An Identification Model of Health States of Machine Wear Based on Oil Analysis

This paper presents a modeling procedure for deriving a single value measurebased on a regression model, and a method for determining a statistical threshold value asidentification criterion of normal or abnormal states of machine wear. A real numerical example isexamined by the method and identification criterion presented. The results indicate that thejudgments by the presented methods are basically consistent with the real facts, and therefore themethod and identification criterion are valuable for judging the normal or abnormal state of machinewear based on oil analysis.

A stability analysis for the sheared oil film contained by the boom

According to the observation in experiment of stability of the oil film, the assumption of velocity distribution for both the water flow and the oil film is introduced. On the basis of the assumption, Orr-Sommerfeld stability equation is applied to develop the method of determining the critical velocity of the oil film, and the criterion for stability of the oil film is obtained. Meanwhile, a formula describing the relation between the thickness of the oil film and the velocity of the water flow is also given and examined by the laboratory experiment.

Doppler Spectrum Analysis of Time-Evolving Sea Surface Covered by Oil Spills

Based on the model of a contaminated sea surface that was proposed by Lombardini et al., the influence of the damping effect of oil films on the sea surface roughness spectrum and the geometrical structure of the sea surface is examined in detail by comparing with a clean sea surface. Fhrthermore, based on a quasi-stationary algorithm, a time series of backscattered echoes from a time-evolving sea surface covered by oil slicks is obtained by utilizing the frequency-domain numerical method of the parallel fast multiple method. Then, the Doppler spectrum is evaluated by performing a standard spectral estimation technique. Finally, the influence of the oil film damping effect on the Doppler spectrum of the backscattered echoes from time-evolving sea surface is investigated in detail by making a comparison of the Doppler spectrum of an oil-covered sea surface with the Doppler spectrum of a dean sea surface. The numerical simulations show that the damping effect of oil films has an influence on the Doppler spectrum signature for both horizontal-to-horizontal and vertical-to-vertical polarizations.

Rapid Detection of Oil Pollution in Soil by Using Laser-Induced Breakdown Spectroscopy

Detection of oil pollution in soil has been carried out using laser-induced breakdown spectroscopy（LIBS）. A pulsed neodymium-doped yttrium aluminum garnet（Nd：YAG） laser（1,064 nm, 8 ns, 200 mJ） was focused onto pelletized soil samples. Emission spectra were obtained from oil-contaminated soil and clean soil. The contaminated soil had almost the same spectrum profile as the clean soil and contained the same major and minor elements. However, a C–H molecular band was clearly detected in the oil-contaminated soil, while no C–H band was detected in the clean soil. Linear calibration curve of the C–H molecular band was successfully made by using a soil sample containing various concentrations of oil. The limit of detection of the C–H band in the soil sample was 0.001 mL/g. Furthermore, the emission spectrum of the contaminated soil clearly displayed titanium（Ti） lines, which were not detected in the clean soil. The existence of the C–H band and Ti lines in oil-contaminated soil can be used to clearly distinguish contaminated soil from clean soil. For comparison, the emission spectra of contaminated and clean soil were also obtained using scanning electron microscope-energy dispersive X-ray（SEM/EDX） spectroscopy,showing that the spectra obtained using LIBS are much better than using SEM/EDX, as indicated by the signal to noise ratio（S/N ratio）.

Research on the Information Fusion Method of Oil Diagnosis and Vibration Diagnosis Based on the Rough Sets Theory

The improved method has been presented for knowledge reduction in rough sets （R-S） theory, when R-S is used to model the information expression of oil and vibration diagnosis. Therefore, the typical fault simulation tests of rolling bearings have been made, and the application method of R-S has been also analysed in this paper. The diagnosis model of holding rack fault in rolling bearing was presented based on the improved reduction method. It is suited to information fusion to combine information when oil analysis and vibration analysis are combined for fault diagnosis.

