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Bringing Bearing Fault Detection to Embedded Systems Using Low-Cost Machine Learning Techniques

Bringing Bearing Fault Detection to Embedded Systems Using L...

引用

Latin America Congress on Computational Intelligence (LA-CCI)

作者： Natalia S. Teixeira M. A. Pastrana Henrique G. Moura Daniel M. Muñoz Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil Department of Mechanical Engineering Mechatronics Graduate Program University of Brasilia Brasilia DF Brazil Faculty of Gama Automotive Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

ISBN: (数字)9798350374575

ISBN: (纸本)9798350374582

Vibration analysis is crucial for predictive maintenance of bearing systems, aiming to reduce costs and prevent failures. This paper investigates signal processing techniques such as FFTs and Hilbert Transforms for fault diagnosis, alongside decision tree models, particularly the J48 algorithm within the WEKA environment, for fault classification. The study emphasizes the integration of signal processing and machine learning for effective fault diagnosis and maintenance. Notably, a classification model with low computational cost has been chosen, facilitating implementation in embedded systems. This model also offers interpretability of outputs, as it is based on decision trees of frequency components obtained through preprocessing.

关键词： Vibrations Accuracy Computational modeling Transforms Machine learning Signal processing Robustness Mathematical models Field programmable gate arrays Predictive maintenance

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Hardware Implementation of a GMDH Controller for Mobile Robot Obstacle Following/Avoidance

Hardware Implementation of a GMDH Controller for Mobile Robo...

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IEEE Latin American Robotics Symposium, LARS

作者： M. A. Pastrana L. H. Oliveira M. S. Santana V. C. Oliveira Jose Mendoza-Peñaloza Daniel M. Muñoz Department of Mechanical Engineering Mechatronics Graduate Program Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

Proportional, integral, and derivative (PID) controllers have been widely adopted for industrial applications. However, these controllers are not very efficient for non-linear systems. Artificial neural networks (ANN) based on the Group Method of Data Handling (GMDH) have great potential to replace the PID controllers due to their polynomial structure, allowing complex non-linear systems to be controlled. This work presents a hardware architecture of a GMDH network applied to speed control of a mobile robot platform. The proposed GMDH controller was implemented using a 16-bit floating-point arithmetic representation and was mapped on a Zynq7020 device. A hardware-in-the-loop based on the Universal Direct Memory Access methodology was developed to validate the proposed circuits, allowing for performance comparisons between a classical PID and the GMDH controllers for different simulated scenarios.

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Comparison of Bioinspired MLP Controllers for Mobile Robots Obstacle Following/Avoidance

Comparison of Bioinspired MLP Controllers for Mobile Robots ...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： Matheus de S. Luiz M. A. Pastrana Lukas. A. Da Silva Pamela. R. A. N. Campagnucci Jose Mendoza-Peñaloza Rafael R.L. Benevides Daniel M. Muñoz Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil Department of Mechanical Engineering Mechatronics Graduate Program University of Brasilia Brasilia DF Brazil

ISBN: (数字)9798350374575

ISBN: (纸本)9798350374582

Proportional, integral, and derivative (PID) controllers have been widely adopted for industrial applications. However, these controllers are not very efficient for non-linear systems. Artificial neural networks (ANN) based on the Multilayer Perceptron (MLP) have great potential to replace PID controllers due to their polynomial structure, allowing complex non-linear systems to be controlled. This article introduces the integration of four MLPs as alternatives to a traditional PID controller. These MLPs were trained through four bioinspired algorithms tailored for following tasks in mobile robots. The bioinspired algorithms employed for MLP network training include Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Month-Flame Optimization (MFO), and Artificial Hummingbird Algorithm (AHA). A comparative analysis was conducted between these MLPs and a classic PID controller, focusing on parameters such as overshoot (OS), settling time (ST), and steady-state error for different simulated scenarios.

关键词： Training Process control Artificial neural networks Vectors Stability analysis Steady-state Mobile robots Optimization Videos Testing

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Improving Control Theory Education Through Face-to-Face Implementation of a Mobile Robotics Methodology

Improving Control Theory Education Through Face-to-Face Impl...

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2024 Brazilian Symposium on Robotics (SBR), and 2024 Workshop on Robotics in Education (WRE)

作者： M. A. Pastrana William Humberto Cuéllar Sánchez Jose Mendoza-Peñaloza M. S. Santana Daniel M. Muñoz Department of Mechanical Engineering Mechatronics Graduate Program University of Federal District UDF Brasilia DF Brazil Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

ISBN: (数字)9798350391084

ISBN: (纸本)9798350391091

Advances in information and communication technologies have significantly transformed engineering education. Virtual laboratories are increasingly adopted to enhance student interaction with control system simulations. The improved visualization and interaction capabilities of modern computers offer a more organic way to teach theoretical foundations. This paper describes the implementation of a methodology for teaching control theory that until now was entirely online in a face-to-face environment at the University of the Federal District in Brasilia, Brazil. This methodology utilizes virtual laboratories with CoppeliaSim, MATLAB, and the EVA mobile robot to teach control theory focused on single-input, single-output (SISO) systems for mobile robot tracking and obstacle avoidance applications. The results of the face-to-face implementation are compared to those of the fully online methodology, revealing that in-person teaching significantly enhances the quality of instruction.

