No abstract

No abstract

No abstract

No abstract

The solubilities of 1-(2-bromo-phenyl)-pyrrole-2,5-dione in 14 pure solvents including isopropanol, methanol, isobutanol, ethanol, n-propanol, n-butanol, ethylene glycol (EG), cyclohexane, acetone, acetonitrile, N-methyl-2-pyrrolidinone (NMP), N,N-dimethyl formamide (DMF), water, and dimethylsulfoxide (DMSO) were determined through the shake-flask method over the temperature range from 278.15 to 323.15 K under 101.2 kPa. The values of the mole fraction solubility of 1-(2-bromo-phenyl)-pyrrole-2,5-dione in above pure solvents increased as the temperature increased and presented the following order in different pure solvents: DMF > NMP > DMSO > acetone > acetonitrile > methanol > ethanol > isopropanol > n-propanol > isobutanol > n-butanol > EG > cyclohexane > water. They were mathematically correlated by the NRTL model, λh equation, Wilson model, and Apelblat equation. The obtained values of the maximum root-mean-square deviation and relative average deviation were, respectively, 51.62 × 10–4 and 4.19 × 10–2. By the relationship examination of the Kamlet and Taft linear solvation energy of the solvent effect, the type and degree of the solvent–solvent and solute–solvent interactions were identified. In addition, the mixing properties, reduced excess enthalpy, and activity coefficient under infinitesimal concentrations were obtained in terms of the Wilson model.

No abstract

The fault characteristic signals of rolling bearings are coupled with each other, thus increasing the difficulty in identifying the fault characteristics. In this study, a fault diagnosis method of rolling bearing based on least squares support vector machine is proposed. First, least squares support vector machine model is trained with the samples of known classes. Least squares support vector machine algorithm involves the selection of a kernel function. The complexity of samples in high-dimensional space can be adjusted through changing the parameters of kernel function, thus affecting the search for the optimal function as well as final classification results. Particle swarm optimization and 10-fold cross-validation method are adopted to optimize the parameters in the training model. Then, with the optimized parameters, the classification model is updated. Finally, with the feature vector of the test samples as the input of least squares support vector machine, the pattern recognition of the testing samples is performed to achieve the purpose of fault diagnosis. The actual bearing fault data are analyzed with the diagnosis method. This method allows the accurate classification results and fast diagnosis and can be applied in the diagnosis of compound faults of rolling bearing.

No abstract