Design, Optimization and Modelling of High Power Density Direct-Drive Wheel Motor for Light Hybrid Electric Vehicles

Throughout the last few years, permanent magnet synchronous motors have been proven suitable candidates for hybrid electric vehicles (HEVs). Among them, the outer rotor topology with surface mounted magnets and concentrated windings seems to be very promising and has not been extensively investigated in literature. In this study, an overall optimization and modelling procedure is proposed for the design and operational assessment of high-power density direct-drive in-wheel motors, targeted towards a light HEV application. The analytical model of an HEV’s subsystems is then implemented for a more accurate evaluation of overall powertrain performance. Furthermore, a simple but effective cooling system configuration, which is taking into account the specific problem requirements, is also proposed

Influence of Soft Magnetic Materials Application to Squirrel Cage Induction Motor Design and Performance

Most of the electrical machines design studies found in literature lie on the concept that the design under investigation (and optimization) focuses mainly on the geometrical aspects of the machine and thus takes into account only a certain ferromagnetic material (i.e. iron) for its parts. These studies, give little or no information about the influence of material alternatives on the same (and optimized) design. From a manufacturer's point of view though, this information is crucial especially nowadays that there are a lot of commercially available materials in the market. In this context, this paper presents the results of a research project in the design stage of an energy efficient three phase squirrel cage induction motor (SCIM), by investigating the effects of several soft magnetic materials (adopted for its stator and/or its rotor parts) on multiple quantities of primary concern such as: efficiency, power factor, output torque, losses, weight and cost. After a brief proposed design procedure, a total of twenty-two different materials from recent manufacturers' data were examined. Also, the main electromagnetic analysis was performed through commercial analysis software. Simple ranking methods are also proposed here and the results obtained are then thoroughly discussed and commented

Permanent Magnet Synchronous Motor Design using Grey Wolf Optimizer Algorithm

Common high-torque low-speed motor drive schemes combine an induction motor coupled to the load by a mechanical subsystem which consists of gears, belt/pulleys or camshafts. Consequently, these setups present an inherent drawback regarding to maintenance needs, high costs and overall system deficiency. Thus, the replacement of such a conventional drive with a properly designed low speed permanent magnet synchronous motor (PMSM) directly coupled to the load, provides an attractive alternative. In this context, the paper deals with the design evaluation of a 5kW/50rpm radial flux PMSM with surface-mounted permanent magnets and inner rotor topology. Since the main goal is the minimization of the machine's total losses and therefore the maximization of its efficiency, the design is conducted by solving an optimization problem. For this purpose, the application of a new meta-heuristic optimization method called “Grey Wolf Optimizer” is studied. The effectiveness of the method in finding appropriate PMSM designs is then evaluated. The obtained results of the applied method reveal satisfactorily enhanced design solutions and performance when compared with those of other optimization techniques

Machine learning and deep learning based methods toward Industry 4.0 predictive maintenance in induction motors: Α state of the art survey

Purpose: Developments in Industry 4.0 technologies and Artificial Intelligence (AI) have enabled data-driven manufacturing. Predictive maintenance (PdM) has therefore become the prominent approach for fault detection and diagnosis (FD/D) of induction motors (IMs). The maintenance and early FD/D of IMs are critical processes, considering that they constitute the main power source in the industrial production environment. Machine learning (ML) methods have enhanced the performance and reliability of PdM. Various deep learning (DL) based FD/D methods have emerged in recent years, providing automatic feature engineering and learning and thereby alleviating drawbacks of traditional ML based methods. This paper presents a comprehensive survey of ML and DL based FD/D methods of IMs that have emerged since 2015. An overview of the main DL architectures used for this purpose is also presented. A discussion of the recent trends is given as well as future directions for research. Design/methodology/approach: A comprehensive survey has been carried out through all available publication databases using related keywords. Classification of the reviewed works has been done according to the main ML and DL techniques and algorithms Findings: DL based PdM methods have been mainly introduced and implemented for IM fault diagnosis in recent years. Novel DL FD/D methods are based on single DL techniques as well as hybrid techniques. DL methods have also been used for signal preprocessing and moreover, have been combined with traditional ML algorithms to enhance the FD/D performance in feature engineering. Publicly available datasets have been mostly used to test the performance of the developed methods, however industrial datasets should become available as well. Multi-agent system (MAS) based PdM employing ML classifiers has been explored. Several methods have investigated multiple IM faults, however, the presence of multiple faults occurring simultaneously has rarely been investigated. Originality/value: The paper presents a comprehensive review of the recent advances in PdM of IMs based on ML and DL methods that have emerged since 2015Peer Reviewe

