Francisco González

Mechanical clearances are present in mechanical systems due to manufacturing tolerances, assembly imperfections, wear, and material deformation. Their presence can significantly influence dynamic behavior, vibration levels, and long-term component performance. As a result, the characterization, monitoring, and estimation of clearances have become important research topics. Considerable research has been devoted to the dynamic analysis of mechanical systems with clearances using multibody formulations, while more recent studies have explored hybrid model-based estimation techniques and data-driven methods for clearance identification. The development of such approaches requires experimental platforms capable of systematically reproducing and investigating clearance-induced dynamic behaviors. This work presents the development of a configurable slider–crank test bench designed for experimental investigation of mechanical clearance, which allows the selection of clearance location and magnitude, operating speed, and mechanism orientation. A multibody model was developed to support the system design, monitor its operation, and provide a numerical reference for the obtained experimental data, retrieved from accelerometers and encoders. The results obtained during experimental tests demonstrate that the platform generates repeatable measurements while remaining sensitive to variations in operating conditions and clearance configurations. Results also served to assess the ability of multibody formulations and clearance modeling approaches to correctly characterize the dynamic effects caused by joint clearances. Formulations that describe the system motion using minimal coordinates resulted in reduced uncertainty in simulation results. The selected contact models succeeded at capturing the effect of revolute joint clearances on the accelerations of the mechanism, establishing a first step in the detailed description of the contact dynamics that is necessary for the use of forward-dynamics simulation results in the development of hybrid model-based and data-driven clearance characterization methods.

Clearance is an important phenomenon in mechanisms that stems from manufacturing imperfections and wear and tear. Undetected clearance can compromise machine operations, negatively impacting its performance, and cause premature damage that requires maintenance actions. Monitoring clearance growth is useful to improve maintenance plans and helps reduce the number of unwanted interruptions during machine operation. In this work, a prediction method that targets the determination of the clearance size based on minimal, raw sensor data and machine learning is proposed. The data in this study are generated using multibody dynamics simulations based on planar mechanisms and used to train and compare several types of neural networks in terms of their ability to assess clearance size. Results show that clearance parameters can be reliably estimated with appropriate combinations of sensor locations and type of neural network. The developed method offers reliable clearance detection based on measurements that can be obtained from physical systems and used to monitor the state of their clearance defects.

El presente estudio permite establecer la interacción y el grado de participación de los padres de familia en las actividades escolares y académicas que inciden, de forma directa, en el desempeño escolar y el desarrollo integral de los educandos. El objetivo de la investigación es Analizar cómo se relaciona la familia y la escuela en el proceso de aprendizaje de los estudiantes de cuarto grado de la Unidad Educativa. La metodología se basó en un enfoque mixto cualitativo-cuantitativo con diseño no experimental y nivel descriptivo. Los instrumentos utilizados fueron la encuesta y la observación directa que ayudaron a recoger los datos mediante un análisis estadístico. Los resultados apuntan a una correlación positiva entre el grado de relación con la familia y la escuela, así como las tácticas para mejorar el desempeño académico. Se concluye que un alumno motivado, con hábitos establecidos, con apoyo por parte de sus padres y Educadores, tendrá un nivel educativo y un aprendizaje significativo

Electric powertrains are commonly used in the automotive industry nowadays. Thermal phenomena are critical for their performance, as the temperatures of some components must remain below certain thresholds during operation. This is the case of permanent-magnet synchronous motors (PMSMs): magnet temperatures cannot exceed a limit value to avoid demagnetization and motor failure. It is not possible, however, to directly monitor magnet temperatures by means of sensors. A feasible alternative to obtain this information consists of the use of virtual sensors, which fuse a numerical model of the motor with readings from a reduced set of physical sensors, for instance via the use of Kalman filters. The required models can be formulated using lumped-parameter thermal networks (LPTN), able to describe the thermal behavior of the motor in real time, at the cost of simplifying the representation of the thermal dynamics of the system. This simplification translates into parameter and geometry uncertainties, which can be alleviated by means of offline calibration. LPTNs are also sensitive to the heat sources in the system, and these, in turn, are highly dependent on the operation conditions. Although numerical models exist to describe heat source behavior, the accurate prediction of heat sources is challenging, particularly at the motor magnets. Improper modeling of thermal losses can result in unrealistic predictions of the motor temperatures. In this work, we present a method to estimate the internal temperatures of PMSMs using additional measurements of the motor power consumption. This information can be related to the thermal losses of the motor, helping Kalman filters to improve estimation accuracy. The proposed approach was tested with a benchmark example and in a standard Worldwide Harmonized Light Vehicles Test Procedure (WLTP) bench test with an automotive-grade PMSM. Results confirmed the ability of the method to correct the inaccuracies that stem from heat source uncertainties.

