Machinery Configuration Research Articles

Wavelet Denoising Applied to Hardware Redundant Systems for Rolling Element Bearing Fault Detection

This work presents a novel wavelet-based denoising technique for improving the signal-to-noise ratio (SNR) of non-steady vibration signals in hardware redundant systems. The proposed method utilizes the relationship between redundant hardware components to effectively separate fault-related components from the vibration signature, thus enhancing fault detection accuracy. The study evaluates the proposed technique on two mechanically identical subsystems that are simultaneously controlled under the same speed and load inputs, with and without the proposed denoising step. The results demonstrate an increase in detection accuracy when incorporating the proposed denoising method into a fault detection system designed for hardware redundant machinery. This work is original in its application of a new method for improving performance when using residual analysis for fault detection in hardware redundant machinery configurations. Moreover, the proposed methodology is applicable to non-stationary equipment that experiences changes in both speed and load.

Review on Energy Management Strategies of PHEV

The problems of air pollution and the shortage of energy sources are increasingly significant, and the traditional automobile industry plays a vital role in it. Consequently, Energy management strategy can adjust the energy allocation between the engine and the motor, achieving the purpose of improving fuel economy. Meanwhile, it can optimize the battery balance so that the comprehensive cost of the hybrid vehicle can be reduced, apart from the limited emissions and conserved energy. This paper first introduces the machinery configuration of the parallel hybrid electric vehicles, and then classifies their working patterns under different working conditions. After analysing the existing energy management strategies, we introduce and analyse the rule-based ones by two frequently used methods: logic threshold method and fuzzy logic control. Additionally, strategies based on optimization, including instantaneous and global optimization strategy, are also introduced and analysed, with each advantages, limitations and potential innovations. Finally, the contradiction between practicability and control effect is identified, which is one of the crucial problems to be resolved.

Deep cepstrum-wavelet autoencoder: A novel intelligent sonar classifier

Different marine vessels belonging to the same class may have different and time-varying radiated noise due to different and changing machinery configurations. Further, the time-varying radiated noise, which is propagated underwater, suffers from random fluctuations in frequency, amplitude, and phase. The ambient noise and time-varying multi-path effects further complicate the classification problem. In this study, for the first time, a deep wavelet autoencoder is proposed, evolving by a two-hybrid cepstral concept to reduce the multi-path distortion and time-varying shallow underwater channel effects. In this approach, the autoencoder is an automatic feature extractor, choosing the best feature composition without information loss and human intervention, and wavelet networks extract the periodic frequency signatures of vessels, which are fluctuating by various machinery regimes. Three underwater acoustic datasets, including synthetic, ShipsEar, and real experimental datasets, are exploited to evaluate the performance of the proposed model so that the performance of the designed model is compared with ten benchmark methods. The results show that the designed model with an average accuracy of 96.11% and average giga-multiplier–accumulators equal to 0.019 represents the best performance in terms of accuracy and complexity compared to benchmark methods. Besides, the cepstral liftering procedure and the average cepstral feature can significantly alleviate the shallow underwater channel’s multi-path distortion and time-varying channel effects. Furthermore, the proposed model is less sensitive to SNR level compare to other benchmark methods.

Model construction for field operation machinery selection and configuration in wheat-maize double cropping system

Model construction for field operation machinery selection and configuration in wheat-maize double cropping system

基于数学模型的农业机械设备优化分析

With the development of science and technology, the degree of agricultural mechanization is getting higher and higher. Agricultural machinery is an important support for the development of agricultural modernization. Optimizing the allocation of agricultural machinery is conducive to improving agricultural production efficiency and economic benefits. In this paper, mathematical modelling method is mainly used in the analysis and optimization of agricultural machinery configuration. By determining the objective function and constraint equation, combined with the actual situation of Xinjiang Production and Construction Corps, the linear programming model and workload model of agricultural machinery and equipment optimization are established. Finally, the actual number of agricultural machinery and equipment and the number of optimal allocations of Xinjiang Production and Construction Corps farm were compared. The effectiveness of the optimization model is verified by comparing the optimized agricultural machinery equipment with the actual equipment. The results show that the optimized equipment model has good optimization effect. On the basis of reducing the number of agricultural machinery and equipment, the matching rate of agricultural machinery is improved, and the operation cost of agricultural machinery is effectively reduced. It is hoped that this study can provide certain reference and reference for the optimization analysis of agricultural machinery and equipment based on mathematical modelling.

