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Fault Diagnosis in Belts Using Signal Processing Techniques and Machine Learning 1st

Fault Diagnosis in Belts Using Signal Processing Techniques ...

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1st international conference on Intelligent Systems in computing and Communications, ISCComm 2023

作者： Kirankumar, M.V. Rajeshwari Nishmitha Kumari, Pavitra Raghavendra, B.K. Vyankatesh, Kulkarni Sourabh Karnataka Moodbidri574225 India Electrical and Electronics Engineering Govternment Polytechnic Karnataka Koppal583231 India KPIT Technologies Pune India

ISBN: (纸本)9783031756047

In the era of modern industries, use of advanced machines has increased the productivity of plants. Defect of such complex machines can increase the downtime and hence reduce productivity and ultimately the profit. Proper maintenance of systems is very crucial to ensure trouble free operation of machines. Predictive Maintenance techniques are those techniques which are used for identification of fault in its early stage. This avoids the sudden unavailability of machines and reduces the downtime. Predictive Maintenance techniques using vibration analysis is one of the very popular methods of Condition Monitoring. These techniques are evolving rapidly due to their efficient detection of fault in its incipient stage. However, non-stationary of the vibration signal produced particularly in belts in belt drive systems using conventional techniques of signal processing poses challenges. In this project work, the application of advanced signal processing techniques is investigated for the automatic fault detection in belts. This project work is focused on developing a novel concept of vibration signal processing for the automatic fault detection in belts using wavelet analysis and the belt condition classification and prediction using Machine Learning algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Condition Monitoring Fault Diagnosis Machine Learning

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Real-time classification of transmission line inspection images based on edge computing system

Real-time classification of transmission line inspection ima...

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electrical engineering and control science (IC2ECS), international conference on

作者： Xiaobing Guo Wu Yan Jiaying Wang Hongbo Sun Junsong Guo Zhongxian Qi State Grid Electric Power Space Technology Company Limited Beijing China College of Engineering China Agricultural University Beijing China

The management of vast and redundant inspection images is developing into an increasingly critical challenge due to the expansion of power line distance and the complexity of inspection requirements. The classification of power line inspection images is the basis for other subsequent tasks and image management. With the development of deep learning, fast and accurate image classification has become possible. For this problem, this paper proposes a real-time image classification scheme based on edge computing modules. In this study, the image acquisition and edge computing modules are designed and the lightweight network MobileNetV3 is improved to make it more suitable for this task. The experiments show that the Top-l accuracy reaches 90.86% when the input image resolution is 672×672. The improved model processes an image on Jetson nano with a speed of 0. 1176s. Most of the categories can be classified with an accuracy of more than 90% when tested on 39 categories. The proposed method in this paper can be used for real-time fast classification of inspection images.

关键词： Image resolution Power transmission lines Computational modeling Inspection Propagation losses Real-time systems Reliability

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Anomaly Detection for Aircraft Based on Multivariate LSTM and Predictive Residual Test

Anomaly Detection for Aircraft Based on Multivariate LSTM an...

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Automation, Robotics and Computer engineering (ICARCE), international conference on

作者： Chaoqi Zhang Wu Wei Gongcheng Zhou Gang Xiang Ruishi Lin Langfu Cui Dongpeng Li Yu Peng Guizhen Yu School of Transportation Science and Engineering Beihang University Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China Beijing Aerospace Automatic Control Institute Beijing China Beihang University Beijing China

With the increasing complexity and high reliability requirements of aircraft, researching more accurate anomaly detection methods and real-time monitoring of aircraft status is of great significance for ensuring equipment safety. This paper proposes a method based on multivariate LSTM and residual testing for anomaly detection in high-dimensional complex data. Firstly, by utilizing maximum mutual information to analyze the correlation of multidimensional data, a multivariate LSTM model is established based on high correlation parameters to predict the parameters. Then, combined with the idea of statistical hypothesis testing, test the statistical distribution of residuals on the training data of the prediction model using Kolmogorov-Smirnov method, and the dynamic threshold for anomaly detection is obtained through significance testing. Finally, real-time calculation of residuals between online monitoring data and predicted values is calculated, and anomaly detection is achieved by comparing the residual with threshold. The method proposed in this paper exhibits excellent anomaly detection performance on real flight data of aircraft.

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Improved control Method of Microgrid for the Better Dynamic Characteristics 10th

Improved Control Method of Microgrid for the Better Dynamic ...

