Linear Machines for Long Stroke Applications: a review

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 arepresented. The ladder slit secondary configuration is identified as an interesting configuration for linearinduction machines. In the case of switched reluctance machines, the mutually-coupled configuration hasbeen found to equate the thrust capability of conventional permanent magnet machines. The capabilities ofthe so called linear primary permanent magnet, viz. switched-flux, flux-reversal, doubly-salient and verniermachines are presented afterwards. A guide of different options to enhance several characteristics of linearmachines is also listed. A qualitative comparison of the capabilities of linear primary permanent magnetmachines is given later, where linear vernier and switched-flux machines are identified as the most interestingconfigurations 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 conditionsto a conventional linear permanent magnet machine. It is found that the flux-reversal machines suffer froma very poor power factor, whereas the thrust capability of both vernier and switched-flux machines isconfirmed. However, the overload capability of these machines is found to be substantially lower than theone from the conventional machine. Finally, some different research topics are identified and suggested foreach type of machine

Industrial Design of Electric Machines Supported with Knowledge-Based Engineering Systems

The demand for electric machines has increased in the last decade, mainly due to applications that try to make a full transition from fuel to electricity. These applications encounter the need for tailor-made electric machines that must meet demanding requirements. Therefore, it is necessary for small-medium companies to adopt new technologies offering customized products fulfilling the customers’ requirements according to their investment capacity, simplify their development process, and reduce computational time to achieve a feasible design in shorter periods. Furthermore, they must find ways to retain know-how that is typically kept within each designer to retrieve it or transfer it to new designers. This paper presents a framework with an implementation example of a knowledge-based engineering (KBE) system to design industrial electric machines to support this issue. The devised KBE system groups the main functionalities that provide the best outcome for an electric machine designer as development-process traceability, knowledge accessibility, automation of tasks, and intelligent support. The results show that if the company effectively applies these functionalities, they can leverage the attributes of KBE systems to shorten time-to-market. They can also ensure not losing all knowledge, information, and data through the whole development process

A Requirement Engineering Framework for Electric Motors Development

The applications using electric motors have increased in the last decade. Some of these applications encounter the need for tailor-made motors that must meet demanding requirements. Therefore, the specification stage of an electric motor is a critical part of its development. If this stage is properly addressed, then future failures in the development process can be avoided. This paper presents a requirement engineering framework to support small-medium electric motors designers/manufacturers with the development of their product. The framework identifies the stakeholders and the tasks that they should undertake to finish a successful requirements specification stage. The framework is made from the designer/manufacturer’s perspective and it emphasizes the derivation of specialized requirements (lower-level). The result of the framework is well-defined requirements that form the design requirements specification of the motor that leads to the beginning of the design stage

Field Weakening Characteristics Computed with FEM-Coupled Algorithms for Brushless AC Motors

Finite Element Method (FEM) analysis tools are the most adopted in the design of brushless alternating current motors due to the advantage of considering multi-physics effects with dependencies of variables such as cross-coupling, saturation and others that are not possible to be modeled analytically with high precision. During the design process designers compute operation points such as maximum torque per ampere or flux weakening characteristics that cannot be targeted directly on the FEM tool. Therefore, designers make a sweep of simulations and post-processed the data in order to obtain the results, this is repetitive particularly in the conceptual phase of the design where features of the motor are still not defined. This paper presents nine algorithms as an alternative to compute with iterative methods operation points that cannot be targeted directly on a FEM tool. The algorithms must be coupled to the FEM tool and can compute complex points such as the characteristic current and modes of operations limits within acceptable range of error and times of execution for practical purposes. Validation of the algorithms using Jython is presented with results for the three types of brushless motors (non-salient, interior permanent magnet and reluctance motor)

A Hybrid Sensor Fault Diagnosis for Maintenance in Railway Traction Drives

Due to the importance of sensors in railway traction drives availability, sensor fault diagnosis has become a key point in order tomove frompreventivemaintenance to condition-basedmaintenance. 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 timewindowof sensormeasurements and sensor fault reconstruction is sent to the remotemaintenance 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