Effects of urea solution concentration on soil hydraulic properties and water infiltration capacity.

Elucidating the effect of fertigation on soil hydraulic parameters and water-solute transportation is fundamental to the design of farmland irrigation systems and their sustainable utilization. Few studies have focused on soil hydraulic parameters or water infiltration characteristics or how they are influenced by urea solution concentration. In this study, the clay loam and sandy loam in Yangling District of Shaanxi Province, China, were used as test soil, and experiments involving seven urea solution concentrations (0.2, 0.4, 0.6, 0.8, 1, 3, and 5 g/L) and a control treatment (0 g/L) were conducted to explore the influence of the various urea solution concentrations on soil hydraulic parameters and water infiltration characteristics. The results indicated that the cumulative infiltration and wetting front migration depth increased with urea solution concentration, as accurately estimated using the Kostiakov model and a power function, respectively. In addition, the coefficients of the Kostiakov model and the power function increased with urea solution concentration. Treatment with multiple concentrations of urea solution resulted in an increase in the volume of macro pores in the soil but a reduction in the volume of mesopores and micro pores in the soil, leading to increases in the saturated water content, saturated hydraulic conductivity, soil water diffusivity, and infiltration capacity and a reduction in the water-holding capacity of the soil. The effect of urea solute potential on the inhibition of soil water movement is small, and this inhibitory effect is far weaker than the improvement effect of the urea solution on soil structure, and hence enhance the soil water infiltration capacity. Our results increase the understanding of soil hydrological mechanisms and may be usefully applied for improving the management of fertigation.

Rolling Bearing Performance Degradation Assessment with Adaptive Sensitive Feature Selection and Multi-Strategy Optimized SVDD.

In light of the problems of a single vibration feature containing limited information on the degradation of rolling bearings, the redundant information in high-dimensional feature sets inaccurately reflecting the reliability of rolling bearings in service, and assessments of the degradation performance being disturbed by outliers and false fluctuations in the signal, this study proposes a method of assessing rolling bearings' performance in terms of degradation using adaptive sensitive feature selection and multi-strategy optimized support vector data description (SVDD). First, a high-dimensional feature set of vibration signals from rolling bearings was extracted. Second, a method combining the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and K-medoids was used to comprehensively evaluate the features with multiple evaluation indicators and to adaptively select better degradation features to construct the sensitive feature set. Next, multi-strategy optimization of the SVDD model was carried out by introducing the autocorrelation kernel regression (AAKR) and a multi-kernel function to improve the ability of the evaluation model to overcome outliers and false fluctuations. Through validation, it could be seen that the method in this study uses samples of rolling bearings in the healthy early stage to establish the evaluation model, which can adaptively determine the starting point of the bearing's degradation. The stability and accuracy of the model were effectively improved.

Remaining Useful Life Prediction of Rolling Bearings Using GRU-DeepAR with Adaptive Failure Threshold.

Aiming at the problem that a single neural network model has difficulty in accurately predicting trends of the remaining useful life of a rolling bearing, a method of predicting the remaining useful life of rolling bearings using a gated recurrent unit-deep autoregressive model (GRU-DeepAR) with an adaptive failure threshold was proposed. First, time domain and frequency domain features were extracted from the rolling bearing vibration signal. Second, its operation process was divided into a smooth operation stage and degradation stage according to the trend of the accumulated root mean square of maximum. Then, the failure threshold for different bearings were determined adaptively by the maximum of the smooth operation data. The degradation dataset of a rolling bearing was subsequently obtained. In the meantime, a GRU-DeepAR model was built to obtain predictions of the failure time and failure probability. Appropriate model parameters were determined after a large number of tests to assure the effectiveness and prediction accuracy. Finally, the trend of time series and failure times were predicted by inputting the degradation dataset into the GRU-DeepAR model. Experiments showed that the proposed method can effectively improve the accuracy of the remaining useful life prediction of a rolling bearing with good stability.