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10th international conference on Frontier computing, FC 2020

作者： Wang, Xuejie Ji, Yanchao Wang, Jianze Wang, Yuanjun Qi, Lei School of Electrical Engineering & Automation Harbin Institute of Technology Harbin China School of Electric Power Shenyang Institute of Engineering Shenyang China Liaoning Electric Power Development Group Co. Ltd Shenyang China School of Electrical Engineering Shenyang University of Technology Shenyang China Liaoning Provincial Institute of Safety Science Shenyang China

ISBN: (纸本)9789811601149

In this paper, the droop control structure is divided into two parts, droop curve injection and inverter terminal voltage control, respectively. The power calculation, filtering, and arc curve equation are included in the vertical curve. The voltage control link achieves the frequency and amplitude of the given reference voltage. The output voltage of the inverter is controlled to make it equal to the reference voltage. The current double-closed-loop control structure of the electrical system and the decoupling principle are further analyzed. From the point of the closed-loop system, the control parameters of the current loop and the voltage loop of DQ0 are designed. In the simulation example, the response characteristics and trends of load disturbance elimination are verified. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Power control

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Construction of a Backstepping controller for controlling the Depth Motion of an automatic Underwater Vehicle

Construction of a Backstepping Controller for Controlling th...

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international conference on Research in Intelligent and computing in engineering, RICE 2020

作者： Vinh, Nguyen Quang Thanh, Nguyen Duc Faculty of Electrical-Electronic Engineering Duy Tan University 254 Nguyen Van Linh Da Nang Viet Nam Academy of Military Science and Technology 17 Hoang Sam Cau Giay Hanoi Viet Nam

ISBN: (纸本)9789811575266

In this paper, a backstepping controller is proposed to consider the depth motion of an automatic underwater vehicle (AUV) based on the Lyapunov global stability principle. The obtained results showed that the proposed algorithm guarantees the stability and follows the reference orbit shortly with a smaller error and also augments the application ability of controllers within more strictly required working conditions. The results of the survey and simulation are examined and show the strength and the rightness of the algorithm. © 2021, Springer Nature Singapore Pte Ltd.

关键词： controllers

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Adaptive perturbation estimator based dynamic control using PID sliding manifold for a quadcopter UAV 1

Adaptive perturbation estimator based dynamic control using ...

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1st IEEE international conference on Advances in electrical, computing, Communications and Sustainable Technologies, ICAECT 2021

作者： Thanh, Ha Le Nhu Ngoc Lee, Choong Hyun Hong, Sung Kyung Ho Chi Minh city Viet Nam Department of Aerospace Engineering Sejong University Seoul Korea Republic of

ISBN: (纸本)9781728157900

This article proposes an adaptive perturbation estimator (APE) founded on a dynamic sliding control (DSC) for a quadcopter unmanned aerial vehicle (UAV). The presented controller is used to significantly remove the negative influences of uncertainties/perturbations to enhance the stabilization control performance of a UAV. First, an APE is introduced to approximate the unknown perturbations. Second, a DSC is developed by applying the conventional Proportional Integral Derivative (PID) function integrated with the Super-Twisting sliding algorithm and the approximated values to reimburse for the bad influences of disturbances on the system and remove the chattering phenomenon. The overall stability of the control system is proven by the Lyapunov theorem. The advantages and effectiveness of the proposed controller is confirmed through a simulation executed and compared with the previous controllers. © 2021 IEEE

关键词： controllers

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Adaptive Prediction control for Parallel Robots Actuated by Pneumatic Artificial Muscles With Motion Constraints and Input Saturation

Adaptive Prediction Control for Parallel Robots Actuated by ...

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IEEE international conference on Real-time computing and Robotics (RCAR)

作者： Tong Yang Qihang Wang Shuzhen Diao Yongchun Fang Ning Sun College of Artificial Intelligence Institute of Robotics and Automatic Information Systems (IRAIS) Nankai University Tianjin China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen China

Benefitting from the inherent characteristics of high compliance, strong flexibility, and lightweight, pneumatic artificial muscles (PAMs), as a kind of soft actuators, are widely used in industrial logistics, medical rehabilitation, bionic engineering, etc. In addition to the basic issues in PAMs, e.g., unmodeled dynamics, input hysteresis, and unidirectional saturation constraints, parallel PAMs also suffer from more complex coupling relationships and antagonistic effects. Hence, it is necessary to ensure safety constraints for independent muscles and their input air pressure. To simultaneously deal with the above-mentioned problems, this paper proposes a dual-layer control structure for parallel PAM-actuated robot manipulators. The lower-layer controller handles uncertain dynamics and ensures accurate tracking of parallel PAMs, and the corresponding Lyapunov-based stability analysis also proves its effectiveness. Meanwhile, the lower-layer controller and parallel PAMs are approximated together by an optimal reference model, and necessary control parameters are flexibly adjusted during the optimization process, which make the upper-layer prediction controller not depend on any plant knowledge, to realize input/output constraints by multi-period motion prediction and horizon optimization. Moreover, experimental results in different cases are provided to illustrate the performance of the presented controller.

