Degradation Trend Prediction of Pumped Storage Unit Based on MIC-LGBM and VMD-GRU Combined Model

The harsh operating environment aggravates the degradation of pumped storage units (PSUs). Degradation trend prediction (DTP) provides important support for the condition-based maintenance of PSUs. However, the complexity of the performance degradation index (PDI) sequence poses a severe challenge of the reliability of DTP. Additionally, the accuracy of healthy model is often ignored, resulting in an unconvincing PDI. To solve these problems, a combined DTP model that integrates the maximal information coefficient (MIC), light gradient boosting machine (LGBM), variational mode decomposition (VMD) and gated recurrent unit (GRU) is proposed. Firstly, MIC-LGBM is utilized to generate a high-precision healthy model. MIC is applied to select the working parameters with the most relevance, then the LGBM is utilized to construct the healthy model. Afterwards, a performance degradation index (PDI) is generated based on the LGBM healthy model and monitoring data. Finally, the VMD-GRU prediction model is designed to achieve precise DTP under the complex PDI sequence. The proposed model is verified by applying it to a PSU located in Zhejiang province, China. The results reveal that the proposed model achieves the highest precision healthy model and the best prediction performance compared with other comparative models. The absolute average (|AVG|) and standard deviation (STD) of fitting errors are reduced to 0.0275 and 0.9245, and the RMSE, MAE, and R2 are 0.00395, 0.0032, and 0.9226 respectively, on average for two operating conditions.

Evaluating the Degradation of Natural Resources in the Mediterranean Environment Using the Water and Land Resources Degradation Index, the Case of Crete Island

Natural resources degradation poses multiple challenges particularly to environmental and economic processes. It is usually difficult to identify the degree of degradation and the critical vulnerability values in the affected systems. Thus, among other tools, indices (composite indicators) may also describe these complex systems or phenomena. In this approach, the Water and Land Resources Degradation Index was applied to the fifth largest Mediterranean island, Crete, for the 1999–2014 period. The Water and Land Resources Degradation Index uses 11 water and soil resources related indicators: Aridity Index, Water Demand, Drought Impacts, Drought Resistance Water Resources Infrastructure, Land Use Intensity, Soil Parent Material, Plant Cover, Rainfall, Slope, and Soil Texture. The aim is to identify the sensitive areas to degradation due to anthropogenic interventions and natural processes, as well as their vulnerability status. The results for Crete Island indicate that prolonged water resources shortages due to low average precipitation values or high water demand (especially in the agricultural sector), may significantly affect Water and Land degradation processes. Hence, Water and Land Resources Degradation Index could serve as an extra tool to assist policymakers to improve their decisions to combat Natural Resources degradation.

Soil Erosion Susceptibility Prediction in Railway Corridors Using RUSLE, Soil Degradation Index and the New Normalized Difference Railway Erosivity Index (NDReLI)

This study presents a remote sensing-based index for the prediction of soil erosion susceptibility within railway corridors. The empirically derived index, Normalized Difference Railway Erosivity Index (NDReLI), is based on the Landsat-8 SWIR spectral reflectances and takes into account the bare soil and vegetation reflectances especially in semi-arid environments. For the case study of the Botswana Railway Corridor (BRC), the NDReLI results are compared with the RUSLE and the Soil Degradation Index (SDI). The RUSLE model showed that within the BRC, the mean annual soil loss index was at 0.139 ton ha−1 year−1, and only about 1% of the corridor area is susceptible to high (1.423–3.053 ton ha−1 year−1) and very high (3.053–5.854 ton ha−1 year−1) soil loss, while SDI estimated 19.4% of the railway corridor as vulnerable to soil degradation. NDReLI results based on SWIR1 (1.57–1.65 μm) predicted the most vulnerable areas, with a very high erosivity index (0.36–0.95), while SWIR2 (2.11–2.29 μm) predicted the same regions at a high erosivity index (0.13–0.36). From empirical validation using previous soil erosion events within the BRC, the proposed NDReLI performed better that the RUSLE and SDI models in the prediction of the spatial locations and extents of susceptibility to soil erosion within the BRC.

Environmental degradation and agriculture: an approach in countries by middle of indexes

ABSTRACT: Society evolution is commonly followed by changes; however, some of them bring negative implications for the community. One of these consequences refers to environmental degradation, which has agricultural activity as one of its influencing agents, which is essentially characterized by man’s predatory actions. Accordingly, this research analyzed the environmental degradation in 167 pattern in the agricultural world. Therefore, the Agricultural Environmental Degradation Index (IDAA) was used as a proxy for agricultural environmental degradation and the factor analysis technique. Results indicated that the most degraded country was Russia, which belongs to the European continent; however, the other positions were occupied predominantly by Africa, followed by North America and Oceania. Issues such as rural poverty and primitive natural settings can leverage this phenomenon. The lowest rates of degradation were concentrated on Central America and Europe, where agricultural activity was most incipient. In this sense, a directly proportional relationship between environmental degradation and agricultural practice was reported considering that countries dependent on this phenomenon had the most worrying results. Thereby, there is an emerging need for public policies that integrate economic and environmental dimensions that reduce negative impacts in the regions most degraded.

