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Novelty detection is a statistical method that verifies new or unknown data, determines whether these data are inliers (within the norm) or outliers (outside the norm), and can be used, for example, in developing classification strategies in machine learning systems for industrial applications. To this end, two types of energy that have evolved over time are solar photovoltaic and wind power generation. Some organizations around the world have developed energy quality standards to avoid known electric disturbances; however, their detection is still a challenge. In this work, several techniques for novelty detection are implemented to detect different electric anomalies (disturbances), which are k-nearest neighbors, Gaussian mixture models, one-class support vector machines, self-organizing maps, stacked autoencoders, and isolation forests. These techniques are applied to signals from real power quality environments of renewable energy systems such as solar photovoltaic and wind power generation. The power disturbances that will be analyzed are considered in the standard IEEE-1159, such as sag, oscillatory transient, flicker, and a condition outside the standard attributed to meteorological conditions. The contribution of the work consists of the development of a methodology based on six techniques for novelty detection of power disturbances, under known and unknown conditions, over real signals in the power quality assessment. The merit of the methodology is a set of techniques that allow to obtain the best performance of each one under different conditions, which constitutes an important contribution to the renewable energy systems.

[EN] The monitoring of machine conditions is very important from the viewpoints of productivity, economic benefits, and maintenance. Several techniques have been proposed where the sensors are the key to provide relevant information to verify the system. Recently, the smart sensor con-cept is tendency where the sensors are integrated with a data processing unit executing dedicated algorithms for generating in situ meaningful information about the system. Also, the infrared thermography has gained relevance for monitoring processes, since the new infrared cameras have more resolution, smaller dimensions, reliability, functionality, and lower costs. These units were firstly used as secondary elements in the condition monitoring of machines, but thanks to modern techniques for data processing the infrared sensors can be used to give a first, or even a direct, diagnosis in a nonintrusive way for industrial applications. Therefore, in this manuscript the structure and development of an infrared thermography-based smart sensor for diagnosing faults in the elements associated to induction motors, like rolling bearings and the gearbox, is described. The smart sensor structure includes five main parts: infrared primary sensor, pre-processing module, image processing module, classification of faults, and user interface. The in-frared primary sensor considers a low-cost micro thermal camera for acquiring the thermal im-ages. The processing modules and the classification module implement the data processing algo-rithms into digital development boards enabling the characteristic of smart to the system. Final-ly, the interface module allows the final users require to the smart sensor to perform processing actions and data visualization, with the additional feature that the diagnosis report can be get-ting out from the system. The smart sensor is validated in a real experimental test bench demon-strating its capabilities in different case studies., This research was funded in part by the Spanish 'Ministerio de Ciencia Innovacion y Universidades' and FEDER program in the framework of the `Proyectos de Generacion de Conocimiento 20210 del Programa Estatal para Impulsar la Investigacion Cientifico-Tecnica y su Transferencia, Subprograma Estatal de Generacion de Conocimiento' (ref: PID2021-122343OB-I00). This project was partially supported by CONACyT scholarship 1184196

Online fault detection in industrial machinery, such as induction motors or their components (e.g., bearings), continues to be a priority. Most commercial equipment provides general measurements and not a diagnosis. On the other hand, commonly, research works that focus on fault detection are tested offline or over processors that do not comply with an online diagnosis. In this sense, the present work proposes a system based on a proprietary field programmable gate array (FPGA) platform with several developed intellectual property cores (IPcores) and tools. The FPGA platform together with a stray magnetic flux sensor are used for the online detection of faults in the outer race of bearings in induction motors. The integrated parts comprising the monitoring system are the stray magnetic flux triaxial sensor, several developed IPcores, an embedded processor for data processing, and a user interface where the diagnosis is visualized. The system performs the fault diagnosis through a statistical analysis as follows: First, a triaxial sensor measures the stray magnetic flux in the motor’s surroundings (this flux will vary as symptoms of the fault). Second, an embedded processor in an FPGA-based proprietary board drives the developed IPcores in calculating the statistical features. Third, a set of ranges is defined for the statistical features values, and it is used to indicate the condition of the bearing in the motor. Therefore, if the value of a statistical feature belongs to a specific range, the system will return a diagnosis of whether a fault is present and, if so, the severity of the damage in the outer race. The results demonstrate that the values of the root mean square (RMS) and kurtosis, extracted from the stray magnetic field from the motor, provide a reliable diagnostic of the analyzed bearing. The results are provided online and displayed for the user through interfaces developed on the FPGA platform, such as in a liquid crystal display or through serial communication by a Bluetooth module. The platform is based on an FPGA XC6SLX45 Spartan 6 of Xilinx, and the architecture of the modules used are described through hardware description language. This system aims to be an online tool that can help users of induction motors in maintenance tasks and for the early detection of faults related to bearings.