Comparative Analysis of Linear and Non-Linear Programming Techniques for the Optimization of Ship Machinery Systems

The selection of a proper machinery system is one of the primary decisions to be taken during the ship design phase. Nonetheless, this selection is made challenging by the presence of a variety of alternatives, and by the limited data availability at the early stages of the design phase. An optimization framework is presented in this paper, supporting decision making at the earliest stages of the ship design process. The framework is suitable to perform the screening and the selection of optimal machinery configurations for a predefined ship operational profile, and it includes both linear and non-linear optimization routines. The results of the linear and the non-linear approaches are compared, and indications on what conditions are the most suitable for the application of one or the other approach are provided. Both approaches are tested for two case studies, a bulk carrier and a small cruise ship. The results indicate that both optimization approaches lead to the same layout of the machinery system, but to slightly different unit scheduling. This suggests that the use of the linear approach is suitable for design purposes, but less appropriate for operational optimization. In addition, the findings of the work suggest that the trade-off between fuel consumption and volume of the engines should be considered when selecting the machinery system for a ship.

Assessing Particle Segregation Using Near-Infrared Chemical Imaging in Twin Screw Granulation.

In the present study the application of near-infrared chemical imaging (NIR-CI) for assessing particle segregation in granules from continuous twin screw granulation (TSG) granules, were the complex attributes of the machinery configuration in relation to particle segregation is not well understood was investigated. Experiments were performed along the compartmental length of the TSG barrel channel by varying the screw element type and liquid binder viscosity. Examination of the data showed a direct correlation between dispersion due to shear force and de-mixing of particles, which allowed for identification of fundamental granule segregation mechanisms affecting content uniformity in TSG. Particle segregation behavior was linked to dispersion due to shear force through a proposed regime mapping approach which links de-mixing potential to controlling granule formation mechanisms with a new dimensionless mixing number. This was carried out in order to provide a general guideline of how particles segregate along the length of the TSG barrel channel.

Predicting delay factors when chipping wood at forest roadside landings

ABSTRACT Chipping of bulky biomass assortments at roadside landings is a common and costly step in the biomass-to-energy supply chain. This operation normally involves one chipping unit and one or several transport trucks working together for simultaneous chipping and chip transport to a terminal or end user. Reducing the delay factors in these operations is a relevant ambition for lowering supply costs. A method to estimate organizational delay based on: (1) the capacity ratio between the transport and the chipper, (2) the use of buffer storage, and (3) the number of transport units involved is suggested here. Other delays will also be present, and some of these may relate to the working conditions at the landing. A method to set a landing functionality index based on characteristics of the forest landing is also suggested. A total of 14 roadside chipping operations were assessed and the operators were interviewed to address the impact of machinery configuration and landing characteristics on machine utilization. At most sites, the chipper was the more productive part, and the chipper utilization was to a large extent limited by organizational delay. Still the utilization of the transport units varied between 37 and 97%, of which some 36% of the variation was explained by the landing functionality index. Knowledge from the work presented here should be a good starting point for improving biomass supply planning and supply chain configuration.

Automatic feature extraction of time-series applied to fault severity assessment of helical gearbox in stationary and non-stationary speed operation

Signals captured in rotating machines to obtain the status of their components can be considered as a source of massive information. In current methods based on artificial intelligence to fault severity assessment, features are first generated by advanced signal processing techniques. Then feature selection takes place, often requiring human expertise. This approach, besides time-consuming, is highly dependent on the machinery configuration as in general the results obtained for a mechanical system cannot be reused by other systems. Moreover, the information about time events is often lost along the process, preventing the discovery of faulty state patterns in machines operating under time-varying conditions. In this paper a novel method for automatic feature extraction and estimation of fault severity is proposed to overcome the drawbacks of classical techniques. The proposed method employs a Deep Convolutional Neural Network pre-trained by a Stacked Convolutional Autoencoder. The robustness and accuracy of this new method are validated using a dataset with different severity conditions on failure mode in a helical gearbox, working in both constant and variable speed of operation. The results show that the proposed unsupervised feature extraction method is effective for the estimation of fault severity in helical gearbox, and it has a consistently better performance in comparison with other reported feature extraction methods.

Reducing shipboard emissions – Assessment of the role of electrical technologies

Saving fuel and reducing emissions are major drivers in the marine industry, with a large number of potential modifications and machinery options available to enable the greening of shipping. Assessing which technology and what combination of solutions gives favourable economic results needs careful consideration of the vessel’s operational profile. Electrification of shipboard systems introduces operational flexibility, offering the potential for fuel savings and emission reductions. Auxiliary drives, energy storage and onshore power supply are three approaches which address the issues of ship fuel consumption/emissions, specifically during in-harbour operation of vessels. In this paper, the impact of these three technologies on ship environmental performance and energy consumption is assessed by considering a real case RoRo vessel sailing a real operational profile. Models of the resultant system are built such that the machinery configurations can be analysed separately or in conjunction with each other. The results stress the importance of the operational profile of the vessel, showing significant fuel and emissions reductions during in-harbour operations but relatively small savings when considering operation through a complete return voyage. The sensitivity of the results to fuel and utility costs are also considered and shown to have a large impact on the economic feasibility (or otherwise) of different solutions.

