivity and recycled nutrient elements through remineralization of organic matter. The study presented here thus offers a new perspective for coupled variations in carbon and nitrogen cycles and sheds new light on this critical time in Earth history.Publisher PDFPeer reviewe

Sphere forming mechanisms in vibration-assisted ball centreless grinding

This paper aims to clarify the sphere forming mechanisms in vibration-assisted ball centreless grinding, a new technique for effectively processing balls using ultrasonic vibrations. Based on a comprehensive analysis of the ball rotation motion, geometrical arrangement and stiffness of the whole grinding system, a reliable mechanics model was successfully developed for predicting the sphere forming process. Relevant experiments conducted showed that the model had captured the mechanics and the major sphere forming mechanisms in ball centreless grinding. It was found that the ball whole surface can be well ground with a high accuracy, while efficiency is much enhanced compared with that in the traditional methods. The ball rotational speed which is controlled by the ultrasonic regulator has a great impact on final sphericity, and the speed controlled by the ultrasonic shoe dominates the whole processing time. To achieve a stable and high precision grinding, the ball needs to rotate rhythmically, and the wheel feed per step and the ball location angle should be controlled in a critical range

Bearing fault diagnosis based on adaptive mutiscale fuzzy entropy and support vector machine

This paper proposes a new rolling bearing fault diagnosis method based on adaptive multiscale fuzzy entropy (AMFE) and support vector machine (SVM). Unlike existing multiscale Fuzzy entropy (MFE) algorithms, the scales of AMFE method are adaptively determined by using the robust Hermite-local mean decomposition (HLMD) method. AMFE method can be achieved by calculating the Fuzzy Entropy (FuzzyEn) of residual sums of the product functions (PFs) through consecutive removal of high-frequency components. Subsequently, the obtained fault features are fed into the multi-fault classifier SVM to automatically fulfill the fault patterns recognition. The experimental results show that the proposed method outperforms the traditional MFE method for the nonlinear and non-stationary signal analysis, which can be applied to recognize the different categories of rolling bearings