Monitoring energy usage of heavy-haul iron ore trains with on-board energy meter for improving energy efficiency

Purpose-For billing purposes,heavy-haul locomotives in Sweden are equipped with on-board energy meters,which can record several parameters,e.g.,used energy,regenerated energy,speed and position.Since there is a strong demand for improving energy efficiency in Sweden,data from the energy meters can be used to obtain a better understanding of the detailed energy usage of heavy-haul trains and identify potential for future improvements.Design/methodology/approach-To monitor energy efficiency,the present study,therefore,develops key performance indicators(KPIs),which can be calculated with energy meter data to reflect the energy efficiency of heavy-haul trains in operation.Energy meter data of IORE class locomotives,hauling highly uniform 30-tonne axle load trains with 68 wagons,together with additional data sources,are analysed to identify significant parameters for describing driver influence on energy usage.Findings-Results show that driver behaviour varies significantly and has the single largest influence on energy usage.Furthermore,parametric studies are performed with help of simulation to identify the influence of different operational and rolling stock conditions,e.g.,axle loads and number of wagons,on energy usage.Originality/value-Based on the parametric studies,some operational parameters which have significant impact on energy efficiency are found and then the KPIs are derived.In the end,some possible measures for improving energy performance in heavy-haul operations are given.

Partial Iterative Decode of Turbo Codes for On-Board Processing Satellite Platform

There is a contradiction between high processing complexity and limited processing resources when turbo codes are used on the on-board processing(OBP)satellite platform.To solve this problem,this paper proposes a partial iterative decode method for on-board application,in which satellite only carries out limited number of iteration according to the on-board processing resource limitation and the throughput capacity requirements.In this method,the soft information of parity bits,which is not obtained individually in conventional turbo decoder,is encoded and forwarded along with those of information bits.To save downlink transmit power,the soft information is limited and normalized before forwarding.The iteration number and limiter parameters are optimized with the help of EXIT chart and numerical analysis,respectively.Simulation results show that the proposed method can effectively decrease the complexity of onboard processing while achieve most of the decoding gain..

Role of on-board discharge in shock wave drag reduction and plasma cloaking

In the present paper, a physical model is proposed for reducing the problem of the drag reduction of an attached bow shock around the nose of a high-speed vehicle with on-board discharge, to the problem of a balance between the magnetic pressure and gas pressure of plane shock of a partially ionized gas consisting of the environmental gas around the nose of the vehicle and the on-board discharge-produced plasma. The relation between the shock strength and the discharge-induced magnetic pressure is studied by means of a set of one-fluid, hydromagnetic equations reformed for the present purpose, where the discharge-induced magnetic field consists of the electron current （produced by the discharge）-induced magnetic field and the partially ionized gas flow-induced one. A formula for the relation between the above parameters is derived. It shows that the discharge-induced magnetic pressure can minimize the shock strength, successfully explaining the two recent experimental observations on attached bow shock mitigation and elimination in a supersonic flow during on-board discharge [Phys. Plasmas 9 （2002） 721 and Phys. Plasmas 7 （2000） 1345]. In addition, the formula implies that the shock elimination leaves room for a layer of higher-density plasma rampart moving around the nose of the vehicle, being favourable to the plasma radar cloaking of the vehicle. The reason for it is expounded.

Optimal energy saving in DC railway system withon-board energy storage system by using peak demand cutting strategy

A problem of peak power in DC-electrified railway systems is mainly caused by train power demand during acceleration.If this power is reduced,substation peak power will be significantly decreased.This paper presents a study on optimal energy saving in DC-electrified railway with on-board energy storage system(OBESS) by using peak demand cutting strategy under different trip time controls.The proposed strategy uses OBESS to store recovered braking energy and find an appropriated time to deliver the stored energy back to the power network in such a way that peak power of every substations is reduced.Bangkok Mass Transit System(BTS)-Silom Line in Thailand is used to test and verify the proposed strategy.The results show that substation peak power is reduced by63.49% and net energy consumption is reduced by 15.56%using coasting and deceleration trip time control.