Validation of the procedure: Quantification of the degradation index of Photovoltaic Grid Connection Systems

The results obtained from the validation of the procedure ‟Quantification of the degradation index of Photovoltaic Grid Connection Systems” are presented, using statistical parameters, which corroborate its accuracy, achieving a coefficient of determination of 0.9896, a percentage of the root of the mean square of the error RMSPE = 1.498% and a percentage of the mean absolute error MAPE = 1.15%, evidencing the precision of the procedure.

A Novel Approach of Label Construction for Predicting Remaining Useful Life of Machinery

Rolling bearings are key components of rotating machinery, and predicting the remaining useful life (RUL) is of great significance in practical industrial scenarios and is being increasingly studied. A precise and reliable remaining useful life prediction result provides valuable information for decision-makers, which is essential to ensure the safety and reliability of mechanical systems. Generally, the RUL label is considered to be an ideal life curve, which is the benchmark for RUL prediction. However, the existing label construction methods make more use of expert experience and seldom mine knowledge from data and combine experience to assist in constructing a health index (HI). In this paper, a novel and simple approach of label construction is proposed for predicting the RUL accurately. More specifically, the degradation index of the multiscale frequency domain is first extracted. Furthermore, the fuzzy C-means (FCM) algorithm is innovatively used to divide the degradation data into several stages to obtain the turning point of degradation. Then, a nonlinear degradation index, the RUL label with the turning point, was constructed based on principal component analysis (PCA). Finally, the recurrent neural network (RNN) is used for prediction and verification. In order to verify the effectiveness of the proposed approach, two different bearing lifecycle datasets are gathered and analyzed. The analysis result confirms that the proposed method is able to achieve a better performance, which outperforms some existing methods.

Prediction of land degradation by Machine Learning Methods

In this study, three models were used to monitor and predict the GWL and the land degradation index via the IMDPA method. In all models, 70% of the data was applied for training, while 30% of data were employed for testing and validation. Monthly rainfall, TWI index, the distance of the river, Geographic location was the inputs and the level of groundwater was the output of each method. we found that ANN has the highest efficiency, which agrees with other findings. We combined the results of ANN with Ordinary Kriging and produced a groundwater condition map. According to the potential desertification map and groundwater level index, the potential of desertification had become severe since 2002 and was at a rate of 60% of land area, which, due to incorrect land management in 2016, increased to almost 98% of the land surface in the study area. Using ANN, we predicted that around 99% of the area was severely degraded for 2017. We also used latitude and longitude as input variables which improved the model. In addition to the target variable, latitude and longitude play important roles in Ordinary Kriging and decreased the total error of two combined models.

Birnbaum-Saunders Durability Life Prediction Model of Site-Exposed Concrete in the Saline Soil Area

Aiming at the prominent problem of short durability life of concrete in saline soil area and the shortcomings of indoor accelerated test, an outdoor field exposure test was designed. The concrete specimens were semiburied in the Tianshui area with salinized soil characteristics, and nondestructive testing was conducted every 180d (days). The durability evaluation parameters and mechanical performance indexes were selected for macroscopic analysis, and the corrosion mechanism was analyzed by using the SEM image and the XRD phase. The Birnbaum-Saunders model based on physical failure and probability statistics was used for life prediction. The results show that there are rod-shaped and chip-shaped crystals growing from the surface of the gel and the internal holes in the exposed end and the embedded end of the concrete. However, the damage and deterioration of the buried end are more serious than those of the exposed end. The corrosion products mainly included ettringite, gypsum, calcium carbonate, sodium sulfate hydrate, carbosilite, and Friedel’s salt. The reliability life curve based on the Birnbaum-Saunders model can describe the whole process of exposed concrete from damage accumulation to failure. In addition, the dynamic modulus degradation index is more sensitive to concrete durability damage, and the life obtained by the Birnbaum-Saunders model is shorter than the quality degradation index. The life obtained by this degradation index is taken as the life of the concrete exposed in the saline soil site, and the concrete life of C30, C40, and C50 is about 3340d, 3930d, and 4360d, respectively.

Influence of Fuel Level on Properties, Productivity, and Mineralogy of Russian Vanadiferous Titanomagnetite Sinter

The influence of fuel level on Russian vanadiferous titanomagnetite sinter properties, productivity, and mineralogy are researched by sintering pot testing, metallographic microscopy, scanning electron microscopy analysis, and energy dispersive spectrometer (SEM-EDS) analysis. A comprehensive index is evaluated in conjunction with the same indexes and significance coefficient as that in the Panzhihua Iron and Steel Group. Results show that with the increasing fuel level from 3.5% to 6.0%, flame front speed, yield, tumbling test index (TI), and productivity, all first increase and then decrease. The low temperature reduction degradation index (RDI+3.15) and softening zone (ΔT) gradually increase while the RI and starting temperature of softening (T10), and ending temperature of softening (T40) decrease with increasing fuel levels from 3.5% to 6.0%. With the increase of fuel level from 3.5% to 6.0%, the content of FeO, SiO2, and MgO increase, while TiO2 shows a decrease. For the same increase in fuel level, the number of pores and calcium ferrite and hematite decrease but the silicate increases. In addition, in the fuel level range of 3.5% to 5.5%, magnetite correspondingly increases but then shows a drop after 5.5%. Moreover, when the fuel level increases to greater than 5.0%, FeOx and fayalite quickly increase and a small amount of metallic iron appears under the fuel level of 6.0%. Overall, the optimal fuel level under current production conditions and indicator selection is 4.0%.