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作者： Huang, Baoshan Xu, Yuandong Mba, David Li, Xiaochuan Hu, Niaoqing Gu, Fengshou Ball, Andrew Beijing Institute of Technology Zhuhai Campus Zhuhai519088 China University of Huddersfield HuddersfieldH1 3DH United Kingdom Faculty of Computing Engineering and Media De Montfort University The Gateway LeicesterL1 9BH United Kingdom College of Mechatronics and Automation National University of Defense Technology Changsha410073 China

Planet gear bearings are of critical components in the main gearbox of mission critical helicopters. Because of multiple excitation sources, weak cyclisation signatures and vibration transmission modulation (VTM) make it very challenging to diagnose the healthy conditions of planet bearings. This paper proposes a new diagnostic method based on modulation signal bispectrum (MSB) analysis. MSB can effectively suppress random noise by phase alignment and extract deterministic modulation effects at high frequency resolution, which allows the closely distributed and weak bearing signatures to be pull out with high accuracy. It thus can combine VTM effect with bearing signatures to enhance the weak signatures for achieving more reliable detection and diagnosis. Based on datasets from a multiple stage 1300 kW epicyclic gearbox employed in a commercial helicopter, this MSB based method is evaluated under different fault severity and operating conditions. It shows that the amplitudes coupled between fault frequency and epicycle carrier frequency can have more deterministic information regarding to bearing vibrations. Consequently, MSB coherences and magnitudes can jointly give accurate diagnostic results that are consistent with different fault severity and operating conditions, showing outstanding performance of MSB analysis. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Modulation

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Data-driven modeling fast-slow coupling delayed dynamics using noisy data and sparse enhancement

Data-driven modeling fast-slow coupling delayed dynamics usi...

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International Conference on Industrial Artificial Intelligence (IAI)

作者： Huifang Tang Yuqiang Wu Yingming Dong Cheng Cheng Xiuting Li College of Science Huazhong Agricultural University Wuhan China School of Artificial Intelligence and Automation Huazhong University of Science and Techonology Wuhan China Yunnan Key Laboratory of Mechatronics Applied Technology Yunnan Mechanical Research and Design Institute of Machinery Research Institute Group Yunnan China Autonomous Intelligent Unmanned System Engineering Research Center Ministry of Education of China Huazhong University of Science and Technology Wuhan China

In this work, we focus on fast-slow delayed dynamical systems, which are usually described by coupled delayed differential equations with two-time scales. The purpose of this article is to propose a data-driven discovery method to identify the fast-slow delayed differential equations from noisy measurements. The proposed method blends delay dictionary-based sparse identification of nonlinear dynamics (SINDy) framework and a fixed interval sampling strategy. Specifically, we leverage the SINDy methodology to describe the fast-slow delayed differential equations by a few active terms of a large dictionary containing the delay terms and many candidate nonlinear basis functions. To identify the active terms and the corresponding coefficients, a sequential thresholded re-weighted ℓ 2 least squares algorithm is used to solve the resulting sparse regression problem to enhance the sparsity. Once a sparse model with fixed delay is selected, the proposed method further estimates the delayed time by calculating the reconstruction error from the sparse model until the one that makes the reconstruction error minimum. We verify the effectiveness of the proposed method with several classical fast-slow coupling delayed systems. The experimental results show that the proposed method can well automate the fast-slow delayed differential equations from noisy data and the delayed terms also can be detected accurately.

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Combined Signals Based Identification Scheme for the Hammerstein-Wiener System with Process Noise

Combined Signals Based Identification Scheme for the Hammers...

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Data Driven Control and Learning Systems (DDCLS)

作者： Feng Li Jia Li College of Electrical and Information Engineering Jiangsu University of Technology Changzhou China College of Mechatronics Engineering and Automation Shanghai University Shanghai China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

An identification scheme of the nonlinear Hammerstein-Wiener system with colored noise with the help of combined signals is proposed. The combined signals are used to active the Hammerstein-Wiener system, resulting in the identification process are significantly simplified. Firstly, the correlation analysis algorithm is employed to identify the output nonlinear block parameters and linear block parameters, thus the process noise can be compensated by correlation function. Furthermore, the immeasurable internal variable and the unknown noise term in identification system are replaced with the output of designed auxiliary model and estimated residual, therefore, auxiliary model based recursive extended least square identification scheme can be obtained to learn the input nonlinear block parameters and noise model parameter. Simulations results are given to demonstrate the advantage of the identification scheme.

