Fault detection in induction motors with noninvasive methods using stray magnetic flux is a convenient way to maintain reliability and reduce maintenance costs. An online monitoring of critical systems like railway transportation, industrial conveyors, or water management could benefit from the rapid identification of incipient failures. One of the principal problems with the fault identification in induction motors is that the characteristic failure signature changes depending on various factors such as motor construction, controller device, load configuration, and rated power. It is common to use artificial intelligence tools like artificial neural networks to identify and classify failures; however, black-box models are not scalable and are unique to each case. In addition, black-box models do not provide insights into the signal relationship among useful signal properties to detect failures. In this work, a fuzzy inference clustering (FIC) method is presented to provide an interpretable classification model to infer fuzzy membership rules, allowing the detection of failures in induction motors with stray magnetic flux signals. The proposed method achieves 100% accuracy in detecting two ball-bearing failures in the outer raceway using axial or radial flux. The obtained model is human-readable, and its real-time implementation requires minimal computational resources, suitable for edge computing applications.

Induction motors are robust machines that are present in various sectors, including industry, transportation, mining, and others, employing a wide range of applications such as pumps, presses, and mills, among others. Despite their robustness, induction motors can exhibit both electrical and mechanical faults, with mixed eccentricity being a mechanical rotor fault that can occur due to errors in the manufacturing process or incorrect bearing mounting, which in turn can cause adverse consequences for the machine. In recent years, various methodologies have been studied and presented for fault diagnosis in induction motors, although many of them present certain limitations, as in some cases, some phenomena may mask the fault signals, and even in some cases, the load may affect the fault-related frequency amplitudes, thus leading to false diagnoses. This paper analyzes the effect of the load on the amplitude of the rotor rotational frequency components of the current signals of an induction motor operating under mixed eccentricity fault by applying the Fast Fourier Transform and examining the frequency components at four different load levels.

According to the World Health Organization (WHO), the population of people over 80 years of age is projected to triple by 2050, due to advances in quality of life, as well as continued progress in medicine and technology. With this context in mind, this article focuses on conducting a comprehensive study on conventional walkers, which represent a fundamental tool to improve the mobility of elderly people. The main objective of this study is to establish the foundations for the design of walkers, with the purpose of identifying the functional and end user requirements under the WalkIES project funded by IEEE’s Industrial Electronics Society (IES). In this initial phase, we seek to define the ergonomic design of a conventional walker, evaluating its advantages and disadvantages, in addition to establishing the standards that these devices must meet. Based on the results obtained, the specific characteristics of the WalkIES walker will be determined. Additionally, international manufacturing standards for walkers are examined and detailed, taking into account the specific features necessary to ensure the effectiveness and safety of these devices for elderly people. Special attention is paid to the individual characteristics of each component that makes up the walkers, with the aim of optimizing their functionality and comfort for the user. In summary, the study of walkers and the establishment of standards are essential to address the mobility needs of the elderly population and those who require assistance to walk, guaranteeing their safety, comfort, and quality of life, as well as to define the requirements of design and manufacturing for robotic walker WalkIES.

This article reviews selected research focused on digital real-time simulation for the intelligent condition monitoring of electrical machines. Traditional multi-physical digital simulations have been broadly used in industrial product manufacturing and processing, particularly in virtual testing under regular and harsh working conditions, aging, and life-cycle analysis. Recent progress in data communication and information technologies based on virtual reality, cloud computing, parallel processing, machine learning, large-scale/big data, and the Internet of Things has allowed the development of intelligent physical and mathematical models with the primary capability of real-time bilateral data transfer with a physical unit. This new trend leads to perform condition-based monitoring, fault diagnosis, prognostics and life-cycle analysis more efficiently. An overview of the recent progress and challenges in the above research topic is presented. The primary emphasis is on the real-time multi-physical modeling of electrical machines, which are broadly utilized in industrial plants.

This study introduces an analytical tool for predicting the lifespan of bearings, combining machine learning and higher-order spectral analysis. The purpose of this approach is the use of acceleration as the primary parameter, capturing the essence of the bearing's functionality and its diverse states through both horizontal and vertical components. Specifically, this research delves into frequency analysis methods, centering on the Bispectrum of the acceleration signal. This technique offers an insight into the complex spectral relationships among frequencies throughout the bearing's life, by exploring the phase relationships between frequency components and unveiling the nonlinear interactions within the signal. This methodology is particularly valuable for bearing lifespan prediction, as it uncovers intricate patterns that traditional analysis might overlook. The aim is to not only elevate the precision of lifespan predictions but also to deepen the understanding of the failure mechanisms at play, thereby enhancing predictive maintenance strategies and industrial efficiency.

