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The purpose of constructing a degradation index (DI) is to better characterize the degradation degree of mechanical transmission compared with relying solely on spectral oil data, which leads to an accurate estimation of the failure time when the transmission no longer fulfills its function.

The DI is modeled using a weighted average function with two desirable properties: maximizing the monotonic trend and minimizing the variance of failure threshold between different transmissions. The method includes concentration modification, data selection and data fusion steps that lead to a reasonable mechanical transmission degradation model. The proposed methodology was verified through a case study involving multispectral oil data sampled from several power-shift steering transmissions.

The results show that the DI outperforms all spectral oil data. Compared with the existing spectral oil data-based degradation modeling approach for mechanical transmissions, the present methodology provides an accurate RUL prediction.

There are several important directions for future research: First, more degradation data (i.e. ferrography) that are tailored to the degradation modeling of mechanical transmission need to be involved. Second, more effective degradation data selection methodologies that are applicable for multiple data types need to be developed. Third, kernel methods that can fuse the nonlinear degradation data need to be investigated.

The novelty of this methodology lies in integrating the multiple degradation data in a unified DI. And the main contribution of this paper is to establish a new direction in degradation modeling and RUL prediction of mechanical transmission.

Rolling bearings based on rotating machinery are one of the most widely used in industrial applications because of their low cost, high performance and robustness. The purpose of this paper is to describe how to identify degradation condition of rolling bearing and predict its fault time in big data environment in order to achieve zero downtime performance and preventive maintenance for the rolling bearing.

The degradation characteristic parameters of rolling bearings including intrinsic mode energy and failure frequency were, respectively, extracted from the pre-processed original vibration signals using EMD and Hilbert transform. Then, Spearman’s rank correlation coefficient and PCA were used to obtain the health index of the rolling bearing so as to detect the appearance of degradations. Furthermore, the degradation condition of the rolling bearings might be identified through implementing the monotonicity analysis, robustness analysis and degradation analysis of the health index.

The effectiveness of the proposed method is verified by a case study. The result shows that the proposed method can be applied to monitor the degradation condition of the rolling bearings in industrial application.

Further experiment remains to be done so as to validate the effectiveness of the proposed method using Apache Hadoop when massive sensor data are available.

The paper proposes a methodology for rolling bearing condition monitoring representing the steps that need to be followed. Real-time sensor data are utilized to find the degradation characteristics.

The result of the work presented in this paper form the basis for the software development and implementation of condition monitoring system for rolling bearings based on Hadoop.

Proposes to examine acceleration of photo‐oxidative degradation of high‐density polyethylene (HDPE) by using photosensitive acetophenone‐formaldehyde resin (AFR).

Degradation of HDPE by UV light was investigated in the presence of photosensitive AFR on natural weathering. The experiments were done at constant temperatures (40, 65 and 90°C). The results were determined by FT‐IR spectrophotometric and viscometric methods. Measurement of the rate of formation of carbonyl groups on the FT‐IR showed the evidence of degradation. The carbonyl indices of photo‐oxidation of HDPE with/without AFR were determined by FT‐IR spectroscopy. The molecular weights of the samples (M_η values) were measured by viscometry.

The amount of carbonyl present in the AFR containing HDPE samples and the changes in their molecular weights were found to depend on the irradiation period, temperature and amount of AFR in the mixture. The improvements in UV performance have been observed by using 1 per cent photosensitive AFR in the mixture. Photo‐oxidative degradation also appeared to be accelerated by heat.

This study can be focused on using photosensitive resins for the polymer degradations just as powder mixture, but the HDPE sample used did not contain antioxidants. From this point of view, commercial HDPE and AFR must be mixed as a film‐former and the AFR concentration will be higher than those of this work.

This work provides technical information for the application of photosensitive resins for easy degradation of HDPE packaging materials.

The method in which a photosensitive resin is used in the polymer degradation may be a reference for other relevant studies.

The purpose of this paper is to propose and develop artificially intelligent methodologies to discover degradation trends through the detection of engine’s status. The objective is to predict these trends by studying their effects on the engine measurable parameters.