Radiation Tolerant Viterbi Decoders for On-Board Processing(OBP) in Satellite Communications

Modern satellite communication systems require on-board processing(OBP)for performance improvements,and SRAM-FPGAs are an attractive option for OBP implementation.However,SRAM-FPGAs are sensitive to radiation effects,among which single event upsets(SEUs)are important as they can lead to data corruption and system failure.This paper studies the fault tolerance capability of a SRAM-FPGA implemented Viterbi decoder to SEUs on the user memory.Analysis and fault injection experiments are conducted to verify that over 97%of the SEUs on user memory would not lead to output errors.To achieve a better reliability,selective protection schemes are then proposed to further improve the reliability of the decoder to SEUs on user memory with very small overhead.Although the results are obtained for a specific FPGA implementation,the developed reliability estimation model and the general conclusions still hold for other implementations.

Fault diagnosis for on-board equipment of train control system based on CNN and PSO-SVM hybrid model

Rapid and precise location of the faults of on-board equipment of train control system is a significant factor to ensure reliable train operation.Text data of the fault tracking table of on-board equipment are taken as samples,and an on-board equipment fault diagnosis model is designed based on the combination of convolutional neural network(CNN)and particle swarm optimization-support vector machines(PSO-SVM).Due to the characteristics of high dimensionality and sparseness of fault text data,CNN is used to achieve feature extraction.In order to decrease the influence of the imbalance of the fault sample data category on the classification accuracy,the PSO-SVM algorithm is introduced.The fully connected classification part of CNN is replaced by PSO-SVM,the extracted features are classified precisely,and the intelligent diagnosis of on-board equipment fault is implemented.According to the test analysis of the fault text data of on-board equipment recorded by a railway bureau and comparison with other models,the experimental results indicate that this model can obviously upgrade the evaluation indexes and can be used as an effective model for fault diagnosis for on-board equipment.

Stream-computing of High Accuracy On-board Real-time Cloud Detection for High Resolution Optical Satellite Imagery

This paper focuses on the time efficiency for machine vision and intelligent photogrammetry, especially high accuracy on-board real-time cloud detection method. With the development of technology, the data acquisition ability is growing continuously and the volume of raw data is increasing explosively. Meanwhile, because of the higher requirement of data accuracy, the computation load is also becoming heavier. This situation makes time efficiency extremely important. Moreover, the cloud cover rate of optical satellite imagery is up to approximately 50%, which is seriously restricting the applications of on-board intelligent photogrammetry services. To meet the on-board cloud detection requirements and offer valid input data to subsequent processing, this paper presents a stream-computing of high accuracy on-board real-time cloud detection solution which follows the “bottom-up” understanding strategy of machine vision and uses multiple embedded GPU with significant potential to be applied on-board. Without external memory, the data parallel pipeline system based on multiple processing modules of this solution could afford the “stream-in, processing, stream-out” real-time stream computing. In experiments, images of GF-2 satellite are used to validate the accuracy and performance of this approach, and the experimental results show that this solution could not only bring up cloud detection accuracy, but also match the on-board real-time processing requirements.

An Interconnected Multi-Plane Multi-Stage Fault-Tolerant On-Board Switching Fabric

The harsh space radiation environment compromises the reliability of an on-board switching fabric by leading to cross-point and switching element(SE)faults.Different from traditional faulttolerant switching fabrics only taking crosspoint faults into account,a novel Input and Output Parallel Clos network,referred to as the(p_1,p_2)-IOPClos,is proposed to tolerate both cross-point and SE faults.In the(p_1,p_2)-IOPClos,there are p_1 and p_2 expanded parallel switching planes in the input and output stages,respectively.The multiple input/output switching planes are interconnected through the middle stage to provide multiple paths in each stage by which the network throughput can be increased remarkably.Furthermore,the network reliability of the(p_1,p_2)-IOPClos under the above both kinds of faults is analyzed.The corresponding implementation cost is also presented along with the network size.Both theoretical analysis and numerical results indicate that the(p_1,p_2)-IOPClos outperforms traditional Clos-type networks at reliability,while has less implementation cost than the multi-plane Clos network.