The electric machines are the elements most used at an industry level, and they represent the major power consumption of the productive processes. Particularly speaking, among all electric machines, the motors and their drives play a key role since they literally allow the motion interchange in the industrial processes; it could be said that they are the medullar column for moving the rest of the mechanical parts. Hence, their proper operation must be guaranteed in order to raise, as much as possible, their efficiency, and, as consequence, bring out the economic benefits. This review presents a general overview of the reported works that address the efficiency topic in motors and drives and in the power quality of the electric grid. This study speaks about the relationship existing between the motors and drives that induces electric disturbances into the grid, affecting its power quality, and also how these power disturbances present in the electrical network adversely affect, in turn, the motors and drives. In addition, the reported techniques that tackle the detection, classification, and mitigations of power quality disturbances are discussed. Additionally, several works are reviewed in order to present the panorama that show the evolution and advances in the techniques and tendencies in both senses: motors and drives affecting the power source quality and the power quality disturbances affecting the efficiency of motors and drives. A discussion of trends in techniques and future work about power quality analysis from the motors and drives efficiency viewpoint is provided. Finally, some prompts are made about alternative methods that could help in overcome the gaps until now detected in the reported approaches referring to the detection, classification and mitigation of power disturbances with views toward the improvement of the efficiency of motors and drives.

[EN] Wind generation is an essential power supply in the last time, as a renewable option. These wind generators are integrated with electrical machines that require correct functionality. However, the increasing use of non-linear loads introduces undesired disturbances that may compromise the integrity of the electrical machines inside the wind generator. Therefore, this work proposes a 5-step methodology for power quality disturbance detection in grids with injection of wind farm energy. First, a database with synthetic signals is generated to be used in the training process. Then, a multi-domain feature estimation is carried out. To reduce the problem dimensionality, the features that provide redundant information are eliminated through an optimized feature selec-tion performed by means of a genetic algorithm and the principal component analysis. Addi-tionally, each one of the characteristic feature matrices of every considered condition is modeled through a specific self-organizing map neuron grid so they can be shown in a 2-D representation. Since the SOM model provides a pattern of the behavior of every disturbance, they are used as inputs of the classifier based in a softmax layer neural network that performs the power quality disturbance detection of six different conditions: healthy or normal, sag or swell voltages, tran-sients, voltage fluctuations and harmonic distortion. Thus, the proposed method is validated using a set of synthetic signals and then it is tested using two different sets of real signals from an IEEE workgroup and from a wind park located in Spain., This research was partially funded by FONDEC-UAQ 2020 FIN202011 project. It was also supported by the Spanish `Ministerio de Ciencia Innovacion y Universidades' and FEDER program in the framework of the `Proyectos de I+D de Generacion de Conocimiento del Programa Estatal de Generacion de Conocimiento y Fortalecimiento Cientifico y Tecnologico del Sistema de I+D+i, Subprograma Estatal de Generacion de Conocimiento' (ref: PGC2018-095747-B-I00).

The incorporation of new technologies as training methods, such as virtual reality (VR), facilitates instruction when compared to traditional approaches, which have shown strong limitations in their ability to engage young students who have grown up in the smartphone culture of continuous entertainment. Moreover, not all educational centers or organizations are able to incorporate specialized labs or equipment for training and instruction. Using VR applications, it is possible to reproduce training programs with a high rate of similarity to real programs, filling the gap in traditional training. In addition, it reduces unnecessary investment and prevents economic losses, avoiding unnecessary damage to laboratory equipment. The contribution of this work focuses on the development of a VR-based teaching and training application for the condition-based maintenance of induction motors. The novelty of this research relies mainly on the use of natural interactions with the VR environment and the design’s optimization of the VR application in terms of the proposed teaching topics. The application is comprised of two training modules. The first module is focused on the main components of induction motors, the assembly of workbenches and familiarization with induction motor components. The second module employs motor current signature analysis (MCSA) to detect induction motor failures, such as broken rotor bars, misalignments, unbalances, and gradual wear on gear case teeth. Finally, the usability of this VR tool has been validated with both graduate and undergraduate students, assuring the suitability of this tool for: (1) learning basic knowledge and (2) training in practical skills related to the condition-based maintenance of induction motors., This research has been partially supported by Banco Santander under the scholarship program Santander Iberoamérica Research 2019/20. This investigation was partially supported by the ACIS project (Reference Number INVESTUN/21/BU/0002) of the Consejeria de Empleo of the Junta de Castilla y León (Spain).