Development and Implementation of a Low Cost μC- Based Brushless DC Motor Sensorless Controller: A Practical Analysis of Hardware and Software Aspects

The ongoing technological advancements of brushless DC motors (BLDCMs) have found a wide range of applications. For instance, ground-based electric vehicles, aerial drones and underwater scooters have already adopted high-performance BLDCMs. Nevertheless their adoption demands control systems to monitor torque, speed and other performance characteristics. Precise design structure and the particular motor functional characteristics are essential for the suitable configuration and implementation of an appropriate controller to suit a wide range of applications. Techniques which do not use Hall sensors should be used then. This paper deals with the analysis of hardware and software aspects during the development of such a microcontroller based and low cost speed controller for motors up to 500 W, along with its practical implementation. The sensorless method employed is based on the zero crossing point (ZCP) detection of the back-electromotive forces’ (back-EMF) differences, as the ZCPs of these quantities match to the time points at which the commutation sequence changes. Additionally, the study presents hardware and software details through calculations, figures, flowcharts and code, providing an insight of the practical issues that may arise in such a low cost prototype. Finally, results obtained by experiments validate the presented hardware/software architecture of the controller

A Combined Short Time Fourier Transform and Image Classification Transformer Model for Rolling Element Bearings Fault Diagnosis in Electric Motors

The most frequent faults in rotating electrical machines occur in their rolling element bearings. Thus, an effective health diagnosis mechanism of rolling element bearings is necessary from operational and economical points of view. Recently, convolutional neural networks (CNNs) have been proposed for bearing fault detection and identification. However, two major drawbacks of these models are (a) their lack of ability to capture global information about the input vector and to derive knowledge about the statistical properties of the latter and (b) the high demand for computational resources. In this paper, short time Fourier transform (STFT) is proposed as a pre-processing step to acquire time-frequency representation vibration images from raw data in variable healthy or faulty conditions. To diagnose and classify the vibration images, the image classification transformer (ICT), inspired from the transformers used for natural language processing, has been suitably adapted to work as an image classifier trained in a supervised manner and is also proposed as an alternative method to CNNs. Simulation results on a famous and well-established rolling element bearing fault detection benchmark show the effectiveness of the proposed method, which achieved 98.3% accuracy (on the test dataset) while requiring substantially fewer computational resources to be trained compared to the CNN approach

A Study of Noise Effect in Electrical Machines Bearing Fault Detection and Diagnosis Considering Different Representative Feature Models

As the field of fault diagnosis in electrical machines has significantly attracted the interest of the research community in recent years, several methods have arisen in the literature. Also, raw data signals can be acquired easily nowadays, and, thus, machine learning (ML) and deep learning (DL) are candidate tools for effective diagnosis. At the same time, a challenging task is to identify the presence and type of a bearing fault under noisy conditions, especially when relevant faults are at their incipient stage. Since, in real-world applications and especially in industrial processes, electrical machines operate in constantly noisy environments, a key to an effective approach lies in the preprocessing stage adopted. In this work, an evaluation study is conducted to find the most suitable signal preprocessing techniques and the most effective model for fault diagnosis of 16 conditions/classes, from a low-workload (computational burden) perspective using a well-known dataset. More specifically, the reliability and resiliency of conventional ML and DL models is investigated here, towards rolling bearing fault detection, simulating data that correspond to noisy industrial environments. Diverse preprocessing methods are applied in order to study the performance of different training methods from the feature extraction perspective. These feature extraction methods include statistical features in time-domain analysis (TDA); wavelet packet decomposition (WPD); continuous wavelet transform (CWT); and signal-to-image conversion (SIC), utilizing raw vibration signals acquired under varying load conditions. The noise effect is examined and thoroughly commented on. Finally, the paper provides accumulated usual practices in the sense of preferred preprocessing methods and training models under different load and noise conditions