Diverse Working Conditions Research Articles

SPEED CONTROL OF BRUSHLESS DC ENGINE USINGFUZZY BASED SHOW REFERENCE VERSATILE CONTROL

Brushless DC engine drives are broadly utilized in different mechanical frameworks applications, such as servomotor drives, therapeutic, car, and aviation industry. BLDC engines are commutated electronically and offer numerous preferences over brushed DC engines which incorporate expanded productivity, longer life, moo volume, and tall torque. Speed control of BLDC engine with radically changing and questionable stack variety with customary PID control framework is incapable to glass up with conjointly incapable to adjust framework stack variety with consistent gain values. So, the most objective of this proposal is to plan a controller to keep the yield speed of the BLDC engine consistent, beneath diverse working conditions such as parameter varieties at evaluated speed, questionable stack unsettling influences and etc. In Fluffy shrewdly controller Based demonstrate reference versatile control, the plant yield is shifted with regard to the yield of the reference demonstrate with a few altering components in arrange to get the speed control at working condition. The stage for displaying of BLDC engine and reenactment of the control framework has been done utilizing MATLAB/Simulink Computer program and the result appears that cleverly based demonstrate reference versatile controller is way better in relentless state execution and stack dismissal property than basic MIT run the show and customary PID controller and moved forward the existing framework of the relentless state response.

Similarity-based meta-learning network with adversarial domain adaptation for cross-domain fault identification

With wide applications of intelligent methods in mechanical fault diagnosis, satisfactory results have been achieved. However, complicated and diverse practical working conditions would significantly reduce the performance of the diagnostic model that works well in the laboratory, i.e. domain shift occurs. To address the problem, this paper proposed a novel similarity-based meta-learning network with adversarial domain adaptation for cross-domain fault identification. The proposed domain-adversarial similarity-based meta-learning network (DASMN) consists of three modules: a feature encoder, a classifier and a domain discriminator. First, the encoder and the classifier implement the similarity-based meta-learning algorithm, in while the good generalization ability for unseen tasks is obtained. Then, adversarial domain adaptation is conducted by minimizing and maximizing the domain-discriminative error adversarially, which takes unlabeled source data and target data as inputs. The effectiveness of DASMN is evaluated by multiple cross-domain cases using three bearing vibration datasets and is compared with five well-established methods. Experimental results demonstrate the availability and outstanding generalization ability of the proposed method for cross-domain fault identification.

Deep transfer learning with limited data for machinery fault diagnosis

Investigation of deep transfer learning on machinery fault diagnosis is helpful to overcome the limitations of a large volume of training data, and accelerate the practical applications of diagnostic algorithms. However, previous reported methods, mainly including parameter transfer and domain adaptation, still require a few labeled or massive unlabeled fault samples, which are not always available. In general, only extremely limited fault data, namely sparse data (single or several samples), can be obtained, and the labeling is also easy to be processed. This paper presents a novel framework for disposing the problem of transfer diagnosis with sparse target data. In consideration of the unclear data distribution described by the sparse data, the main idea is to pair the source and target data with the same machine condition and conduct individual domain adaptation so as to alleviate the lack of target data, diminish the distribution discrepancy as well as avoid negative transfer. More impressive, the issue of label space mismatching can be appropriately addressed in our network. The extensive experiments on two case studies are used to verify the proposed method. Comprehensive transfer scenarios, i.e., diverse working conditions and diverse machines, are considered. The thorough evaluation shows that the proposed method presents superior performance with respect to traditional transfer learning methods.

Photocatalytic inactivation of dairy bacteriophages contained in aerosol: Comparative evaluation of efficiencies