关键词： Interference Autoregressive processes Nonlinear dynamical systems Heuristic algorithms Correlation Colored noise Analytical models

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Dynamic Analysis of Reciprocating Compressor with Crankshaft Crack

Dynamic Analysis of Reciprocating Compressor with Crankshaft...

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International Conference on Data Science and Business Analytics (ICDSBA)

作者： Shouguo Cheng School of Mechatronics Engineering and Automation Shanghai University Shanghai China Jiangyin Polytechnic College Jiangsu China

In order to study the influence of crankshaft crack on the dynamics of reciprocating compressor, the model of crankshaft flexible body with crack was established by using finite element method, and the flexible body was activated for analysis and calculation during dynamic simulation. Through the analysis of the simulation results of the rigid body model and rigid flexible coupling model, it can be seen that the crank-shaft has a small deformation under the force of piston force. Comparing the simulation results of rigid flexible coupling model with crack and without crack when crankshaft rotates clockwise, it is found that the existence of crack increases the deformation of crankshaft. By comparing the simulation results of the rigid flexible coupling model with and without crack when the crankshaft rotates counterclockwise, the influence of crankshaft steering on the deformation of cracked crankshaft is found. The stability of the system is discussed by using phase diagram and Lyapunov exponent. The results show that the motion of the system is chaotic when the crankshaft with cracks rotates in reverse direction.

关键词： Deformable models Couplings Pistons Analytical models Simulation Dynamics Force

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Just-in-time learning based integrated MPC-ILC control for batch processes

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Chinese Journal of Chemical engineering 2018年第8期26卷 1713-1720页

作者： Li Jia Wendan Tan Shanghai Key Laboratory of Power Station Automation Technology Department of Automation College of Mechatronics Engineering and Automation Shanghai University

Considering the two-dimension(2 D) characteristic and the unknown optimal trajectory problem of the batch processes, an integrated model predictive control-iterative learning control(MPC-ILC) for batch processes is proposed in this paper. Firstly, the batch-axis information and time-axis information are combined into one quadratic performance index. It implies the integration of ILC and MPC algorithm idea, which leads to superior tracking performance and better robustness against disturbance and uncertainty. To address the problem of the unknown optimal trajectory, both time-varying prediction horizon and end product quality control are employed. Moreover, an integrated 2 D just-in-time learning(JITL) model is used to improve the predictive accuracy. Furthermore, rigorous description and proof are presented to prove the convergence and tracking performance of the proposed MPC-ILC strategy. The simulation results show the effectiveness of the proposed method.

关键词： Model predictive control Batch process Just-in-time learning （JITL） model

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Record Framework Utilization for the Collection of Wireless Device Static Users

Record Framework Utilization for the Collection of Wireless ...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Maganti Syamala Annaji Kuthe V. Selvakumar Alankrita Joshi K. Baranidharan Joshuva Arockia Dhanraj Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering K. D. K. College of Engineering Nagpur Maharashtra India Department of Master of Business Administration Sri Sai Ram Institute of Technology Chennai Tamil Nadu India Department of Electronics & Communication Engineering Graphic Era Deemed to be University Dehradun Uttarakhand Department of Master of Business Administration Sri Sairam Institute of Technology Chennai Tamil Nadu India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Chennai Tamil Nadu India

The cell of today is a typical instrument. It has become a significant "social item," as opposed to only a minor "specialized object," in the regular daily existences of its users. In this article, we look at a few ongoing improvements in the examination of mobile phone data. With the developing openness of enormous, anonymised datasets, this field of concentrate previously seemed decade prior and has since formed into an independent subject. The estimating investigation of record framework utilization for a gathering of mobile gadget users in different spots is introduced in this work. The purpose of this research is to refute the notion that mobile system behaviour can be modelled as being uniform across different environments. The research demonstrates that when participants travel between places, they obtain statistically substantially diverse sets of data. These findings call into question how mobile computing systems are normally evaluated, which often models a user's behaviour as being comparable to her behaviour at a single place over a longer length of time.