关键词： Visualization mechatronics Educational robots SISO Education Laboratories Software Control theory Mobile robots MATLAB

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Teaching Control Theory using Mobile Robot Obstacle Following/Avoidance with CoppeliaSim and MFO Algorithm.

Teaching Control Theory using Mobile Robot Obstacle Followin...

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IEEE Latin American Robotics Symposium, LARS

作者： M. A. Pastrana J. Bautista Jose Mendoza-Peñaloza L. H. Oliveira D. A. Mendes Daniel M. Muñoz Department of Mechanical Engineering Mechatronics Graduate Program University of Brasilia Brasilia DF Brazil Faculty of Engineering Systems Undergraduate Program Florencia University of the Amazonia Caquetá Colombia Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

The advancements in information and communication technologies have had a significant impact on the engineering educational system. Virtual laboratories are progressively being adopted to improve the way in which students interact with simulations for control systems. The enhancement of visualization and interaction offered by modern computers presents an opportunity to teach the theoretical foundation with a more organic approach. In addition, there are optimization algorithms that can be employed to designing controllers in an optimal way without having extensive knowledge in the area of control theory. This paper delineates the utilization of CoppeliaSim software, the Moth Flame Optimization (MFO) algorithm, and the EVA mobile robot for teaching control theory with Single-Input, Single-Output systems (SISO), for mobile robot obstacle following/avoidance application. The approach employs an online multi-language (Spanish and Portuguese) methodology for students without knowledge of control theory.

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Hardware Implementation of a Sliding Detection Algorithm for Robotic Hands Using Force Sensors

Hardware Implementation of a Sliding Detection Algorithm for...

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Symposium on Integrated Circuits and Systems Design (SBCCI)

作者： Jose Mendoza-Peñaloza Daniel M. Muñoz Department of Mechanical Engineering Mechatronics Systems Graduate Program Automation and Control Group/GRACO Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

In this work, a sliding detection algorithm was proposed for a previously developed robotic hand through the utilization of force sensors. Previous works have designed several hardware architectures to filter sensor data, implement dynamic control, and machine learning models for controlling the robotic fingers using a single Field programmable Gate Arrays (FPGA) chip. In this regard, the slip detection algorithm was developed to comprise three stages, each with a low computational cost, including a moving average filter, a first-order derivative, and a peak detection algorithm. A reference model was constructed and validated through an experimental protocol employing daily use objects to create a database. Subsequently, the slip detection algorithm was mapped onto hardware utilizing previously developed floating-point arithmetic IP cores and implemented using a Zynq 7020 device. The FPGA implementation was characterized in terms of resource occupation, power consumption, execution time, and numerical error with the reference model.

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Fuzzy Logic for Assessing the Health of a Corn Crop within Family Farming

Fuzzy Logic for Assessing the Health of a Corn Crop within F...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： Vanessa Mendonça Mikael Santos Daniel M. Muñoz Carlos Humberto Llanos Department of Computer Science Federal Institute of Brasilia Department of Mechanical Engineering Mechatronics Graduate Program Department of Agronomy Federal Institute of Brasilia Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

ISBN: (数字)9798350374575

ISBN: (纸本)9798350374582

Technological advancements are increasingly evident across various sectors, including automobiles, industry, and healthcare. In precision agriculture, significant progress has been made, with AgroTICs and Smart Agriculture gaining substantial traction in the market. However, a gap remains between cutting-edge technology and family farming, presenting a challenge from both social and applied research perspectives. However, there is still a gap between cutting-edge technology and family farming, which creates a challenge from a social and applied research point of view. In this context, this paper proposes a monitoring model based on Fuzzy Logic and sensor automation applied to estimate the health of a corn crop. The proposed Fuzzy inference system involves calculating an indicator of nutrients as well as the average color and area of corn plants. The nutrient indicator is automatically computed by an ESP32 microcontroller using sensor readings, while the average color and area inputs are manually entered via a mobile application. Additionally, the Fuzzy inference is integrated into the ESP32. The model underwent experimental validation on the health of the plantation, and the results were evaluated in four areas: one was designated for testing, and three were for validation. The model achieved an accuracy of 97.5% in Scenario 3, categorized as ’Very Favorable’, and an accuracy of 65% in Scenarios 2 and 4, categorized as ‘Unfavorable’. The implications of this research contribute to the advancement of AgroTICs among small producers, with the potential to enhance and automate the monitoring of their harvest production.

关键词： Fuzzy logic Accuracy Computational modeling Plantations Crops Production Color Nutrients Monitoring Fuzzy systems

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Data-Driven Control and Behavior-Based Control Applied to a SISO Mobile Robot

Data-Driven Control and Behavior-Based Control Applied to a ...