A novel oil spill detection method from synthetic aperture radar imageries via a bidimensional empirical mode decomposition

Oil spills pose a major threat to ocean ecosystems and their health. Synthetic aperture radar（SAR） sensors can detect oil spills on the sea surface. These oil spills appear as dark spots in SAR images. However, dark formations can be caused by a number of phenomena. It is aimed to distinguishing oil spills or look-alike objects. A novel method based on a bidimensional empirical mode decomposition is proposed. The selected dark formations are first decomposed into several bidimensional intrinsic mode functions and the residue. Subsequently, 64 dimension feature sets are calculated using the Hilbert spectral analysis and five new features are extracted with a relief algorithm. Mahalanobis distances are then used for classification. Three data sets containing oil spills or look-alikes are used to test the accuracy rate of the method. The accuracy rate is more than 90%. The experimental results demonstrate that the novel method can detect oil spills validly and accurately.

Growth, Yield and Yield Characteristics of Three Pepper Cultivars to Fertilizers Application in the Mount Cameroon Region

This study determined the effects of organic and inorganic fertilizers on growth and yield of three pepper cultivars in the Mount Cameroon Region. Pepper seedlings were treated with six treatments (T1 = no fertilizers (control), T2 = 250 Kg/ha NPK 20:10:10, T3 = 350 Kg/ha NPK 20:10:10, T4 = 450 Kg/ha NPK 20:10:10, T5 = 20 Mg/ha poultry manure (PM), T6 = 30 Mg/ha PM). The effects of fertilizers had a significant effect on vegetative growth and yield of Capsicum chinense cv. “Big Sun” plants had the maximum plant height (39.09 cm) and mean collar diameter (6.02 mm) in plots treated with poultry manure at 20 Mg/ha (T5) and 30 Mg/ha (T6) respectively. The highest yield (33.63 Mg/ha) was in Capsicum chinense cv. Safi plants supplied with 30 Mg/ha poultry manure (T6) and the lowest yield (10.44 Mg/ha) was in Capsicum frutescens in control plots (T1). The highest fruit diameter (11.08 mm) and longest fruits (18.39 mm) were observed in Capsicum chinense cv. Big Sun plants supplied with 30 Mg/ha poultry manure. Capsicum frutescens fruits had the highest concentration of N (2.39%), P (0.42%) and K (3.06%). Essential oils analysis shows that there were 179 essential oils from pepper fruits of Capsicum chinense cv. Safi, with major essential oil been 3,4-dimethyl-benzaldehyde and main essential oils were alpha Terpinene and alpha Pinene based on their abundance. Fruits of Capsicum chinense cv. Big Sun had two-hundred and eight essential oils with major essential oil been 1,3,5-Trimethyl-1-H-pyrazol-4-amine and main essential oils were Caryophyllene oxide and Cymene, based on their abundance. Fruits of Capsicum frutescens had one-hundred and eighty-one essential oils with major essential oil been Limonene, and main essential oils were Carvacrol and D-limonene with respect to abundance. Results show that 20 Mg/ha poultry manure was the most cost effective in the production of pepper in Buea Cameroon.

辣蓼挥发油的提取及其GC/MS分析

采用水蒸气蒸馏提取辣蓼挥发油,对蒸馏时间、萃取溶剂、叶片碎度进行三因子三水平正交试验,结果表明,最佳蒸馏时间为150 min,最佳萃取溶剂为乙醚,最佳叶片碎度为完全剪碎。将得到的挥发油进行GC/MS分析,水蒸气蒸馏所得挥发油各化学成分含量分别为:烯(7.19%)、酮(40.79%)、酸(2.56%)、醇(9.87%)、醛(2.31%)。

The Research of Aluminum Particle in the Direct Reading Ferograph Precipitator Tube

The depositional law of sub magnetic metal particles of aluminum is not random on the basis of many experiments; previously it was believed to be random. The particles of aluminum have a distribution law in the precipitator tube, and present almost the similar deposition curve as the magnetic particle.