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A printed formula recognition method based on the "Encoder-Decoder" deep learning model

A printed formula recognition method based on the "Encoder-D...

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Computer engineering and Intelligent control (ICCEIC) international conference on

作者： Yilin Chen Zhan Shi Institute of Computer Engineering Nanjing Institute of Technology Nanjing China The College of Information Mechanical and Electrical Engineering Shanghai Normal University Shanghai China

With the popularization of computers and the rapid development of internet technology, electronic books and literature have become important information carriers. Therefore, digitization and informatization of scientific and technological books and literature containing a large number of mathematical formulas has become an important task. Although OCR (Optical Character Recognition) technology has high efficiency and accuracy in text recognition processing, it provides a convenient method for digitization and informatization of scientific and technological books, literature, etc. However, due to the complex two-dimensional structure of mathematical formulas and the diverse logical relationships and varying sizes between characters, OCR technology is not ideal for recognizing mathematical formulas. This paper proposes a deep learning model of "encoder-decoder" to solve the recognition problem of printed mathematical formulas. Utilize the powerful expression and sequence modeling capabilities of Vision Transformer and Transformer architecture to achieve accurate recognition of printed mathematical formulas. Through experimental verification, the model has achieved the expected recognition results on the publicly available printed mathematical formula datasets, with high recognition accuracy, fast running speed, and automatic generation of editable formats. This indicates that the model has potential in this field and provides new directions and choices for further improvement and application.

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Robust-to-Noise Algorithms for Distributed Resource Allocation and Scheduling

Robust-to-Noise Algorithms for Distributed Resource Allocati...

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RSI/ISM international conference on Robotics and Mechatronics (ICROM)

作者： Mohammadreza Doostmohammadian Alireza Aghasi Faculty of Mechanical Engineering Semnan University Iran Electrical Engineering and Computer Science Department Oregon State University USA

Efficient resource allocation and scheduling algorithms are essential for various distributed applications, ranging from wireless networks and cloud computing platforms to autonomous multi-agent systems and swarm robotic networks. However, real-world environments are often plagued by uncertainties and noise, leading to sub-optimal performance and increased vulnerability of traditional algorithms. This paper addresses the challenge of robust resource allocation and scheduling in the presence of noise and disturbances. The proposed study introduces a novel sign-based dynamics for developing robust-to-noise algorithms distributed over a multi-agent network that can adaptively handle external disturbances. Leveraging concepts from convex optimization theory, control theory, and network science the framework establishes a principled approach to design algorithms that can maintain key properties such as resource-demand balance and constraint feasibility. Meanwhile, notions of uniform-connectivity and versatile networking conditions are also addressed.

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DMGA: A Directional-Mutation Genetic Algorithm for CDMS Problem of Kafka

DMGA: A Directional-Mutation Genetic Algorithm for CDMS Prob...

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IEEE international conference on Networking, Sensing and control

作者： Hanbo Ma Yaofei Ma Haitao Yuan Meijia Wang Jingwei Hu Yihuan Wang Hanbo Yang School of Automation Science and Electrical Engineering Beihang University Beijing China Beijing Simulation Center National Key Laboratory of Modeling and Simulation for Complex Systems Beijing China

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

With the increasing role of distributed message systems (DMS) represented by Kafka in handling large-scale data streams, it is critical to improve system performance by optimizing configuration parameters in different production environments. Due to the target system possessing numerous configuration parameters and the complex interactions between them, parameter optimization becomes a complex and time-consuming process that relies heavily on human effort and an iterative trial-and-error approach. At present, this type of configuration parameter problem has been classified as the Configuration of Distributed Message Systems (CDMS) problem, for which the goal is to obtain the optimal set of parameters for achieving the best performance. In order to deal with this complex problem, an automatic optimization method is proposed in this paper, which combines an automatic script-based deployment and running framework with an improved Genetic Algorithm (GA) in cooperation with the Random Forest (RF) and statistical methods, named the Directional-Mutation Genetic Algorithm (DMGA). The framework and algorithm are designed to find the best configuration efficiently, obtaining optimal parameters accurately without human supervision. Actual results of comparison experiments based on the Kafka system show that the DMGA algorithm significantly improves the data throughput performance of Kafka, and its optimization speed is more efficient than the basic GA, highlighting its potential to quickly optimize Kafka's operational efficiency in different scenarios.

关键词： Statistical analysis System performance Production Throughput Sensors Tuning Streams Genetic algorithms Message systems Convergence

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