La aparición de holguras por desgaste en la maquinaria industrial tiene un impacto importante ensu rendimiento y vida útil. Una holgura reduce la eficiencia de la máquina y provoca incrementosde temperatura, ruidos y vibraciones, lo que puede dañar ciertos componentes o causar el falloprematuro del equipo. Para reducir estos efectos, es necesario efectuar tareas de mantenimiento deforma regular. El proceso de caracterización de las holguras y su simulación ha sido objeto de estudio de lacomunidad de dinámica multicuerpo. Los métodos propuestos requieren una descripción precisa dela dinámica de los sólidos unidos por las juntas con holgura. La representación de este fenómenoen la simulación depende de la selección de varios parámetros que caracterizan la geometría de laholgura. En la práctica, estos parámetros son complejos de obtener y pueden variar durante eltiempo debido, por ejemplo, a cambios en las condiciones de operación de la máquina. Además,muchos de estos parámetros no se pueden medir directamente mediante sensores. Los observadores de estados, como los basados en el filtro de Kalman, se emplean para estimarvariables o parámetros que no se conocen con certeza, lo que los convierte en una alternativa paradetectar la presencia de holguras en un mecanismo. Los filtros de Kalman se han empleado conmodelos multicuerpo para estimar fuerzas y parámetros de configuración de modelos, además delpropio estado del sistema. Sin embargo, aún no se han aplicado al ámbito de las holguras de formasistemática, especialmente en el contexto de montajes experimentales. Los primeros pasos haciauna solución siguiendo el enfoque de estimadores implica la selección de los métodos másadecuados para cuantificar el problema de la holgura de forma directa, incluyendo la selección desensores, tipo de filtro y la identificación de las magnitudes más relevantes del problema. En este trabajo, la máquina objeto de estudio se basa en un mecanismo de biela-manivela. Se hacombinado un modelo ideal del mecanismo con un filtro de Kalman extendido consiguiendo estimarlas fuerzas en la holgura. El siguiente paso es conseguir estimar el tamaño de la holgura, para loque se están estudiando diferentes alternativas, como la estimación de parámetros mediante unfiltro de Kalman “unscented”. De esta forma, será posible monitorizar la aparición y evolución deholguras con el fin de evitar daños críticos en la maquinaria.

Lumped-parameter thermal networks (LPTN) feature an excellent tradeoff between computational cost and accuracy, and are an effective option to model thermal phenomena in electric motors. The key to obtaining a high-fidelity LPTN model resides in the identification of its parameters. These, however, may experience modifications over time. Thermal ageing is one of the main causes of the gradual deterioration of the insulation system in electric motors, which leads to changes in the thermophysical properties of its dielectric materials. This effect considerably decreases the expected lifetime of the insulation. Hence, thermal ageing should be considered in order to increase the fidelity of the LPTN models. In this work, an experimental campaign is presented together with computational fluid dynamics (CFD) simulations to obtain a law of deterioration of the equivalent conductivity of the insulation system in a complete automotive-grade stator. The obtained rules are subsequently implemented in LPTNs. These can increase the fidelity of the representations used in digital twins (DTs) of motors. Indeed, the thermal characterization obtained in this work is to be implemented in an eMotor DT, where the virtual model is bidirectionally connected to a motor in a test bench used for model-based validation and model calibration.

Thermal phenomena hold vital significance in automotive applications involving permanent-magnet synchronous motors (PMSMs). The accumulation of heat in vulnerable regions, such as the magnets or end-windings, has the potential to impair device performance and inflict severe damage to components under extreme circumstances. Obtaining precise knowledge of the device temperatures is vital for implementing preemptive measures. However, conducting direct measurements to monitor thermal effects during production is often unfeasible. As an alternative, the development of real-time digital twins, capable of accurately representing the motor’s thermal effects using a limited set of sensor data, emerges as a viable solution. In this study, we present an approach for the development of these digital twins that relies on the utilization of a Lumped-Parameter Thermal Network (LPTN) representation. The LPTN delivers a prediction of the heat flows and temperatures within the PMSM. Then, data from sensors mounted on the device can then be used as input for a sensor data fusion algorithm, to obtain an accurate estimation of the temperatures at critical locations in the motor. With this approach, an accurate calibration of the LPTN parameters is required to ensure the reliability of the results. We present a calibration protocol based on individual tuning of thermal resistances and inertias in the LPTN through a gradient optimization method. This optimization is supported by a sensitivity analysis based on the analytical differentiation of the circuit dynamics. This approach has been proven to deliver fast and accurate results.