Smart-Sensor for the Automatic Detection of Electromechanical Faults in Induction Motors Based on the Transient Stray Flux Analysis

This research was funded by ¿GENERALITAT VALENCIANA, Conselleria de Educación, Investigación, Cultura y Deporte, grant number AICO/2019/224¿Zamudio-Ramírez, I.; Osornio-Ríos, RA.; Antonino-Daviu, JA.; Quijano-Lopez, A. (2020). Smart-Sensor for the Automatic Detection of Electromechanical Faults in Induction Motors Based on the Transient Stray Flux Analysis. Sensors. 20(5):1-19. https://doi.org/10.3390/s2005147711920

DEVELOPMENT OF A WIRELESS SIGNAL ACQUISITION SYSTEM FROM SENSORS FOR COMFORT AND ENERGY QUALITY

AbstractThe acquisition of wireless signals from sensors represents a variety of advantages over cable communication systems. This work presents a ZigBee-based signal acquisition system that takes advantage of its features to make a flexible system that can be used in different fields without the necessary use of a PC since a touchscreen and a microcontroller is used. The system is implemented in a building to monitor all the physical variables that are referred for the comfort of people, such as luminosity, temperature, humidity, gas concentration, smoke, human presence, glass breakage among others. The measure of these variables also could contribute to define or activate some extra-functions of the system, for example, alarms in case of fire presence. The system stores information of all sensors of all the network created in a Micro SD and uses it to make plots, also it is possible to visualize real-time readings.Keywords: Touchscreen, wireless sensor network (WSN), ZigBee.DESARROLLO DE UN SISTEMA DE ADQUISICIÓN DE SEÑALES INALÁMBRICAS A PARTIR DE SENSORES PARA COMODIDAD Y CALIDAD ENERGÉTICAResumenLa adquisición de señales inalámbricas de sensores representa una variedad de ventajas sobre los sistemas de comunicación por cable. Este trabajo presenta un sistema de adquisición de señales basado en antenas ZigBee que aprovecha sus características para hacer un sistema flexible que puede ser utilizado en diferentes campos sin el uso necesario de una PC ya que se utiliza una pantalla táctil y un microcontrolador. El sistema es implementado en un edificio para monitorear todas las variables físicas que se refieren a la comodidad de las personas, tales como luminosidad, temperatura, humedad, concentración de gas, humo, presencia humana, rotura de vidrios, entre otros. La medición de estas variables también es utilizada para activar algunas funciones extras del sistema, por ejemplo, alarmas en caso de presencia de fuego. El sistema almacena información de todos los sensores de toda la red creada en una Micro SD y crea gráficos históricos de dichas variables, además, es posible visualizar lecturas en tiempo real.Palabras claves: Pantalla táctil, red de sensores inalámbrica, ZigBee

Virtual Reality Training Application for the Condition-Based Maintenance of Induction Motors

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)

Methodology for Tool Wear Detection in CNC Machines Based on Fusion Flux Current of Motor and Image Workpieces

In the manufacturing industry, computer numerical control (CNC) machine tools are of great importance since the processes in which they are used allow the creation of elements used in multiple sectors. Likewise, the condition of the cutting tools used is paramount due to the effect they have on the process and the quality of the supplies produced. For decades, methodologies have been developed that employ various signals and sensors for wear detection, prediction and monitoring; however, this field is constantly evolving, with new technologies and methods that have allowed the development of non-invasive, efficient and robust systems. This paper proposes the use of magnetic stray flux and motor current signals from a CNC lathe and the analysis of images of machined parts for wear detection using online and offline information under the variation in cutting speed and tool feed rate. The information obtained is processed through statistical and non-statistical indicators and dimensionally reduced by linear discriminant analysis (LDA) and a feed-forward neural network (FFNN) for wear classification. The results obtained show a good performance in wear detection using the individual signals, achieving efficiencies of 77.5%, 73% and 89.78% for the analysis of images, current and stray flux signals, respectively, under the variation in cutting speed, and 76.34%, 73% and 63.12% for the analysis of images, current and stray flux signals, respectively, under the variation of feed rate. Significant improvements were observed when the signals are fused, increasing the efficiency up to 95% for the cutting speed variations and 82.84% for the feed rate variations, achieving a system that allows detecting the wear present in the tools according to the needs of the process (online/offline) under different machining parameters

System for Tool-Wear Condition Monitoring in CNC Machines under Variations of Cutting Parameter Based on Fusion Stray Flux-Current Processing

The computer numerical control (CNC) machine has recently taken a fundamental role in the manufacturing industry, which is essential 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 wear. Current TCM methodologies mainly rely on vibration signals, cutting force signals, and acoustic emission (AE) signals, which have the common drawback of requiring the installation of sensors near the working area, a factor that limits their application in practical terms. Moreover, as machining processes require the optimal tuning of cutting parameters, novel methodologies must be able to perform the diagnosis under a variety of cutting parameters. This paper proposes a novel non-invasive method capable of automatically diagnosing cutting tool wear 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); a feed-forward neural network is then 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 wear levels and different cutting speed and feed rate values