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This paper deals with the development of analysis tools for axial-flux permanent-magnet machines. Normally, the study of this kind of machine involves three-dimensional (3-D) finite element method (FEM) (FEM-3-D) due to the 3-D nature of the magnetic problem. As it is widely known, the FEM-3-D software could take too much time, and both definition and solving processes of the problem may be very arduous. In this paper, a novel analysis procedure for axial-flux synchronous machines is proposed. This method consists in the combination of 2-D FEM simulations with analytical models based on the Fourier-series theory. The obtained results prove that the proposed method could be a very interesting option in terms of time and accuracy.Index Terms-Analytic models, axial-flux machines, finite element method (FEM), Fourier series.

This document reviews the current state of the art in the linear machine technology. First, the recent advancements in linear induction, switched reluctance and permanent magnet machines are presented. The ladder slit secondary configuration is identified as an interesting configuration for linear induction machines. In the case of switched reluctance machines, the mutually-coupled configuration has been found to equate the thrust capability of conventional permanent magnet machines. The capabilities of the so called linear primary permanent magnet, viz. switched-flux, flux-reversal, doubly-salient and vernier machines are presented afterwards. A guide of different options to enhance several characteristics of linear machines is also listed. A qualitative comparison of the capabilities of linear primary permanent magnet machines is given later, where linear vernier and switched-flux machines are identified as the most interesting configurations for long stroke applications. In order to demonstrate the validity of the presented comparison, three machines are selected from the literature, and their capabilities are compared under the same conditions to a conventional linear permanent magnet machine. It is found that the flux-reversal machines suffer from a very poor power factor, whereas the thrust capability of both vernier and switched-flux machines is confirmed. However, the overload capability of these machines is found to be substantially lower than the one from the conventional machine. Finally, some different research topics are identified and suggested for each type of machine.

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Due to the importance of sensors in railway traction drives availability, sensor fault diagnosis has become a key point in order to move from preventive maintenance to condition-based maintenance. Most research works are limited to sensor fault detection and isolation, but only a few of them analyze the types of sensor faults, such as offset or gain, with the aim of reconfiguring the sensor in order to implement a fault tolerant system. This article is based on a fusion of model-based and data-driven techniques. First, an observer-based approach, using a Sliding Mode observer, is utilized for sensor fault reconstruction in real time. Then, once the fault is detected, a time window of sensor measurements and sensor fault reconstruction is sent to the remote maintenance center for fault evaluation. Finally, an offline processing is carried out to discriminate between gain and offset sensor faults, in order to get a maintenance decision-making to reconfigure the sensor during the next train stop. Fault classification is done by means of histograms and statistics. The technique here proposed is applied to the DC-link voltage sensor in a railway traction drive and is validated in a hardware-in-the-loop platform.Sensors 2020, 20, 962 2 of 21 which normally requires an important effort in complex systems, but as an advantage, it is suitable for on-board and real-time implementation, as it requires neither a large quantity of data, nor high computational resources. The model-based techniques can be divided into two parts: the residual generation and the residual evaluation. On the other hand, data-driven techniques do not need a knowledge of the physical system, but the computational burden is normally higher. Moreover, data quantity to process and storage is large, so it makes difficult its on-board implementation.