The method is based on the implementation of an artificial neural network (ANN) trained with well-known cases referred to real conditions, able to recognize degradation because of two main gas turbine engine deterioration effects: erosion and fouling. Three different scenarios are considered: compressor fouling, turbine erosion and presence of both degraded conditions. The work consists of three parts: the first one contains the mathematical model of real jet engine in healthy and degraded conditions, the second step is the optimization of ANN for engine performance prediction and the last part deals with the application of ANN for prediction of engine fault.

This study shows that the proposed diagnostic approach has good potential to provide valuable estimation of engine status.

Knowledge of the true state of the engine is important to assess its performance capability to meet the operational and maintenance requirements and costs.

The main advantage is that the engine performance data for model validation were obtained from real flight conditions of the engine VIPER 632-43.

The purpose of this paper is to present an optical technique for the condition monitoring of synthetic hydraulic oil; a deviation from the current techniques based on electrical principles which could be masked by wear particles and polar contaminants in oil.

Color‐change detecting device was developed using light‐emitting diodes, optic fibers and photodiodes of three‐color‐sensing elements. Color ratio (CR) and total contamination parameters based on transmitted light intensity in red, green, and blue wavelengths were used for oil chemical and particulate contamination assessment.

CR criterion was found independent of the particulate contamination of oil; but depended on chemical degradation. Total contamination index of the device depended on both the chemical degradation and particulate contamination of the oil, being most sensitive in blue wavelength, and least in the red. Test results for synthetic hydraulic oils monitored corroborated with results of viscosity, total acid number, RDE emission spectrometry, particulate counts and UV‐Vis photospectrometry. CR showed a clearer indication of oil degradation, compared to key monitoring parameters such as total acid number, viscosity, RDE emission spectrometry and particle counts.

This paper demonstrates how oil chemical degradation and total contamination could be detected through the device, before incipient wear occurs at tribological interfaces. The results showed that the color‐change parameters are effective criteria for the condition monitoring of synthetic hydraulic oils.

In Brazil, the city of Pelotas experienced an economic apex between the end of the nineteenth century and the beginning of the twentieth century, reflecting in the construction of several buildings. The aim of this article is to evaluate the state of degradation of the facades of the Old School of Agronomy Eliseu Maciel, using qualitative and quantitative indicators.

The study was divided into visual and photographic survey, damage map and subsequent application of the Degradation Measurement Method (MMD) and the Element Performance Index (Ip). Taking into consideration the evaluation of the building: through historical research and survey of damages. Finally, the building was framed at a level of degradation.

By calculating the average damage between the methods – damage map, MMD and Ip – a more faithful representation of the damage was obtained, since the values become balanced. The results show the potential of the use of the methods in the analysis of pathological manifestations in facades.

The originality of this article refers to the use of methods for evaluation of historical buildings. The authors believe that the methods described applied jointly provide the results about the state of degradation through nondestructive and low-cost analyses. The methods of surveying damage to Brazilian heritage are a little researched area. This work will hopefully be engaged by academics and professionals to help establish and promote broad government interest and investments.

The purpose of this examine the impact of income inequality and shadow economy on environmental degradation given the growing income inequality, shadow economy and ecological degradation in developing countries. Thus, this study is motivated to offer empirical insight into how income inequality and shadow economy influence the environment in African countries.

Data from 29 countries in Africa between 2000 and 2017 were used, while the novel method of moments quantile regression of Machado and Silva (2019) and Dumitrescu and Hurlin (D-H) (2012) granger causality is used as the estimation techniques.

The results established the presence of cross-sectional dependence and slope heterogeneity in the panel, while Westerlund panel cointegration confirmed the long-run cointegration among the variables. The results from the quantile regression suggest that income inequality increases environmental degradation from the 5th to the 30th quantiles, while from the 70th quantiles, income inequality reduces ecological degradation. The shadow economy negatively influences environmental degradation across the quantiles, strengthening environmental quality. Per capita income (economic growth) and financial development positively impact environmental degradation throughout the quantiles. However, urbanization reduces environmental degradation from 60th to 95th quantiles. The D-H causality established a two-way relationship between income inequality and environmental degradation, while one-way from shadow economy, per capita income and urbanization to environmental degradation were established.