Managing Shipboard Energy: A Stochastic Approach Special Issue on Marine Systems Electrification

The efficient utilization of energy is of high importance for shipboard applications, for environmental and economic reasons as well as maximized vessel operation. The electrification of marine systems facilitates the storage and generation of energy from multiple sources, offering the potential for energy savings, due to the flexibility associated with electric systems. In this paper, an energy management system (EMS) based on bond graph system models used in conjunction with a stochastic search algorithm is proposed and developed. The EMS optimizes the configuration of the vessel’s onboard machinery system based on the current operating condition. The approach is universal, with the system model and optimization routine being separate and distinct. The effectiveness of the proposed EMS is demonstrated using a real operating profile of a roll-on/roll-off vessel fitted with a hybrid machinery configuration including energy storage, onshore power supply, and photovoltaic generation. The results show fuel savings of around 23% compared with current fuel consumption levels.

A two-stage optimization approach for sulphur emission regulation compliance

ABSTRACTIn this paper, we present a two-stage optimization model for the machinery system selection problem. The objective is to minimize total cost, while aggregated power requirement and emission regulations are constraining the problem. Future fuel prices are considered to be uncertain. From a set of alternatives, the machinery configuration providing the lowest total cost is found. Also design flexibility in terms of future reconfiguration possibilities is taken into account. The machinery selection for a 2000 TEU container vessel is used as an illustrative case. Five initial machinery concepts are considered: diesel machinery, diesel machinery with a scrubber system, dual fuel (DF) machinery, pure gas engines, and a DF ready machinery. There is also a set of reconfiguration possibilities available for each alternative. From solving the case study, DF machinery is found optimal, while pure gas machinery is close to equally good. By solving the problem with deterministic fuel prices, the value of flexibility is not properly accounted for, resulting in an unreasonably high total cost for the flexible machinery alternatives. This demonstrates the need for a decision support approach that explicitly handles future uncertainty, as the two-stage stochastic model presented in this paper does.

A process modelling approach to the evaluation of ship machinery configuration alternatives of LNG carriers

The increasing seaborne transportation of Liquefied Natural Gas (LNG) in the current volatile global market and energy supply environment puts a pressure on LNG vessels to be more efficient, environmentally friendly, and cost-effective. Modern LNG carriers feature complex and tightly integrated machinery systems to convert the onboard primary energy sources to useful energy demands for propulsion, electricity and heat. Therefore, process modelling and simulation techniques combined with an integrated systems approach is required for the evaluation of different configuration alternatives of LNG carriers. In this paper, we used our in-house process modelling framework DNVGL COSSMOS to develop a generic model of an LNG carrier integrated machinery system encompassing various propulsion and energy recovery technologies. The resulting system model was then coupled with a generic operational profile description accounting for various operating modes and intended trading routes of the vessel. The integrated LNG carrier machinery process model was subsequently used for the evaluation of different technology alternatives and machinery configurations. Namely, the model was used to size the gas-fuel compression trains; assess the introduction and optimal size of an LNG reliquefaction plant; compare electric and mechanical propulsion technologies; and, assess the introduction of energy recovery technologies such as shaft generators and exhaust gas economisers. The model-based studies resulted in an improved insight of this complex integrated machinery arrangement, revealing important performance trade-offs and interrelations between the vessel’s sub-systems. The results revealed high energy savings potential between 5 to 8% depending on the energy recovery options implemented, operating profile and trading route.

Functional modeling for green biomass supply chains

The biomass supply chain is a multiple-segment chain characterized by prominent complexity and uncertainty, and as such, it requires increased managerial efforts as compared to the case of a single operation management. This paper deals with the supply chain management of green (e.g. grass) biomass. Specifically, three different supply chain systems, in terms of machinery configurations, were analyzed and evaluated in terms of task times and cost performance. By using a functional modeling methodology, the structural representations of the systems, in terms of activities, actions, processes, and operations, were generated and implemented by the ExtendSim® simulation software. It was shown that the models can identify the bottlenecks of the systems and can be further used as a decision support system by testing various alternatives, in terms of the resources used and their dimensioning. Finally, the models were evaluated against the sensitivity on input parameters which are known with a level of uncertainty, i.e. the expected yield and the expected machinery performance.