A real-time fault-tolerant scheduling algorithm with low dependability cost in on-board computer system

To make the on-board computer system more dependable and real-time in a satellite, an algorithm of the fault-tolerant scheduling in the on-board computer system with high priority recovery is proposed in this paper. This algorithm can schedule the on-board fault-tolerant tasks in real time. Due to the use of dependability cost, the overhead of scheduling the fault-tolerant tasks can be reduced. The mechanism of the high priority recovery will improve the response to recovery tasks. The fault-tolerant scheduling model is presented simulation results validate the correctness and feasibility of the proposed algorithm.

Novel satellite transport protocol with on-board spoofing proxy

As a result of the exponential growing rate of worldwide Internet usage, satellite systems are required to support broadband Internet applications. The transmission control protocol （TCP） which is widely used in the Internet, performs very well on wired networks. However, in the case of satellite channels, clue to the delay and transmission errors, TCP performance degrades significantly and bandwidth of satellite links can not be fully utilized. To improve the TCP performance, a new idea of placing a TCP spoofing proxy in the satellite is considered. A Novel Satellite Transport Protocol （NSTP） which takes advantage of the special properties of the satellite channel is also proposed. By using simulation, as compared with traditional TCPs, the on-board spoofing proxy integrated with the special transport protocol can significantly enhance throughput performance on the high BER satellite link, the time needed to transfer files and the bandwidth used in reverse path are sharply reduced.

Adaptive Constrained On-board Guidance Technology forPowered Glide Vehicle

To make full use of expanded maneuverability and increased range,adaptive constrained on-board guidance technology is the key capability for a glide vehicle with a double-pulse rocket engine,especially under the requirements of desired target changing and on-line reconfigurable control and guidance.Based on the rapid footprint analysis,whether the new target is within the current footprint area is firstly judged.If not,the rocket engine ignites by the logic obtained from the analysis of optimal flight range by the method of hp-adaptive Gauss pseudospectral method(hp-GPM).Then,an on-board trajectory generation method based on powered quasi-equilibrium glide condition(QEGC)and linear quadratic regulator(LQR)method is used to guide the vehicle to the new target.The effectiveness of the guidance method consisted of powered on-board trajectory generation,LQR trajectory tracking,footprint calculation,and ignition time determination is indicated by some simulation examples.

Thermodynamic Aspects of the Graphene/Graphane/Hydrogen Systems: Relevance to the Hydrogen On-Board Storage Problem

The present analytical review is devoted to the current problem of thermodynamic stability and related thermodynamic characteristics of the following graphene layers systems: 1) double-side hydrogenated graphene of composition CH (theoretical graphane) (Sofo et al. 2007) and experimental graphane (Elias et al. 2009);2) theoretical single-side hydrogenated graphene of composition CH;3) theoretical single-side hydrogenated graphene of composition C2H (graphone);4) experimental hydrogenated epitaxial graphene, bilayer graphene and a few layers of graphene on SiO2 or other substrates;5) experimental and theoretical single-external side hydrogenated single-walled carbon nanotubes, and experimental hydrofullerene C60H36;6) experimental single-internal side hydrogenated (up to C2H or CH composition) graphene nanoblisters with intercalated high pressure H2 gas inside them, formed on a surface of highly oriented pyrolytic graphite or epitaxial graphene under the atomic hydrogen treatment;and 7) experimental hydrogenated graphite nanofibers-multigraphene with intercalated solid H2 nano-regions of high density inside them, relevant to solving the problem of hydrogen on-board storage (Nechaev 2011-2012).

Comparing of Hydrogen On-Board Storage by the Largest Car Companies, Relevance to Prospects for More Efficient Technologies

It presented a comparative consideration of General Motors long-term activities on the current subject of fuel-cell-powered electric vehicles vs Toyota Mirai recent results, relevant to prospects on more efficient and safe technologies of the hydrogen on-board storage. It also presented a call on the project International cooperation. The main aim of this paper is to attract attention of General Motors, Toyota and/or other large car companies to a real possibility of developing and using, in the nearest future, of the break-through hydrogen on-board storage technology based on the solid H2 intercalation into graphite nanostructures.