The work presents a lower limb active exoskeleton for people with problems of stand up and walk. The project is developed based on the requirements for walking and stand up of the elderly people or people with reduced mobility. PLA materials printed by using a 3D printer and linear actuators and sensors fabricated the present prototype. The device can be used by a person no more than 40 kg. The actual prototype is controlled with a mobile App for stand up and walk. For the future we consider to be controlled by muscle sensors and use inertial sensors.

Electric motors condition monitoring courses, due to their eminently practical nature, have been seriously affected by the irruption of the COVID-19 pandemic. Classical laboratory sessions, face-to-face demonstrations and seminars and student visits to real industrial facilities have had to be replaced by other learning mechanisms adapted to the new context of virtual teaching. Instructors of these courses have been forced to devise, often in a very short time, suitable strategies to replace the aforementioned presential mechanisms, by substituting them by solutions adapted to the virtual learning that, on the one hand, guarantee the acquisition of the necessary competencies and skills, and, on the other hand, maintain the student interest and motivation. This paper explains the strategies adopted in the context of two courses related to electric motors condition monitoring that are taught at the authors’ University. The described strategies have not only shown satisfactory results to transmit the regulated contents but also open new perspectives to enhance the future teaching in those courses. The ideas presented here may be useful for other instructors teaching similar courses or other subjects related to the considered area.

This paper presents a cross-disciplinary view of industrial electronics for building a sustainable society. After explaining efforts and challenges from human factors, professional education, electronic systems on chip, technology ethics and society, and standards, we suggest a methodology for cross-disciplinary technology integrating the above-mentioned fields, in which the technical committees in Cluster 4, Industrial Electronics Society, play an active part.

In this work, the application of multifractal spectrum of higher order cumulants slice and bicoherence of stator current signals is proposed as a way to detect field winding faults in wound field synchronous motors. These signals are analyzed both under starting and under steady-state regimes. Likewise, a quantitative indicator based on the summation of the first three log cumulants of the scaling exponents obtained from the multifractal analysis is proposed. In addition, a comparative study is carried out during starting and at steady-state, obtaining satisfactory results that prove the potential of the proposed methodology for its implementation in real applications.

This paper describes the development of a prosthetic robotic hand based on the human hand anatomy. The phalanges used are real human scanned phalanges. The carpal bones and the arm structure are modelled by using Autodesk Inventor Professional 2019 software. The control system is developed under Arduino Mega 2560. The input signals are based on MyWare sensors and press sensors. The prosthetic hand is dotted with 5 step motors one for each finger that control the flexion/extension and one servo that control the addiction/abduction of the thumb. Actual prosthetic hand is dotted with 16 DOF. The test results demonstrate an accurate finger movements.

Once a new product has been designed, or when creating a new model, most of the times, variations are needed (different configurations) that allow us to study its behavior, feasibility, etc. In the way designers traditionally design, models and products are very rigid in the sense that, albeit certain modifications may be applied, they are not flexible enough to obtain the desired new product designs or configurations. This work presents a modeling strategy, which has been implemented by mean of rules using Autodesk Inventor's iLogic tool. This strategy considers the study of the parameters prior to modeling and the implementation of certain rules, which based on a few parameters, allow the complete generation of the new model or product automatically. To illustrate this strategy, a basic explosion engine with a V-cylinder arrangement has been used as an example of a product. With the modification of a minimum number of parameters through a user interface, the new product automatically varies in number of cylinders, their capacity, the stroke ratio and the V angle, what will allow their study through dynamic simulation and FEM of the new desired motor without the need to carry out modifications to the models or to create a new assembly of the product. This strategy has been implemented in the Graphic Engineering department and used for the training of students of the Degree in Engineering in Industrial Technologies.

The analysis of the external magnetic field has been proven to be an effective way to diagnose different types of faults in induction motors. Classical methods based on permanent regime are being complemented by recent approaches that rely on the analysis of the magnetic field during transient operation of the machine. Most of these approaches have been applied with success to detect failures in cage induction motors, but few works have dealt with their wound rotor counterparts. This paper proposes the analysis of the magnetic field in the vicinity of the machine to detect rotor failures in wound rotor induction motors. Classical methods based on the Fast Fourier transform of steady-state signals are compared with recent methods relying on the analysis of signals captured under starting. Different positions for the considered flux sensors are considered and novel indicators are presented for the determination of the level of fault severity.