[EN] The computer numerical control (CNC) machine has recently taken a fundamental role in the manufacturing industry, which is primordial for the economic development of many countries. Current high quality production standards, along with the requirement for maximum economic benefits demand the use of tool condition monitoring (TCM) systems able to monitor and diagnose cutting tool wearing. Current TCM methodologies mainly rely on vibration signals, cutting force signals, acoustic emission (AE) signals, which have the common drawback of requiring the in-stallation of sensors near the working area, a factor that limits their application under practical terms. Moreover, as machining processes require the optimal tuning of cutting parameters, novel methodologies must be able to perform the diagnosis under a variation of cutting parameters. This paper proposes a novel non-invasive method capable to automatically diagnose cutting tool wearing in CNC machines under the variation of cutting speed, and feed rate cutting parameters. The proposal relies on the sensor information fusion of spindle-motor stray flux and current signals by means of statistical and non-statistical time-domain parameters, which are then reduced by means of a linear discriminant analysis (LDA), and then a feed-forward neural network is used to automatically classify the level of wear on the cutting tool. The proposal is validated with a Fanuc Oi mate Computer Numeric Control (CNC) turning machine for three different cutting tool wearing levels and different cutting speed, and feed rate values., This work was supported by the Spanish 'Ministerio de Ciencia Innovacion y Universidades' and FEDER program in the framework of the 'Proyectos de I + D de Generacion de Conocimiento del Programa Estatal de Generacion de Conocimiento y Fortalecimiento Cientifico y Tecnologico del Sistema de I + D + i, Subprograma Estatal de Generacion de Conocimiento' (ref: PGC2018-095747-B-I00).

Despite the use of brush dc motors in industry has been decremental during recent decades, they are still employed in many industrial sites, even in high output power applications. However, the limited number of research works addressed to them yields a lack of predictive maintenance techniques to diagnose relevant faults in these machines, which may have catastrophic repercussions. Sparking in the commutator/brushes system is a symptom of incorrect operation linked to different possible failures (brush wear, deficient contacts, commutator defects...) and its detection can be crucial to prevent a further development of these anomalies. This work proposes a method for sparking assessment in dc motors based on the computation of the energy of specific signals resulting from the DWT analysis of the armature current. The experimental results confirm that the increment of the sparking activity yields low frequency components that provoke an increment in the energies of high order wavelet signals. Based on this fact, a new indicator of the sparking activity is proposed that shows a high effectivity according to the obtained results. The developed indicator can be a valuable tool for field engineers, satisfying their need of an online, simple and reliable method to assess the condition of these critical parts of dc motors.

[EN] Electric motors use about 68% of total generated electricity. Fault diagnosis of electrical motors is an important task, because it allows saving a large amount of money and time. An analysis of acoustic signals is a promising tool to improve the accuracy of fault diagnosis. It is essential to analyze acoustic signals to assess the state of the motor. In this paper, three electric impact drills (EID) were analyzed using acoustic signals: healthy EID, EID with damaged rear bearing, EID with damaged front bearing. Three angle grinders (AG) were analyzed: healthy AG, AG with 1 blocked air inlet, AG with 2 blocked air inlets. The authors proposed a method for feature extraction: SMOFS-NFC (Shortened Method of Frequencies Selection Nearest Frequency Components). Acoustic features vectors were classified by the nearest neighbor classifier and Naive Bayes classifier. The classification accuracy were in the range of 89.33¿97.33% for three electric impact drills. The classification accuracy were in the range of 90.66¿100% for three angle grinders. The presented method is very useful for diagnosis of bearings, ventilation faults and other mechanical faults of power tools. It can be also useful for diagnosis of similar power tools., This work was supported in part by Generalitat Valenciana, Conselleria de Innovacion, Universidades, ' Ciencia y Sociedad Digital, (project AICO/019/224).