On-board modeling of gravity fields of elongated asteroids using Hopfield neural networks

To rapidly model the gravity field near elongated asteroids,an intelligent inversion method using Hopfield neural networks(HNNs)is proposed to estimate on-orbit simplified model parameters.First,based on a rotating mass dipole model,the gravitational field of asteroids is characterized using a few parameters.To solve all the parameters of this simplified model,a stepped parameter estimation model is constructed based on different gravity field models.Second,to overcome linearization difficulties caused by the coupling of the parameters to be estimated and the system state,a dynamic parameter linearization technique is proposed such that all terms except the parameter terms are known or available.Moreover,the Lyapunov function of the HNNs is matched to the problem of minimizing parameter estimation errors.Equilibrium values of the Lyapunov function areused as estimated values.The proposed method is applied to natural elongated asteroids 216 Kleopatra,951 Gaspra,and 433 Eros.Simulation results indicate that this method can estimate the simplified model parameters rapidly,and that the estimated simplified model provides a good approximation of the gravity field of elongated asteroids.

Research on text fault recognition for on-board equipment of a C3 train control system based on an integrated XGBoost algorithm

The robust guarantee of train control on-board equipment is inextricably linked to the safe functioning of a high-speed train.A fault diagnostic model of on-board equipment is built utilizing the integrated learning XGBoost(eXtreme Gradient Boosting)algorithm to help technicians assess the malfunction category of high-speed train control on-board equipment accurately and rapidly.The XGBoost algorithm iterates multiple decision tree models to improve the accuracy of fault diagnosis by lifting the predicted residual and adding regular terms.To begin,the text features were extracted using the improved TF-IDF(Term Frequency-Inverse Document Frequency)approach,and 24 fault feature words were chosen and converted into weight word vectors.Secondly,considering the imbalanced fault categories in the data set,the ADASYN(Adaptive Synthetic sampling)adaptive synthetically oversampling technique was used to synthesize a few category fault samples.Finally,the data samples were split into training and test sets based on the fault text data of CTCS-3train control on-board equipment recorded by Guangzhou Railway Group maintenance personnel.The XGBoost model was utilized to realize the automatic fault location of the test set after optimized parameter tuning through grid search.Compared with other methods,the evaluation index of the XGBoost model was significantly improved.The diagnostic accuracy reached 95.43%,which verifies the effectiveness of the method in text fault diagnosis.

Design of on-board calibration methods for a digital sun sensor based on Levenberg–Marquardt algorithm and Kalman filters

Digital sun sensor is one of the most important sensors used in the Attitude Determination System(ADS)of the satellite.Due to the harsh environmental conditions that exist in the space,various distortions may occur in the sun sensor optical system that lead to the reduced accuracy of this equipment.So,it is necessary to recalibrate the optical parameters of the aforementioned sensors.For this purpose,first a novel attitude independent error model is proposed for the SS-411 sun sensor that includes the central point of the CCD array,installation error,filter thickness and sensor misalignment.So,the mutual interfaces between the sensor parameters are considered in the developed model.In order to extract the sensor parameters,a nonlinear optimization technique called the Levenberg–Marquardt is applied to the developed model as a batch algorithm.In addition,the Extended Kalman Filter(EKF)and the Unscented Kalman Filter(UKF)have been utilized as sequential strategies.It will be shown that by considering a worst case of variation amount for sensor parameters,an accuracy improvement of about 17°is achieved by the developed calibration algorithms.Comparison between the developed algorithms represents that UKF has higher accuracy,shorter time convergence but higher computational load.

Ultrafine particle emission characteristics of diesel engine by on-board and test bench measurement

This study investigated the emission characteristics of ultra.fine particles based on test bench and on-board measurements. The bench test results showed the ultrafine particle number concentration of the diesel engine to be in the range of （0.56-8.35）×10^8 cm^-3. The on-board measurement results illustrated that the ultra_fine particles were strongly correlated with changes in real-world driving cycles. The particle number concentration was down to 2.0 ×10^6 cm^-3 and 2.7 ×10^7 cm^-3 under decelerating and idling operations and as high as 5.0×10^8 cm^-3 under accelerating operation. It was also indicated that the particle number measured by the two methods increased with the growth of engine load at each engine speed in both cases. The particle number presented a ＂U＂ shaped distribution with changing speed at high engine load conditions, which implies that the particle number will reach its lowest level at medium engine speeds. The particle sizes of both measurements showed single mode distributions. The peak of particle size was located at about 50-80 nm in the accumulation mode particle range. Nucleation mode particles will significantly increase at low engine load operations like idling and decelerating caused by the high concentration of unburned organic compounds.