The present paper describes the learning experience of students during the development of multidisciplinary projects that involve electric and electronic engineering. The methodology is based on student interaction on the decision-making level, design and development, as well as on team management. Five students from European countries and two students from USA participated in this experience. All students are in the 4th year of their degrees in their respectively universities. The specific areas involved in the project are product design, biomedical engineering, mechanics and electronics. The correct distribution of the tasks made the collaboration between students and professor very efficient. The results demonstrate the positive experience of the educational experiment.

Predictive maintenance of electric motors is a hot topic nowadays. Due to the extensive participation of these machines in industry, there is an increasing demand of graduate engineering students with skills in this area. In spite of this fact, in many Universities, engineering programs did not include specific courses in this specific area. In recent years, there has been a certain increase in the offer of courses devoted to electric motors condition monitoring. Due to the constant research dynamism of this area, these courses should be prepared to incorporate the latest technological advances in order to instruct the students on the use of the most recent technologies. This paper presents several laboratory sessions that have been built in the context of a course dealing with the maintenance of electric motors. The sessions are designed to facilitate the student participation, promoting the collaborative work and facilitating the use of information and communication technologies (ICTs). At the same time, they are conceived to instruct the students with the most recent techniques for electric motors condition monitoring.

This paper describes the advantages of the project-based learning and the skills improvement on university students. During one semester, Erasmus students from Europe create project groups of 4-6 students with the objective of developing technological projects. During a semester, the students develop knowledge in the areas of management, electrics, electronics, product design, planning and programming. In this study, six students from Finland, France and Sweden worked on the Easy Access project. Their objective was to develop an intelligent door lock that relies on face recognition to open the front home door. During the project development, the students received classes of project planning from idea to testing and validation, as well as classes of modeling with Autodesk Inventor 2015 tool and programming with Arduino and artificial intelligence. They reached abilities of project planning, communication, writing reports, oral presentation, time managing, problem solving and critical thinking.

This work describes the development of a face detection and recognition application developed into Raspberry Pi and Android. The application connects with the Raspberry Pi by Bluetooth protocols. The object detection is based on boosted cascade while the face recognition is based on Eigenfaces. The developed system may be especially useful for visually impaired users since it can contribute to facilitate their autonomous behavior during their everyday life. The developed device shows great potential for extrapolation to other areas as education of visually impaired users.

The promotion of the students' transversal or generic competencies is one of the key points of the new European educational framework. Today, companies hiring graduate students are not only concerned about their technical background but also transversal skills and competencies. Hence, it becomes necessary to adopt measures to promote and cultivate these competencies among students. The students' involvement in innovative research projects brings an excellent opportunity to achieve this goal: competence dimensions as team work, effective oral and written communication, effective use of informational resources, independent learning, foreign language, problem resolution among many other can be effectively developed by involving students in multidisciplinary teams conceived to develop this type of projects. This paper focuses on this idea, remarking the usefulness of these projects to achieve the goals pursues by the new educational framework in the context of generic competencies. A real success story at the Universitat Politecnica de Valencia, based on a project relying on the development of an intelligent hardware system (SEE4ME) is presented. It confirms the ability of this approach to promote generic skills and competencies among students.

The paper presents a new wearable Cognitive Aid System for Blind People (CASBliP). The prototype device was developed as an obstacle detector, orientation and navigation Electronic Travel Aid (ETA) for blind people. The device provides a binaural acoustic image representation. The environmental information acquisition system is based on an array of 1×64 CMOS Time-of-Flight sensors. Through stereoscopic (binaural) acoustic sounds the device relays the surrounding near and far environment. Experimental results demonstrate that blind users are able to detect obstacles and navigate through unknown and known environments safety and confidently. CASBliP works accurately in range of 15m in distance and 64° in azimuth, providing significant advantages in comparison with currently existing ETA systems.

In this paper we propose a fuzzy method to detect free paths in real-time using digital stereo images. It is based on looking for linear variations of depth in disparity maps, which are obtained by processing a pair of rectified images from two stereo cameras. By applying least-squares fitting over groups of disparity maps columns to a linear model, free paths are detected by giving a certainty using a fuzzy rule. Experimental results on real outdoor images are also presented.