The aim of this work was to investigate the photocatalytic inactivation efficiencies of eight phages infective to lactic acid bacteria (LAB) contained in bioaerosols. Studies were performed in a semi-pilot scale equipment, which consists of a stainless steel camera, in which air containing the phage particles circulates, and an acrylic reactor with twenty UV-A lamps and six borosilicate plates covered with the catalytic film (TiO2). Phages (ALQ13.2, ATCC 15807-B1, ATCC 8014-B1, BYM, CHD, LDG, Ln-9 and MLC-A) were nebulized into the camera using the 6-jet Collison nebulizer, being assays performed for a total time of 100 min. Sampling by impact of air on a glass slide was carried out. Some conditions of equipment operation (phage suspension concentration, nebulization method, influence of relative humidity) using phage ATCC 8014-B1 were previously adjusted. Phages ALQ13.2, ATCC 15807-B1, BYM and CHD were not detected in the air because of loss of their infectivity after nebulization when assays in absence of catalyst and UV radiation were conducted. On the other hand, it was possible to achieve a complete inactivation (< 10 PFU mL−1) of phages ATCC 8014-B1, LDG and Ln-9 within 100 min of photocatalytic treatments. On the other, phage MLC-A was partially inactivated under the same conditions. These results are in agreement to those obtained when photonic and quantum inactivation efficiencies were calculated. In this sense, efficiencies varying between 1.27–2.64 × 10−17 PFU photon−1 and 4.42–9.18 × 10−17 PFU photon−1 were obtained, respectively, being the lowest efficiencies for phage MLC-A. This phage with the highest resistance could be studied in the future to evaluate diverse working conditions in order to optimize the operation and efficiency of the semi-pilot scale equipment.

Deep Transfer Learning based Multisource Adaptation Fault Diagnosis Network for Industrial Processes

Abstract In industrial processes, there are generally multiple data sources generated from different working conditions, which can provide different fault diagnosis knowledge to the target application. In this paper, a multisource adaptation diagnosis network (MADN) method is proposed to transfer the diagnostic knowledge existed in multiple sources to the target. First, a stacked-autoencoder based feature generator is pretrained to extract feature representations from the process data acquired from diverse working conditions. Then, domain discriminators are developed to reduce the distribution discrepancy between the target domain and each of the sources in an adversarial way. The domain discrimination ability, on the other hand, also reveals the different importance of the source domains. Thus, the fault classifiers can be assembled to identify the fault types of the unlabeled target data. The superiority of the proposed method is verified using a real-world process.

A comparison on microstructure and mechanical properties of electric discharge metal matrix nickel and silica composite coating on duplex stainless steel

Electric Discharge Alloying/Coating a thermal process of elemental deposition together with pyrolysis carbon enhance various properties. Nickel and silica both provide excellent property, but the solidification rate differs the material microstructural properties (amorphous) that improves its properties under diverse working conditions. In present study analysis of properties such as surface roughness, Microstructure, layer thickness, elemental composition, porosity, decarburizing depth is detailed through metallurgical characterizations and mechanical testing are compared to nickel and silica. Nickel holds a hardness value of 1272 HV and across 968 HV with minimal in carter, pokes, splatters having uniform boundaries that improves friction co-efficient, limiting wear resistant, phase transformation, oxidation, and graphite flakes confirm self-lubrication properties at room temperature under higher loading condition. Silica having 1284 HV on the surface and across it reaches to 980 HV due to dispersed elements and its phase transformation of silica to silica carbide directs secondary arching improves bonding by restrictive carbon deposition promotes passage to speak. This limits the porosity, coating layer thickness and increases metal matrix composition in decarburizing layer holds stability towards coating. Nickel affords surface properties limiting matrix composite, oxidation, and grain growth where else silica provides exceptional in metallurgical mixed composition improves the mechanical behaviour of coating.

A comparative study between ORC and Kalina based waste heat recovery cycles applied to a green compressed air energy storage (CAES) system

Intermittent nature of the generated power from renewable energy resources and a higher demand for electricity during peak demand periods have intensified the need for grid-scale energy storage systems. Compressed air energy storage system, owing to significant merits such as minimum geographical and environmental limits and high reliability, has attracted attention in recent years. To improve efficiency, its hybridization with various waste heat recovery cycles has been taken into the account. To provide the grid with further power during peak demand periods, organic Rankine and Kalina cycles are the most practical alternatives. Hence, the combination of a green compressed air energy storage with various low- and medium-temperature waste heat recovery cycles is analyzed in the present article to address the most feasible choice in diverse working conditions. The subcritical and supercritical organic Rankine cycles with different working fluids (R717, R1270, R290, and R1234yf), as well as two Kalina cycles (KCS11 and KCS34) are analyzed from the perspective of the first and second laws of thermodynamics and compared with each other. The results indicate that the energy and exergy round trip efficiencies can be improved by 1.69–2.67% and 1.70–2.69%, compared to the stand-alone compressed air energy storage. The supercritical organic Rankine cycle with R290 as the working fluid is the best alternative, which recovers the highest amount of wasted heat and improves the production capacity by 2.47%.