Real-world fuel consumption of light-duty passenger vehicles using on-board diagnostic (OBD) systems

An increasing discrepancy between real-world and type-approval fuel consumption for light-duty passenger vehicles(LDPVs)has been reported by several studies.Normally,real-world fuel consumption is measured primarily by a portable emission measurement system.The on-board diagnostic(OBD)approach,which is flexible and offers high-resolution data collection,is a promising fuel consumption monitoring method.Three LDPVs were tested with a laboratory dynamometer based on a type-approval cycle,the New European Driving Cycle(NEDC).Fuel consumption was measured by the OBD and constant-volume sampling system(CVS,a regulatory method)to verify the accuracy of the OBD values.The results of the OBD method and the regulatory carbon balance method exhibited a strong linear correlation(e.g.,R^2=0.906-0.977).Compared with the carbon balance results,the fuel consumption results using the OBD were 7.1%±4.3%lower on average.Furthermore,the real-world fuel consumption of six LDPVs was tested in Beijing using the OBD.The results showed that the normalized NEDC real-world fliel consumption of the tested vehicles was 13%±17%higher than the type-approval-based fuel consumption.Because the OBD values are lower than the actual fuel consumption,using a carbon balance method may result in a larger discrepancy between real-word and type-approval ftiel consumption.By means of the operating mode binning and micro trip methods,a strong relationship(R^2=0.984)was established between the average speed and relative fuel consumption.For congested roads(average vehicle speed less than 25 km/h),the fuel consumption of LDPVs is highly sensitive to changes in average speed.

Fault Classification for On-board Equipment of High-speed Railway Based on Attention Capsule Network

The conventional troubleshooting methods for high-speed railway on-board equipment, with over-reliance on personnel experience, is characterized by one-sidedness and low efficiency. In the process of high-speed train operation, numerous text-based onboard logs are recorded by on-board computers. Machine learning methods can help technicians make a correct judgment of fault types using the on-board log reasonably. Therefore, a fault classification model of on-board equipment based on attention capsule networks is proposed. This paper presents an empirical exploration of the application of a capsule network with dynamic routing in fault classification. A capsule network can encode the internal spatial part-whole relationship between various entities to identify the fault types. As the importance of each word in the on-board log and the dependencies between them have a significant impact on fault classification, an attention mechanism is incorporated into the capsule network to distill important information. Considering the imbalanced distribution of normal data and fault data in the on-board log, the focal loss function is introduced into the model to adjust the imbalanced data. The experiments are conducted on the on-board log of a railway bureau and compared with other baseline models. The experimental results demonstrate that our model outperforms the compared baseline methods, proving the superiority and competitiveness of our model.

On-board measurement of emissions from liquefied petroleum gas,gasoline and diesel powered passenger cars in Algeria

On-board measurements of unit emissions of CO,HC,NOx and CO2 were conducted on 17 private cars powered by different types of fuels including gasoline,dual gasoline–liquefied petroleum gas（LPG）,gasoline,and diesel. The tests performed revealed the effect of LPG injection technology on unit emissions and made it possible to compare the measured emissions to the European Artemis emission model. A sequential multipoint injection LPG kit with no catalyst installed was found to be the most efficient pollutant reduction device for all of the pollutants,with the exception of the NOx. Specific test results for a sub-group of LPG vehicles revealed that LPG-fueled engines with no catalyst cannot compete with catalyzed gasoline and diesel engines. Vehicle age does not appear to be a determining parameter with regard to vehicle pollutant emissions. A fuel switch to LPG offers many advantages as far as pollutant emissions are concerned,due to LPG＇s intrinsic characteristics.However,these advantages are being rapidly offset by the strong development of both gasoline and diesel engine technologies and catalyst converters. The LPG＇s performance on a chassis dynamometer under real driving conditions was better than expected. The enforcement of pollutant emission standards in developing countries is an important step towards introducing clean technology and reducing vehicle emissions.