Rolling Bearing Fault Diagnosis Based on Support Vector Machine Optimized by Improved Grey Wolf Algorithm.

This study targets the low accuracy and efficiency of the support vector machine (SVM) algorithm in rolling bearing fault diagnosis. An improved grey wolf optimizer (IGWO) algorithm was proposed based on deep learning and a swarm intelligence optimization algorithm to optimize the structural parameters of SVM and improve the rolling bearing fault diagnosis. A nonlinear contraction factor update strategy was also proposed. The variable coefficient changes with the shrinkage factor α. Thus, the search ability was balanced at different early and late stages by controlling the dynamic changes of the variable coefficient. In the early stages of optimization, its speed is low to avoid falling into local optimization. In the later stages of optimization, the speed is higher, and finding the optimal solution is easier, balancing the two different global and local optimization capabilities to complete efficient convergence. The dynamic weight update strategy was adopted to perform position updates based on adaptive dynamic weights. First, the dataset of Case Western Reserve University was used for simulation, and the results showed that the diagnosis accuracy of IGWO-SVM was 98.75%. Then, the IGWO-SVM model was trained and tested using data obtained from the full-life-cycle test platform of mechanical transmission bearings independently researched and developed by Nanjing Agricultural University. The fault diagnosis accuracy and convergence value of the adaptation curve were compared with those of PSO-SVM (particle swarm optimization) and GWO-SVM diagnosis models. Results showed that the IGWO-SVM model had the highest rolling bearing fault diagnosis accuracy and the best diagnosis convergence.

Bayesian-Optimized Hybrid Kernel SVM for Rolling Bearing Fault Diagnosis.

We propose a new fault diagnosis model for rolling bearings based on a hybrid kernel support vector machine (SVM) and Bayesian optimization (BO). The model uses discrete Fourier transform (DFT) to extract fifteen features from vibration signals in the time and frequency domains of four bearing failure forms, which addresses the issue of ambiguous fault identification caused by their nonlinearity and nonstationarity. The extracted feature vectors are then divided into training and test sets as SVM inputs for fault diagnosis. To optimize the SVM, we construct a hybrid kernel SVM using a polynomial kernel function and radial basis kernel function. BO is used to optimize the extreme values of the objective function and determine their weight coefficients. We create an objective function for the Gaussian regression process of BO using training and test data as inputs, respectively. The optimized parameters are used to rebuild the SVM, which is then trained for network classification prediction. We tested the proposed diagnostic model using the bearing dataset of the Case Western Reserve University. The verification results show that the fault diagnosis accuracy is improved from 85% to 100% compared with the direct input of vibration signal into the SVM, and the effect is significant. Compared with other diagnostic models, our Bayesian-optimized hybrid kernel SVM model has the highest accuracy. In laboratory verification, we took sixty sets of sample values for each of the four failure forms measured in the experiment, and the verification process was repeated. The experimental results showed that the accuracy of the Bayesian-optimized hybrid kernel SVM reached 100%, and the accuracy of five replicates reached 96.7%. These results demonstrate the feasibility and superiority of our proposed method for fault diagnosis in rolling bearings.

Global practical tracking by output feedback for nonlinear systems with unknown growth rate and time delay.

This paper is the further investigation of work of Yan and Liu, 2011, and considers the global practical tracking problem by output feedback for a class of uncertain nonlinear systems with not only unmeasured states dependent growth but also time-varying time delay. Compared with the closely related works, the remarkableness of the paper is that the time-varying time delay and unmeasurable states are permitted in the system nonlinear growth. Motivated by the related tracking results and flexibly using the ideas and techniques of universal control and dead zone, an adaptive output-feedback tracking controller is explicitly designed with the help of a new Lyapunov-Krasovskii functional, to make the tracking error prescribed arbitrarily small after a finite time while keeping all the closed-loop signals bounded. A numerical example demonstrates the effectiveness of the results.

Biodegradation of Crude Oil by Nitrate-Reducing, Sulfate-Reducing, and Methanogenic Microbial Communities under High-Pressure Conditions.

Carbon capture, utilization, and storage (CCUS) is an important component in many national net-zero strategies, and ensuring that CO2 can be safely and economically stored in geological systems is critical. Recent discoveries have shown that microbial processes (e.g., methanogenesis) can modify fluid composition and fluid dynamics within the storage reservoir. Oil reservoirs are under high pressure, but the influence of pressure on the petroleum microbial community has been previously overlooked. To better understand microbial community dynamics in deep oil reservoirs, we designed an experiment to examine the effect of high pressure (12 megapascals [MPa], 60 °C) on nitrate-reducing, sulfate-reducing, and methanogenic enrichment cultures. Cultures were exposed to these conditions for 90 d and compared with a control exposed to atmospheric pressure (0.1 MPa, 60 °C). The degradation characteristic oil compounds were confirmed by thin-layer analysis of oil SARA (saturates, aromatics, resins, and asphaltenes) family component rods. We found that the asphaltene component in crude oil was biodegraded under high pressure, but the concentration of asphaltenes increased under atmospheric pressure. Gas chromatography analyses of saturates showed that short-chain saturates (C8-C12) were biodegraded under high and atmospheric pressure, especially in the methanogenic enrichment culture under high pressure (the ratio of change was -81%), resulting in an increased relative abundance of medium- and long-chain saturates. In the nitrate-reducing and sulfate-reducing enrichment cultures, long-chain saturates (C22-C32) were biodegraded in cultures exposed to high-pressure and anaerobic conditions, with a ratio of change of -8.0% and -2.3%, respectively. However, the relative proportion of long-chain saturates (C22-C32) increased under atmospheric pressure. Gas Chromatography Mass Spectrometry analyses of aromatics showed that several naphthalene series compounds (naphthalene, C1-naphthalene, and C2-naphthalene) were biodegraded in the sulfate-reducing enrichment under both atmospheric pressure and high pressure. Our study has discerned the linkages between the biodegradation characteristics of crude oil and pressures, which is important for the future application of bioenergy with CCUS (bio-CCUS).