Sensitivity Analysis of Transport Machinery Configuration in Underground Cavern Group Construction

The method of drilling and blasting is widely used in the construction of an underground cavern group. In the whole process, the slagging occupies 40–60% of overall cycle time. Therefore, it is important to optimize transport machinery configuration in order to reduce the time of slagging, and improve the overall efficiency of construction. In this paper, sensitivity analysis was applied to study the relations among the time of slagging, configuration of truck, and the number and capacity of loader when trackless transport was utilized in the construction of an underground cavern group. The analysis was based on the data of underground cavern group in JINPING II hydropower construction. The analysis results could be used to guide the configuration of mechanical equipment.

Assessing air emissions for uncertain life-cycle scenarios via responsive systems comparison method

A core aspect of assessing the environmental footprint of ships during the conceptual design phase is the estimation of the total air emission during the vessel life cycle. This process must take into account not only the design of an appropriate propulsion system and air emission controls but also the uncertainty related to changes in the vessel mission, such as future market conditions, new regulations, and contract opportunities. In this article, we model possible realizations of uncertain operational life-cycle scenarios using the responsive systems comparison method. This complex systems engineering method includes a strong emphasis on the structural and behavioral aspects of complexity, by mapping of function and form via machinery configuration variables and performance attributes. However, it also has the capacity to handle additional complexity aspects, discretizing the context into epoch variables, time into epochs and eras, and perception into utilities through the life cycle. We present a theoretical example related to the design of a general cargo container. The study illustrates the challenge in striking the correct balance between minimizing the emissions for an initial scenario, while providing additional performance capabilities to be efficient in the uncertainty of future market requirements.

Marine vessel classification based on passive sonar data: the cepstrum‐based approach

Marine vessel classification is complicated by the variability in the radiated signal of the marine vessel because of changing machinery configuration for the same class of vessels. Further, the radiated signal of the marine vessel propagating towards a distant receiver undergoes random fluctuations in phase, amplitude and frequency. The ambient noise at the receiver will further complicate the authors’ classification problem. The shallow underwater channel, in particular, where these classification systems are more likely to operate presents the most challenges because of severe time-varying multi-path. Cepstral approaches are proposed in this study, including cepstral features and average cepstral features to augment existing feature sets that are mostly based on spectral analysis. Analytical studies have been supported by simulation experiments and tests on real ship recorded data. The cepstral features with cepstral liftering process is able to significantly reduce the multipath distortion effects of shallow underwater channel whereas the average cepstral feature is able to notably reduce the time-varying channel effects.

Evaluation of Highway Maintenance Machinery Configuration Based on Fuzzy Comprehensive Evaluation Method

Aimed at the problem of the configuration program of road maintenance machinery index difficult to quantify, by using the method of fuzzy comprehensive in highway maintenance machinery configuration evaluation, depicted some fuzzy phenomena with subjective qualitative description , closely integrated quantitative analysis and subjective qualitative description. Try to find a reasonable and workable evaluation approach. So that make the mechanization of highway maintenance and construction more scientific and practical

Oxygen supply for oxycoal CO2 capture

The oxygen requirements for oxycoal combustion in a coal-fired power station are unusual in several respects. The amount of oxygen required is large, for instance a 500 MWe power plant will require around 10,000 tonnes per day of oxygen. The oxygen purity is low and the pressure required is essentially atmospheric pressure. There is no demand for any significant quantity of co-products, such as argon or nitrogen. The parasitic nature of the power means that high efficiency is important but the large scale means that capital cost should also be minimised. These factors open up new possibilities to optimise the process cycle and machinery configuration to minimise the power consumption of the air separation unit without compromising the requirement for low cost. This paper presents the results of a study to develop Air Products’ air separation unit (ASU) offerings for oxycoal CO2 capture projects. A scalable “reference plant” concept is described to match particular sizes of power generation equipment, taking into account factors such as safety, reliability, operating flexibility, efficiency, and of course low capital cost. We describe the selection of a process cycle to exploit the low purity requirements as well as the options for compression machinery and drivers as the scale of the plant increases and the sizes of referenced equipment limit the possibilities. We also explore integration with other elements of the oxycoal system, such as preheating condensate or heating and expanding pressurised nitrogen. Finally, the advantages and disadvantages of different execution strategies for air separation unit projects are discussed, as well as alternative commercial models for the supply of oxygen.

RESEARCH ON FAULT DIAGNOSIS SYSTEM OF ROTATING MACHINERY BASED ON MACHINERY CONFIGURATION

The knowledge representation of multi-symptom fuzzy production rules based on machinery configuration model, and the establishment and maintenance mechanism of knowledge base based on relational database are studied in the paper. With the support of ADO technique, the access to knowledge base and fault reasoning are realized. Application shows that the expert system has the merits of being simple to construct and of high reasoning efficiency. And, the adaptability and universality of fault diagnosis expert system to rotate machinery are greatly increased.