Interplay between rotational work shift and high altitude-related chronic intermittent hypobaric hypoxia on cardiovascular health and sleep quality in Chilean miners

Mining activities expose workers to diverse working conditions, rotational shifts and high altitude-related hypobaric hypoxia. Separately, each condition has been reported having a negative impact on miners’ health risk; however, the combination of both stressors has been poorly explored. The present study aimed to analyse the effects of exposure to rotational work shift (RWS) alone or in combination with high altitude-related chronic intermittent hypobaric hypoxia (CIHH) on cardiometabolic, physical activity and sleep quality related markers in copper miners from Los Pelambres mine in Chile. One hundred and eleven male miners working in RWS with or without CIHH were included. Anthropometrics measures, sleep quality assessment, physical activity level (PAL) and handgrip strength were evaluated. Exposure to CIHH exacerbated the detrimental effects of RWS as miners exposed to the combination of RWS and CIHH where more obese and had a wider neck circumference, reduced PAL at work and worsened sleep quality. Practitioner summary: The purpose was to assess cardiometabolic health and sleep quality markers associated with the combined effects of rotational shift work and high altitude-related intermittent hypobaric hypoxia in miners. Findings showed a wider neck circumference, lower physical activity level and higher prevalence of poor sleep quality in exposed miners. Abbreviations: ANOVA: analysis of variance; BM: body mass; BMI: body mass index; CI: confidence intervals; CIHH: chronic intermittent hypobaric hypoxia; CV: cardiovascular; CVR: cardiovascular risk; HA: high altitude; HACE: high-altitude cerebral edema; HGS: handgrip strength; IPAQ-SF: International Physical Activity Questionnaire - Short Form; LSD: Fisher's least standardized difference; MANCOVA: multivariate general lineal model; MET: metabolic equivalent; PAL: physical activity level; PSQI: Pittsburg sleep quality index; RWS: rotational work shift; WHR: waist-to-hip ratio

Investigation of the effects of base fluid type of the nanofluid on heat pipe performance

Most recently, an ascending tendency in nanoparticles containing working fluid utilization has been observed in such thermal systems as double pipe heat exchangers and thermosiphons in so far as its advantages upon the performance of such systems. In order to investigate how the type of the base fluid affects the nanofluid’s properties used for thermal applications, an experimental test rig was setup and two different nanofluid each of which involves different base fluid, but same nanoparticles and surface active agent were tested. During the nanofluid preparation process, bauxite nanoparticles as nanoparticle material and sodium dodecyl benzene sulfonate as surface active agents were used in volume fractions of 2% and 0.5%, respectively. As the base fluid type, ethylene glycol and deionized water were utilized. The tests were conducted under diverse working conditions and vacuum pressure. Distribution of temperature ahead the heat pipe wall, efficiency and heat pipe’s thermal resistance was experimentally investigated. It was observed from the experiments that each nanofluid improved the heat pipe performance significantly; however, deionized water/bauxite nanofluid gave the best results in terms of heat pipe’s thermal performance. For each nanofluid, the maximum increment in efficiency was observed under 200 W heating power and 10 g/s cooling water mass flow rate conditions, and improvement rates were 29.5% and 13.3% for ethylene glycol-based and deionized water-based nanofluids, respectively. At least 20% of decline in thermal resistance of the heat pipe was also recorded, when nanofluid was employed as working fluid.

