Search results

This study aims to improve the availability of regenerative braking for urban metro vehicles by introducing a sensorless operational temperature estimation method for the braking resistor (BR) onboard the vehicle, which overcomes the vulnerability of having conventional temperature sensor.

In this study, the energy model based sensorless estimation method is developed. By analyzing the structure and the convection dissipation process of the BR onboard the vehicle, the energy-based operational temperature model of the BR and its cooling domain is established. By adopting Newton's law of cooling and the law of conservation of energy, the energy and temperature dynamic of the BR can be stated. To minimize the use of all kinds of sensors (including both thermal and electrical), a novel regenerative braking power calculation method is proposed, which involves only the voltage of DC traction network and the duty cycle of the chopping circuit; both of them are available for the traction control unit (TCU) of the vehicle. By utilizing a real-time iterative calculation and updating the parameter of the energy model, the operational temperature of the BR can be obtained and monitored in a sensorless manner.

In this study, a sensorless estimation/monitoring method of the operational temperature of BR is proposed. The results show that it is possible to utilize the existing electrical sensors that is mandatory for the traction unit’s operation to estimate the operational temperature of BR, instead of adding dedicated thermal sensors. The results also validate the effectiveness of the proposal is acceptable for the engineering practical.

The proposal of this study provides novel concepts for the sensorless operational temperature monitoring of BR onboard rolling stocks. The proposed method only involves quasi-global electrical variable and the internal control signal within the TCU.

The aim of this work is to research and design an expert diagnosis system for rail vehicle driven by data mechanism models.

The expert diagnosis system utilizes statistical and deep learning methods to model the real-time status and historical data features of rail vehicle. Based on data mechanism models, it predicts the lifespan of key components, evaluates the health status of the vehicle and achieves intelligent monitoring and diagnosis of rail vehicle.

The actual operation effect of this system shows that it has improved the intelligent level of the rail vehicle monitoring system, which helps operators to monitor the operation of vehicle online, predict potential risks and faults of vehicle and ensure the smooth and safe operation of vehicle.

This system improves the efficiency of rail vehicle operation, scheduling and maintenance through intelligent monitoring and diagnosis of rail vehicle.

The purpose of this paper is to characterize distracted driving by quantifying the response time and response intensity to an emergency stop using the driver’s physiological states.

Field tests with 17 participants were conducted in the connected and automated vehicle test field. All participants were required to prioritize their primary driving tasks while a secondary nondriving task was asked to be executed. Demographic data, vehicle trajectory data and various physiological data were recorded through a biosignalsplux signal data acquisition toolkit, such as electrocardiograph for heart rate, electromyography for muscle strength, electrodermal activity for skin conductance and force-sensing resistor for braking pressure.

This study quantified the psychophysiological responses of the driver who returns to the primary driving task from the secondary nondriving task when an emergency occurs. The results provided a prototype analysis of the time required for making a decision in the context of advanced driver assistance systems or for rebuilding the situational awareness in future automated vehicles when a driver’s take-over maneuver is needed.

The hypothesis is that the secondary task will result in a higher mental workload and a prolonged reaction time. Therefore, the driver states in distracted driving are significantly different than in regular driving, the physiological signal improves measuring the brake response time and distraction levels and brake intensity can be expressed as functions of driver demographics. To the best of the authors’ knowledge, this is the first study using psychophysiological measures to quantify a driver’s response to an emergency stop during distracted driving.

In electric system of high-speed trains, neutral sections are set to balance the three-phase load. When passing neutral sections, the train should detach from the power supply for a short time. To permanent magnet synchronous motors (PMSMs) traction system, the voltage of DC link will increase quickly due to the back-EMF of PMSM during this time. Although the energy consumption braking method can be adopted to consume the feedback energy. It not only wastes energy, but also causes more speed drop of the train. The paper aims to discuss these issues.