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IEEE Latin American Robotics Symposium, LARS

作者： M. A. Pastrana Karine R. Santos Alceu B. C. de Farias Daniel M. Muñoz Department of Mechanical Engineering Mechatronics Graduate Program Automation and Control Group/GRACO University of Brasilia Brasilia DF Brazil Faculty of Gama Biomedical Engineering Graduate Program University of Brasilia Brasilia DF Brazil Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

ISBN: (数字)9781665462808

ISBN: (纸本)9781665462815

With the advancement of technology, systems in many different areas of application have become more complex, and obtaining reliable mathematical models is a task that is increasingly becoming more difficult as well. To overcome these issues, new methodologies have emerged, such as the one known as Data-Driven-Control (DDC), which uses the input and output data of the system to design a controller, and Behavior-Based-Control (BBC), which provides the controller based on the desired behavior of a system. In this work, these two methodologies were applied to obtain the transfer function of a Single-Input Single-Output mobile robotic platform that moves parallel to a wall. A Proportional Integral Derivative controller was used to control the left wheel, while the right wheel was set to a constant speed, allowing the mobile robot to maintain a constant distance from the wall. The controller was tuned utilizing root locus methodology with an overshot of 16.7%, settling time of 6.45 seconds, and a static error of 2.1%. Additionally, disturbances were added to test the robustness of the mobile robot model and the PID controller.

关键词： Analytical models Biological system modeling Wheels Transfer functions Robot sensing systems Control systems Mathematical models

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Structural Analysis of Thumb Mechanism Exoskeleton Design for Post-Stroke Rehabilitation

Structural Analysis of Thumb Mechanism Exoskeleton Design fo...

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International Conference on Advanced mechatronics, Intelligent Manufacture, and Industrial Automation (ICAMIMIA)

作者： Rika Dwi Hidayatul Qoryah Harus Laksana Guntur Hendro Nurhadi Handri Prastyo Franciscus Xaverius Kristianta Shendy Chandika Graduate Program of Mechanical Engineering Department Institut Teknologi Sepuluh Nopember Surabaya Indonesia Mechanical Engineering Department Institut Teknologi Sepuluh Nopember Surabaya Indonesia Mechanical Engineering Department Center of Excellence for Mechatronics and Industrial Automation Research Center (PUI-PT MIA-RC) Institut Teknologi Sepuluh Nopember Surabaya Indonesia Undergraduate Program Mechanical Engineering Department University of Jember Jember Indonesia Mechanical Engineering Department University of Jember Jember Indonesia

Stroke is the second cause of death and the third cause of disability in the world. Stroke is a disease caused by a clot in the artery that supplies blood to the brain (ischemia). Rehabilitation is a solution to restore the function of parts of the body that have been disturbed by a stroke. Therefore, exoskeleton robots for stroke therapy are one solution to provide good rehabilitation. The exoskeleton robot supports healthy human hand movements and considers the safety and comfort factors during rehabilitation. The exoskeleton in this research uses a cable and sheath transmission mechanism with a locally situated motor as a design mechanism that guarantees safety and comfort. The design strength can be determined using ANSYS static structural assistance software so that the maximum stress in links 1, 2, and 3 is known within safe limits with anthropometric loading. Apart from that, the static structural simulation results show that link 1 can withstand a maximum load of 20 N or more in the X-axis loading, a load of 15 N in the Y-axis loading, and a load of 15 N in the Y-axis loading, and a load of 10 N in the Z-axis loading. Link 2 can withstand a maximum load of 10 N in the X-axis load and 20 N or more in the Y-axis load. Meanwhile, link 3 can withstand a maximum load of 20 N in the X-axis load and below 10 N in the Y-axis load.

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A modified gravitational search algorithm for continuous optimization 14

A modified gravitational search algorithm for continuous opt...

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16th Genetic and Evolutionary Computation Conference Companion, GECCO 2014 Companion

作者： De, Emerson Hochsteiner Segundo, Vasconcelos Neto, Gabriel Fiori Da Silva, Andre Mendes Mariani, Viviana Cocco Dos Santos Coelho, Leandro Mechanical Eng. Grad. Program Pontifical Catholic University of Parana Curitiba PR Brazil Curitiba PR Brazil Mechatronics Engineering Undergraduate Program Pontifical Catholic University of Parana Curitiba PR Brazil Mechanical Engineering Graduate Program Pontifical Catholic University of Parana and Electrical Engineering Federal University of Parana Curitiba PR Brazil Industrial and Systems Eng. Graduate Program Pontifical Catholic University of Parana and Electrical Engineering Federal University of Parana Curitiba PR Brazil

ISBN: (纸本)9781450328814

The gravitational search algorithm (GSA) is a stochastic population-based metaheuristic inspired by the interaction of masses via Newtonian gravity law. In this paper, we propose a modified GSA (MGSA) based on logarithm and Gaussian signals for enhancing the performance of standard GSA. To evaluate the performance of the proposed MGSA, well-known benchmark functions in the literature are optimized using the proposed MGSA, and provides comparisons with the standard GSA.

关键词： Optimization

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