Relationship between corrosion product and fouling growth on mild steel, copper and brass surface

The corrosion and fouling, accumulating on the heating transfer surface of the water supply system and the steam turbine system, are two main concerns in condenser tube maintenance. This work studied the coupling mechanism of the corrosion products and fouling on different materials, as well as the crystal forms of fouling products under diverse working conditions. The common materials of condenser tube, mild steel, brass and copper, and they were firstly pre-oxidized in different dissolved oxygen concentrations and temperatures in the reaction kettle. Then fouling experiment was performed on the corroding specimens. Consequently, at 40℃, specimens have the highest oxidation corrosion rate and the corrosion rate in turn are mild steel 2.472 mm·year−1, copper 0.789 mm·year−1 and brass 0.147 mm·year−1 at the same temperature. Slightly corroded surface is prone to foul while fouling growth rate on brass and copper surface decreases with time. Moreover, the crystal form of fouling on different surfaces are distinct. The surface free energy of mild steel, brass and copper are 55.262 mN·m−1, 33.126 mN·m−1 and 46.954 mN·m−1, respectively. Thus, specimen surface shows hydrophobicity and the adhesion probability of calcium carbonate fouling on brass, copper and mild steel surfaces increases in turn correspondingly. Calcite is the main crystal structure on mild steel, copper and brass surface after dissolved oxygen corrosion. Contrastively, aragonite only show in large numbers on corroded mild steel surface. Thus, this work provides evidence that fouling growth rate is influenced by the dissolved oxygen corrosion in the fields of heat transfer.

A Multistage Deep Transfer Learning Method for Machinery Fault Diagnostics Across Diverse Working Conditions and Devices

Deep learning methods have promoted the vibration-based machinery fault diagnostics from manual feature extraction to an end-to-end solution in the past few years and exhibited great success on various diagnostics tasks. However, this success is based on the assumptions that sufficient labeled data are available, and that the training and testing data are from the same distribution, which is normally difficult to satisfy in practice. To overcome this issue, we propose a multistage deep convolutional transfer learning method (MSDCTL) aimed at transferring vibration-based fault diagnostics capabilities to new working conditions, experimental protocols and instrumented devices while avoiding the requirement for new labeled fault data. MSDCTL is constructed as a one-dimensional convolutional neural network (CNN) with double-input structure that accepts raw data from different domains as input. The features from different domains are automatically learned and a customized layer is designed to compute the distribution discrepancy of the features. This discrepancy is further minimized such that the features learned from different domains are domain-invariant. A multistage training strategy including pre-train and fine-tuning is proposed to transfer the weight of a pre-trained model to new diagnostics tasks, which drastically reduces the requirement on the amount of data in the new task. The proposed model is validated on three bearing fault datasets from three institutes, including one from our own. We designed nine transfer tasks covering fault diagnostics transfer across diverse working conditions and devices to test the effectiveness and robustness of our model. The results show high diagnostics accuracies on all the designed transfer tasks with strong robustness. Especially for transfer to new devices the improvement over state of the art is very significant.

Characteristics Analysis and Fuzzy Fractional-Order PID Parameter Optimization for Primary Frequency Modulation of a Pumped Storage Unit Based on a Multi-Objective Gravitational Search Algorithm

Compared with conventional hydropower units, the pumped storage unit has the characteristics of diverse working conditions and frequent switching. Therefore, the stability and regulation quality of the primary frequency modulation transition process of the regulating system is very important. Due to the “S” characteristics of the pumped storage unit (PSU), the pumped storage unit regulating system has a strong nonlinearity, and the conventional proportional-integral-derivative (PID) controller cannot provide high-quality control under low water head conditions. In this paper, the nonlinear PSU model with an elastic water hammer effect is studied, and the fuzzy fractional-order PID (FFOPID) controller is designed to improve the stability of the system. The membership function and the control parameters of the fractional-order PID are optimized based on the multi-objective gravitational search algorithm (MOGSA). The experimental results show that the optimized design of the FFOPID controller has better control quality than the traditional PID controller, the fractional-order PID (FOPID) controller, and the fuzzy PID controller (FPID) when the system is disturbed by the rotating speed under low water head.

Determination of the order of stochastically linear dynamic systems by using non-parametric estimation of a regression function

In order to determine the accurate order of the model of discrete linear dynamic system, nonparametric algorithm is investigated. The algorithm is based on the application of the rule of selection of significant variables depending on a bandwidth. Selected variables are included in the Nadaraya–Watson regression estimation. The investigated dynamic system was simulated under diverse working conditions. The accuracy of the models estimated with the non-parametric methods is investigated. Computational experiments showed that using the non-parametric algorithms, the model orders can be determined. Because of the large number of cases investigated, the probability of the general validity of this nonparametric algorithm is high.