This study provides fresh insights into the nexus between shadow economy and environmental quality in the presence of higher levels of income inequality for the case of African region. The study applies quantile analysis via moment proposed by Machado and Silva (2019). This technique shows that the impact of income inequality and shadow economy on environmental degradation is heterogeneous across the quantiles of ecological footprints in Africa.

This study attempts to come in help for Morocco by investigating rigorously the linkage between environmental degradation, measured by ecological footprint (EF), and the gross domestic product growth (EG), the human capital (HC) index and the natural resources (NR) depletion over the period of 1980:Q1 to 2021:Q1. The paper examines the validity of environmental Kuznets curve (EKC) hypothesis in the Moroccan context.

Unlike previous studies, which are based only on the autoregressif dynamic linear (ARDL) model, this paper investigates two recent models: the novel DYNARDL simulation approach and the Kernel-based regularized least squares (KRLS) technics and uses in addition the frequency domain causality (FDC) test.

Models output say a significant and negative association between HC and the EF and a significant and positive interplay between economic growth and environmental quality in the long term. In the short term, findings reveal a significant and negative association between NR and the EF. Based on the FDC test, results conclude about a unidirectional causality from NR to the EF in short-, medium-, and long-term. Moreover, results validate the EKC hypothesis for the Moroccan environment sustainability.

In this study, the researchers use the “ecological footprint” as dependent variable to obtain more accurate and comprehensive assessment of environmental deterioration. Based on time series data investigations, this study is the first paper, which validates the EKC hypothesis and develops important policy implications for Morocco context to achieve sustainable development targets.

The importance of thoroughly monitoring the state of the environment in Mediterranean ecosystems has long been recognised. With regard to the spatial extension of large areas threatened by various degradation processes it becomes obvious that terrestrial observation alone is hardly able to cope with this task. Remote sensing with air‐ or spaceborne sensor systems provides a comprehensive spatial coverage, is intrinsically synoptic, and collects objective, repetitive data and is thus ideally suited for monitoring environmentally sensitive areas. The major problem associated with its use is to quantitatively interpret a measured signal that has interacted with remote objects in terms of the properties of these objects. In parallel to the advances in remote sensing geographical information systems (GIS) have emerged as a fully functional support for resource management tasks. As an example for tracing and analysing environmental change with coupled remote sensing and GIS approaches we present a case study on the island of Crete which was carried out in the framework of research programmes supported by the European Union. Although it is known that grazing in Crete dramatically increased during the last two decades, it was not well understood how grazing pressure differs spatially and in how far it altered the landscape of Crete. One of the major rangeland areas of central Crete, the Psiloritis Mountains, have been selected to serve as a test site for answering these questions. On the basis of an extended Landsat‐TM and ‐MSS data set acquired between 1977 and 1996 it has been shown that time series analysis techniques based on vegetation fractions derived from spectral unmixing can substantiate a spatio‐temporal interpretation of degradation processes. In areas under massive grazing pressure such processes can be linked to the respective driving forces by GIS‐based analyses of natural and socio‐economic boundary conditions.

In rotary machines, the bearing failure is one of the major causes of the breakdown of machinery. The bearing degradation monitoring is a great anxiety for the prevention of bearing failures. This paper aims to present a combination of the stationary wavelet decomposition and extra-trees regression (ETR) for the evaluation of bearing degradation.

The higher order cumulants features are extracted from the bearing vibration signals by using the stationary wavelet decomposition (stationary wavelet transform [SWT]). The extracted features are then subjected to the ETR for obtaining normal and failure state. A dominance level curve build using the dissimilarity data of test object and retained as health degradation indicator for the evaluation of bearing health.

Experiment conducts to verify and assess the effectiveness of ETR for the evaluation of performance of bearing degradation. To justify the preeminence of recommended approach, it is compared with the performance of random forest regression and multi-layer perceptron regression.

The experimental results indicated that the presently adopted method shows better performance for detecting the degradation more accurately at early stage. Furthermore, the diagnostics and prognostics have been getting much attention in the field of vibration, and it plays a significant role to avoid accidents.