Motor oil degradation during urban cycle road tests

Several civilian vehicles in China operate in urban traffic conditions and have their motor oil changed every 5,000 km.This study investigates the variations in oil properties after servicing at 5,000 km,based on systematic road tests(including a repeated test,a parallel test,and a new vehicle test).The physicochemical properties,changes in components,oxidation stability,detergent‐dispersant performance,and tribological properties of motor oils were analyzed.The results showed that the total acid number(TAN)of oils increased with the operation mileage,by up to 1.41 mgKOH/g.The total base number(TBN)decreased after the road tests were completed,and the decrease was less than 44.6%.The kinematic viscosity(KV)of most oils decreased initially and then stabilized in the middle stage,before starting to increase later in the experiment.The change in KV at 100℃was less than 15.96%.The oxidation onset temperature(OOT)of the oils diminished gradually with the operation mileage.All OOT values of the used oils were higher than 210℃.A spot test indicated that the used oils retained their detergentdispersant performance to an appropriate extent.The four‐ball wear scar diameters and friction coefficient of the used oils did not increase significantly after the road tests were completed.This study can serve as a reference for end‐users when changing motor oils.

Well-being analysis of GSU transformer insulation incorporating the impact on power generation using fuzzy logic

With the prevailing power scenario, every watt-second of electrical energy has its own merit in satisfying the consumer demand. At the state of such a stringent energy demanding era, failure of a power generation equipment compounds the energy constraints which will not only result in a huge loss of generation but also have an impact on capital revenue. The unexpected failure of generator step-up （GSU） transformer is espe- cially a major disturbance in the power system operation and leads to unscheduled outages with power delivery problems. The time lag in bringing back the equipment in service after rectification or replacement may increase the criticality as the process involves mobilization of spares and maintenance professionals. Hot atmosphere existing in the vicinity of thermal power stations running round-the- clock with more than 100% plant load factor （PLF） increases the thermal stress of the electrical insulation which leads to premature failure of windings, bushings, core laminations, etc. The healthy state of the GSU transformer has to be ensured to minimize the loss of power generation. As the predication related to failure of a GSU transformer is associated with some uncertainties, a fuzzy approach is employed in this paper along with actual field data and case studies for the well-being analysis of GSU transformer.

Transformer fault diagnosis based on relational teacher-student network

The analysis of dissolved gas in oil can provide an important basis for transformer fault diagnosis.In order to improve the accuracy of transformer fault diagnosis,a method based on the relational teacher-student network(R-TSN)is proposed by analyzing the relationship between the dissolved gas in the oil and the fault type.R-TSN replaces the original hard labels with soft labels,and uses it to measure the similarity between different samples in the space,to a certain extent,it can obtain the hidden feature information in the samples,and clarify the classification boundary.Through the identification experiment,the effect of R-TSN diagnosis model is analyzed,and the influence of the compound fault of discharge and thermal on the diagnosis model is studied.This paper compares R-TSN with support vector machines(SVMs),decision trees and multilayer perceptron models in transformer fault diagnosis.Experimental results show that R-TSN has better performance than the above methods.After adding compound faults in the sample set,the accuracy rate can still reach 86.0%.

A combined risk-based and condition monitoring approach:developing a dynamic model for the case of marine engine lubrication

This paper focuses on the creation of a dynamic probabilistic model which simulates deterioration trends of a marine engine lubricationsystem. The approach is based on risk and the implementation is achieved through a dynamic Bayesian network (dBN).Risk can be useful for decision making, while dBNs are a powerful tool for risk modelling and prediction models. The model takesinto account deterioration of engine components, oil degradation and the off-line condition monitoring technique of oil analysis, inthe context of predictive maintenance. The paper aims to efficiently predict probability evolution for main engine lubrication failureand to decide upon the most beneficial schemes from a variety of lubrication oil analysis interval schemes by introducing monetarycosts and producing the risk model. Real data and respective analysis, along with expert elicitation, are utilized for achieving modelquantification, while themodel is materialized through a code in the Matlab environment. Results from the probabilistic model showa realistic simulation for the system and indicate the obvious, that with more frequent oil analyses and respective maintenance orrepairs, the probability of failure drops significantly. However, the results from the risk model highlight that the costs can redefinescheme suggestions, as they can correspond to low probabilities of failure but also to higher costs. A two-month interval scheme issuggested, in contrast to the most preferred practice among shipping companies of a three-month interval. The developed model isin general identified as a failure prediction tool focusing on marine engine lubrication failure.