Learning transferable features in deep convolutional neural networks for diagnosing unseen machine conditions

Recent years have witnessed increasing popularity and development of deep learning spanning through various fields. Deep networks, and in particular convolutional neural network (CNN) have also achieved many state-of-the-art competition results in the intelligent fault diagnosis of mechanical systems. However, most of the existing studies have been performed with the assumption that the same distribution holds for both the training data and the test data, which is not in accord with situations in real diagnosis tasks. To tackle this problem, a transfer learning framework based on pre-trained CNN, which leverages the knowledge learned from the training data to facilitate diagnosing a new but similar task, is presented in this work. First, the CNN is trained on large datasets to learn the hierarchical features from the raw data. Then, the architecture and weights of the pre-trained CNN are transferred to new tasks with proper fine-tuning instead of training a network from scratch. To adapt the pre-trained CNN in a specific case, three transfer learning strategies are discussed and compared to investigate the applicability as well as the significance of feature transferability from the different levels of a deep structure. The case studies show that the proposed framework can transfer the features of the pre-trained CNN to boost the diagnosis performance on unseen machine conditions in terms of diverse working conditions and fault types.

A semi-active control method for decreasing longitudinal torsional vibration of vehicle engine system: Theory and experiments

Due to diverse working conditions, the vehicle engine system may generate complicated nonlinear whole-body vibration with wide frequency band. Since conventional vibration control methods are usually designed in terms of the external excitations and implemented based on the linear models, their performance is difficult to improve. In this study, a new semi-active vibration absorption method that is both independent of external excitations and capable of dealing with nonlinear vibration problem is put forward to decrease the longitudinal torsional vibration of the vehicle engine system. It sufficiently utilizes nonlinear coupling and modal interaction to transfer and dissipate vibration energy, rather than relies on linear neutralization. A novel vibration absorber scheme with controllable frequency and controllable damping is proposed, which can be readily implemented by the control algorithms. Numerical simulations and virtual prototyping simulations have verified the correctness of theoretical research. Experimental investigations have been carried out to successfully examine the effectiveness and the feasibility of the proposed method.

A transient one-dimensional numerical model for kinetic Stirling engine

A third-order numerical model based on one-dimensional computational fluid dynamics is developed for kinetic Stirling engines. Various loss mechanisms in Stirling engines, including gas spring hysteresis loss, shuttle loss, appendix displacer gap loss, gas leakage loss, finite speed loss, piston friction loss, pressure drop loss, heat conduction loss, mechanical loss and imperfect heat transfer, are considered and embedded into the basic control equations. The non-equilibrium thermal model is adopted for the regenerator to capture the oscillating features of the gas and solid temperatures. To improve the numerical stability and accuracy, the implicit second-order time difference scheme and the second-order upwind scheme are adopted for discretizing the time differential terms and convective terms, respectively. Experimental validations are then conducted on a beta-type Stirling engine with the extensive experimental data for diverse working conditions. The results show that the developed model has better accuracies than the previous second-order models. Good agreements are achieved for predicting various critical system parameters, including pressure-volume diagram, indicated power, brake power, indicated efficiency, brake efficiency and mechanical efficiency. In particular, both the experiments and simulations show that the Stirling engine charged with helium tends to have much lower optimal working frequencies and poorer performances compared to the hydrogen system. Based on the analyses of the losses, it reveals that the pressure drop in the flow channels plays a critical role in shaping the different behaviors. The pressure drop in the helium system is much larger and more sensitive to the frequency increase due to the much larger viscosity of gaseous helium. Hydrogen is a superior working gas for a Stirling engine. The transient characteristics of the oscillating flow and the associated thermal interactions between gas and solid in the regenerator are finally analyzed in order to have an insight of the complex thermodynamic process. The study provides a promising numerical approach in simulating Stirling engines for further understandings of their operating characteristics and the underling mechanisms.