Metabolic profiling of petroleum-degrading microbial communities incubated under high-pressure conditions.

While pressure is a significant characteristic of petroleum reservoirs, it is often overlooked in laboratory studies. To clarify the composition and metabolic properties of microbial communities under high-pressure conditions, we established methanogenic and sulfate-reducing enrichment cultures under high-pressure conditions using production water from the Jilin Oilfield in China. We utilized a metagenomics approach to analyze the microbial community after a 90-day incubation period. Under methanogenic conditions, Firmicutes, Deferribacteres, Ignavibacteriae, Thermotogae, and Nitrospirae, in association with the hydrogenotrophic methanogen Archaeoglobaceae and acetoclastic Methanosaeta, were highly represented. Genomes for Ca. Odinarchaeota and the hydrogen-dependent methylotrophic Ca. Methanosuratus were also recovered from the methanogenic culture. The sulfate-reducing community was dominated by Firmicutes, Thermotogae, Nitrospirae, Archaeoglobus, and several candidate taxa including Ca. Bipolaricaulota, Ca. Aminicenantes, and Candidate division WOR-3. These candidate taxa were key pantothenate producers for other community members. The study expands present knowledge of the metabolic roles of petroleum-degrading microbial communities under high-pressure conditions. Our results also indicate that microbial community interactions were shaped by syntrophic metabolism and the exchange of amino acids and cofactors among members. Furthermore, incubation under in situ pressure conditions has the potential to reveal the roles of microbial dark matter.

Adaptive Neural Output-Feedback Controller Design of Switched Nonlower Triangular Nonlinear Systems With Time Delays.

In this article, we study the issue of adaptive neural output-feedback controller design for a class of uncertain switched time-delay nonlinear systems with nonlower triangular structure. The prominent contribution of this article is that the delay-dependent stability criterion of nonswitched nonlinear systems is successfully extended to that of switched nonlower triangular nonlinear systems. The design algorithm is listed as follows. First, a switched state observer is designed such that the error dynamic system can be generated. Second, neural networks, adaptive backstepping technique, and variable separation method are, respectively, applied to construct a common controller for all subsystems, in which the Lyapunov-Krasovskii functionals are deliberately constructed such that the average dwell-time scheme can be employed to guarantee the stability and performance of the closed-loop system, despite the existence of time delays. Third, the stability analysis process confirms in detail that all the variables of the closed-loop system are semiglobally uniformly ultimately bounded. Finally, simulation study is given to show the validity of the proposed control approach.

Linear estimation for discrete-time periodic systems with unknown measurement input and missing measurements.

This paper considers the estimation problem for periodic systems with unknown measurement input and missing measurements. The missing measurements phenomenon is described by an independent and identically distributed Bernoulli process. The quality of the estimation achieved by an admissible filter is measured by a performance criterion described by the Cesaro limit of the mean square of the deviation between the remote signal and the estimated signal. By employing the minimum variance unbiased estimation technique, the periodic unbiased estimator is obtained, where the estimator gain is designed in terms of the unique periodic solution of a Lyapunov equation together with the periodic stabilizing solution of a Riccati equation. Finally, a numerical example is provided to show the effectiveness of the proposed estimation approach.

Diverse Bacteria with Lignin Degrading Potentials Isolated from Two Ranks of Coal.

Taking natural coal as a "seed bank" of bacterial strains able to degrade lignin that is with molecular structure similar to coal components, we isolated 393 and 483 bacterial strains from a meager lean coal sample from Hancheng coalbed and a brown coal sample from Bayannaoer coalbed, respectively, by using different media. Statistical analysis showed that isolates were significantly more site-specific than medium-specific. Of the 876 strains belonging to 27 genera in Actinobacteria, Firmicutes, and Proteobacteria, 612 were positive for lignin degradation function, including 218 strains belonging to 35 species in Hancheng and 394 strains belonging to 19 species in Zhongqi. Among them, the dominant lignin-degrading strains were Thauera (Hancheng), Arthrobacter (Zhongqi) and Rhizobium (both). The genes encoding the laccases- or laccase-like multicopper oxidases, key enzymes in lignin production and degradation, were detected in three genera including Massila for the first time, which was in high expression by real time PCR (qRT-PCR) detection, confirming coal as a good seed bank.

Accessibility of primary health care workforce in rural China.

This study aims to develop an accessibility index to illustrate the status of the accessibility of primary health care workers in remote and rural areas in China. Relevant county-level data were used to analyze the number and distribution of primary health care workforce in rural China, and relevant provincial-level data were used to analyze the accessibility index. The study found that the development of rural primary health care workers is suboptimal. The rural Primary Health Care Worker Accessibility index shows that the accessibility to primary health care workers in western rural areas is poor. The correlation between PHCWA index and maternal mortality rate is more significant than that between primary health care workers density and maternal mortality rate. In addition to increasing the number of primary health care workers, strategies addressing the challenge of distance are also required.