Renewable energy-based power generation technologies are becoming more and more popular since they represent alternative solutions to the recent economic and environmental problems that modern society is facing. In this sense, the most widely spread applications for renewable energy generation are the solar photovoltaic and wind generation. Once installed, typically outside, the wind generators and photovoltaic panels suffer the environmental effects due to the weather conditions in the geographical location where they are placed. This situation, along with the normal operation of the systems, cause failures in their components, and on some occasions such problems could be difficult to identify and hence to fix. Thus, there are generated energy production stops bringing as consequence economical losses for investors. Therefore, it is important to develop strategies, schemes, and techniques that allow to perform a proper identification of faults in systems that introduce renewable generation, keeping energy production. In this work, an analysis of the most common faults that appear in wind and photovoltaic generation systems is presented. Moreover, the main techniques and strategies developed for the identification of such faults are discussed in order to address the advantages, drawbacks, and trends in the field of detection and classification of specific and combined faults. Due to the role played by wind and photovoltaic generation, this work aims to serve as a guide to properly select a monitoring strategy for a more reliable and efficient power grid. Additionally, this work will propose some prospective with views toward the existing areas of opportunity, e.g., system improvements, lacks in the fault detection, and tendency techniques that could be useful in solving them.

[EN] The desire for improving student motivation and interest in engineering learning have yielded new methodologies for active learning. The present paper presents a new methodology based on project learning and portable learning technology. The goal of the method was to improve the students’ confidence and learning in industrial design and prototyping during one semester. The results indicate that students’ motivation for learning was very high and it has been maintained during the whole semester. Also, the confidence on their learning and retention ability increased, which indicates that the method had a positive impact on the students.

Induction motors (IMs) have been extensively used for driving a wide variety of processes in several industries. Their excellent performance, capabilities and robustness explain their extensive use in several industrial applications. However, despite their robustness, IMs are susceptible to failure, with broken rotor bars (BRB) being one of the potential faults. These types of faults usually occur due to the high current amplitude flowing in the bars during the starting transient. Currently, soft-starters have been used in order to reduce the negative effects and stresses developed during the starting. However, the addition of these devices makes the fault diagnosis a complex and sometimes erratic task, since the typical fault-related patterns evolutions are usually irregular, depending on particular aspects that may change according to the technology implemented by the soft-starter. This paper proposes a novel methodology for the automatic detection of BRB in IMs under the influence of soft-starters. The proposal relies on the combined analysis of current and stray flux signals by means of suitable indicators proposed here, and their fusion through a linear discriminant analysis (LDA). Finally, the LDA output is used to train a feed-forward neural network (FFNN) to automatically detect the severity of the failure, namely: a healthy motor, one broken rotor bar, and two broken rotor bars. The proposal is validated under a testbench consisting of a kinematic chain driven by a 1.1 kW IM and using four different models of soft-starters. The obtained results demonstrate the capabilities of the proposal, obtaining a correct classification rate (94.4% for the worst case).

Recent advancements in mechanical engineering are an essential topic for discussion [...]

The analysis of electrical circuits is an essential task in the evaluation of electrical systems [...]

International audience; This paper presents the use of a novel method dealing with mathematical morphology to de-noise spectral contents. This approach is based on two fundamental operators which are dilation and erosion. This type of filter does not need a huge computing time because the operations involved are simple. The goal of this filter in the application considered in this paper is to de-noise the spectrum of the stator current demanded by an induction motor in order to facilitate the self-extraction of occasional fault components. Furthermore, the induction motors are widely used under speed variation conditions, usually fed by an inverter. Experiments showed in this paper are convincing and suggest the possible extension of the method to a wide range of applications.

International audience; In electrical engineering, the utilization of infrared thermography has been traditionally confined to the periodic inspection of electric installations, distribution lines and power transformers, among other applications. Over recent years, this technique is being increasingly incorporated in electric motors predictive maintenance programs. This is due to the fact that it has been reported its suitability for detecting certain types of problems, such as deficient bearing lubrication, cooling system problems, stator winding asymmetries or problems in the transmission system, among others. This paper presents an infrared-based methodology for detecting bearing anomalies in induction motors. It relies on the use of isothermal representations that enable to visualize the temperature gradient and, hence, to facilitate the localization of the anomaly source. At the same time, it enables an approximated computation of the motor losses, as it is carried out in the paper. The method is applied to both laboratory motors and industrial motors operating in a petrochemical plant. The results prove the potential of the presented infrared-based methodology to reliably detect the considered anomalies.