Optimization of Power Line Communication System Using a Resonant HVDC Bus in a Distributed Renewable Energy Generator

The development of smart grid architecture for renewable energy production system is intensively studied for the possibilities that this architecture offers to optimize the global production and to increase the efficiency of the generator by monitoring individually the various sources subjected to diverse working conditions. To assume monitoring and intelligence in the grid and to communicate between remote stations and a central master one, a communication system needs to be associated to the power grid of the global production equipment. In this contribution we analyzepower line communication solutions in terms of quality of transmitted signal, efficiency and electromagnetic compatibility. We analyze the working modesof communication interfaces based on power line communication using the amplitude-shift-keying (ASK) modulation and integrating a light protocol of Modbus type. We point out that basic solutions offer poor quality of signal and generate a lot of harmonics of the fundamental carrier inducing important electromagnetic perturbations. An original solutionis thus developed, based on a narrow spectrum carrier on resonant HVDC bus working at a low frequency allowing to minimize the generation of harmonics and thus to increase the quality of the transmitted information. A PLC interfaceis then implemented in a hardware circuit and test pointing out the advantages of the PLC communication based on a resonant HVDC bus with sinusoidal carrier for renewable energy generator.

Simulation of an integrated gasification combined cycle with chemical-looping combustion and carbon dioxide sequestration

Chemical-looping combustion is an interesting technique that makes it possible to integrate power generation from fuels combustion and sequestration of carbon dioxide without energy penalty. In addition, the combustion chemical reaction occurs with a lower irreversibility compared to a conventional combustion, leading to attain a somewhat higher overall thermal efficiency in gas turbine systems. This paper provides results about the energetic performance of an integrated gasification combined cycle power plant based on chemical-looping combustion of synthesis gas. A real understanding of the behavior of this concept of power plant implies a complete thermodynamic analysis, involving several interrelated aspects as the integration of energy flows between the gasifier and the combined cycle, the restrictions in relation with heat balances and chemical equilibrium in reactors and the performance of the gas turbines and the downstream steam cycle. An accurate thermodynamic modeling is required for the optimization of several design parameters. Simulations to evaluate the energetic efficiency of this chemical-looping-combustion based power plant under diverse working conditions have been carried out, and a comparison with a conventional integrated gasification power plant with precombustion capture of carbon dioxide has been made. Two different synthesis gas compositions have been tried to check its influence on the results. The energy saved in carbon capture and storage is found to be significant and even notable, inducing an improvement of the overall power plant thermal efficiency of around 7% in some cases.

HDRE: Coverage hole detection with residual energy in wireless sensor networks

Coverage completeness is an important indicator for quality of service in wireless sensor networks (WSN). Due to limited energy and diverse working conditions, the sensor nodes have different lifetimes which often cause network holes. Most of the existing methods expose large limitation and one-sidedness because they generally consider only one aspect, either coverage rate or energy issue. This paper presents a novel method for coverage hole detection with residual energy in randomly deployed wireless sensor networks. By calculating the life expectancy of working nodes through residual energy, we make a trade-off between network repair cost and energy waste. The working nodes with short lifetime are screened out according to a proper ratio. After that, the locations of coverage holes can be determined by calculating the joint coverage probability and the evaluation criteria. Simulation result shows that compared to those traditional algorithms without consideration of energy problem, our method can effectively maintain the coverage quality of repaired WSN while enhancing the life span of WSN at the same time.

Energetic analysis of a syngas-fueled chemical-looping combustion combined cycle with integration of carbon dioxide sequestration

Chemical-looping combustion for power generation has significant advantages over conventional combustion. Mainly, it allows an integration of CO2 capture in the power plant without energy penalty; secondly, a less exergy destruction in the combustion chemical transformation is achieved, leading to a greater overall thermal efficiency. Most efforts have been devoted to systems based on methane as a fuel, although other systems for alternative fuels have can be proposed. This paper focus on the study of the energetic performance of this concept of combustion in a gas turbine combined cycle when synthesis gas is used as fuel. After optimization of some thermodynamic parameters of the cycle, the power plant performance is evaluated under diverse working conditions and compared to a conventional gas turbine system. Energy savings related with CO2 capture and storage have been quantified. The overall efficiency increase is found to be significant, reaching values of around 5% (even more in some cases). In order to analyze the influence of syngas composition on the results, different H2-content fuels are considered. In a context of real urgency to reduce green house gas emissions, this work is intended to contribute to the conceptual development of highly efficient alternative power generation systems.