In order to get better performance when the train is under passing neutral section condition, a suitable control method is proposed, in which the torque command is set to zero and d-axis current order remains unchanged during passing neutral section. Based on a co-simulation model, the influences of this method on the PMSMs traction system are compared with that of traditional method, which is used in induction motors traction system. This model combines both control strategy and finite element model of motor, which can take the effects of magnetic saturation and power loss into consideration.

In PMSMs traction system, PMSMs work as generators during neutral section, and charge to DC bus, which may cause over-voltage damage. Moreover, there would be strong torque shock at the moment of power cut-off. It is finally found that, with the suitable control method, the high-speed train can pass the neutral section with less speed drop, less torque shock and little DC link voltage rise.

The control method proposed in this paper is easier to achieve and gets a better performance of PMSMs traction system in high-speed train compared with the traditional method. Furthermore, the co-simulation model is much closer to reality than the analytical model.

It is well known in the mining industry that the increase in failures and breakdowns is due mainly to a poor maintenance policy for the equipment, in addition to the difficult access that specialized personnel have to combat the breakdown, which translates into more machine downtime. For this reason, this study aims to propose a remote assistance model for diagnosing and repairing critical breakdowns in mining industry trucks using augmented reality techniques and data analytics with a quality approach that considerably reduces response times, thus optimizing human resources.

In this work, the six-phase CRIPS-DM methodology is used. Initially, the problem of fault diagnosis in trucks used in the extraction of material in the mining industry is addressed. The authors then propose a model under study that seeks a real-time connection between a service technician attending the truck at the mine site and a specialist located at a remote location, considering the data transmission requirements and the machine's characterization.

It is considered that the theoretical results obtained in the development of this study are satisfactory from the business point of view since, in the first instance, it fulfills specific objectives related to the telecare process. On the other hand, from the data mining point of view, the results manage to comply with the theoretical aspects of the establishment of failure prediction models through the application of the CRISP-DM methodology. All of the above opens the possibility of developing prediction models through machine learning and establishing the best model for the objective of failure prediction.

The original contribution of this work is the proposal of the design of a remote assistance model for diagnosing and repairing critical failures in the mining industry, considering augmented reality and data analytics. Furthermore, the integration of remote assistance, the characterization of the CAEX, their maintenance information and the failure prediction models allow the establishment of a quality-based model since the database with which the learning machine will work is constantly updated.

The purpose of this paper is to introduce an aid for teaching transverse flux permanent magnet machines (TFPMs) with emphasis on their torque production.

The Lorentz force law is applied to fictitious current loops emulating the permanent magnets (PMs) mounted on the rotor according to different arrangements; the air gap flux density is created by the armature current.

Implemented in a master lecture on special AC machines, the proposed approach has revealed a renewed interest in electromagnetic fundamentals for pedagogical purposes. It makes simple the explanation of the principle of operation of a class of AC machines reputed by the complexity of their magnetic circuits. The latter incorporates axially stacked decoupled sub-circuits, one per phase generating alternating magnetic fields. More specifically, there is common air gap, shared by the machine phases, in which a rotating magnetic field is created by the superposition of the PM contribution and the armature one.

Accounting for the complexity of the magnetic circuits and the three-dimensional (3D) flux paths characterizing TFPMs, a 3D finite element analysis (FEA) is required for the validation of the analytical predictions. Nevertheless, such a 3D FEA validation is far from being obvious to be carried on within a master lecture.

While the basis of Lorentz forces resulting from fictitious current loops emulating PMs has been considered in some referenced papers, its simple and pedagogical application to assess the torque production of several TFPM concepts represents the added value of the present paper.

This paper describes the development of closed loop control techniques for matrix converter fed induction motor drives. A prototype drive rated at 2.5kW is used to demonstrate closed loop speed control using vector control technique. Solutions to the special problems associated with the power circuit and implementation of closed loop control in the matrix converter drive are given. Experimental results demonstrating the control techniques are presented for both motoring and generating operations of the matrix converter drive in transient and steady‐state.

THE MRCA head up display incorporates a camera recorder, fig J, which is mounted directly on the pilot's display unit, and is used to provide a film record of the HUD symbology superimposed on the pilot's view of the outside world.