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Learning-based real-time optimal control of unmanned surface vessels in dynamic environment with obstacles

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Ocean engineering 2025年 335卷

作者： Lai, Jialun Ren, Zhigang Wu, Zongze Tan, Qi Xie, Shengli School of Automation Guangdong University of Technology Guangzhou Guangdong510006 China College of Mechatronics and Control Engineering Shenzhen University Shenzhen Guangdong518052 China Ministry of Education Guangzhou510006 China Shenzhen Guangdong518123 China

In Zermelo's navigation problem (ZNP), classic solutions struggle with integrating obstacle avoidance constraints and adjusting the control law online in response to uncertain disturbances. Learning-based methods that leverage prior knowledge offer a promising approach for achieving real-time optimal control. This paper proposes an end-to-end real-time optimal control strategy based on a deep neural network (DNN) with homotopy embedding for an underactuated unmanned surface vessel (USV) navigating dynamic environments with obstacles. The proposed framework leverages a bi-level structure, combining the advantages of DNN-based learning strategies and optimal control theory. To achieve this, optimal trajectories are generated by offline solving a nonlinear trajectory planning problem for the USV, considering environmental uncertainties and obstacle constraints. Under the Lipschitz continuity constraint, the DNN structure with homotopy parameter embedding utilizes a dataset incorporating prior knowledge of the navigation task to ensure stable learning. Consequently, the well-trained network can be effectively employed as an online controller to generate optimal feedback control based on the current state and varying disturbance intensities. Finally, numerical results are presented to demonstrate the viability and efficiency of the proposed control strategy in planning and steering the USV to its intended targets, showcasing robustness to disturbances from initial conditions and the environment, while also satisfying obstacle constraints. © 2025

关键词： Robustness (control systems)

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Skin Cancer Prediction using Machine Learning and Neural Networks

Skin Cancer Prediction using Machine Learning and Neural Net...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Neha Tyagi Logeshwari Dhavamani Mohammed Saleh Al Ansari Bhasker Pant Dilip Kumar Jang Bahadur Saini Joshuva Arockia Dhanraj Department of CSE Amity University Greater Noida Uttar Pradesh India Department of Information Technology St. Joseph’s College of Engineering Chennai India Department of Chemical Engineering College of Engineering University of Bahrain Bahrain UAE Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering Himalayan School of Science and Technology Swami Rama Himalayan University Swami Ram Nagar Jolly Grant Dehradun Uttarakhand India Department of Automation and Robotics (ANRO) Department of Mechatronics Engineering Hindustan Institute of Technology and Science Padur Chennai India

The apparent similarities between skin conditions have made medical diagnosis difficult. Although melanoma is the most well-known type of skin cancer, other diseases have recently been responsible for a large number of fatalities. One of the biggest challenges in creating a dependable automatic categorization system is the absence of massive data. A deep learning (DL) system for identifying skin cancer is presented in this paper. The rapid development rate of melanoma skin cancer, its massive price of surgery, and its mortality rate have all heightened the need for timely diagnosis of skin disease. Most of the time, treating cancer cells requires time and careful detection. The commitment to deep learning powered by machine learning (ML) has been repeatedly shown in the medical sector. Skin cancer categorization has benefited significantly from growing study attention since it is amenable to visual pattern recognition. Studies have revealed that DL-based image classifications may be utilised to enhance skin cancer diagnosis or are on par with or even outperform human specialists. In this study, we provide a deep learning architecture that can identify skin cancer. Five state-of-the-art convolutional neural networks were trained using transfer learning to provide a simple classifier and a hierarchical (with two stages) classification that can differentiate between seven different species of moles. Experiments were conducted using data from the HAM10000 database, a huge collection of dermatoscopic pictures, with the use of data augmentation methods to boost results. The DenseNet201 network performed well in this experiment, as seen by the high classification accuracies and F-measures achieved with very few false negatives. The simple model outperformed the 2-levels model, with the best result coming from the first level, or a binary classification between nevi and non-nevi.

关键词： Deep learning Visualization Databases Sensitivity analysis Transfer learning Surgery Melanoma

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The influence of the clearance on the motion of the reciprocating compressor connecting rod

The influence of the clearance on the motion of the reciproc...

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International Conference on mechatronics and Intelligent Robotics, ICMIR 2018

作者： Cheng, Shouguo School of Mechatronics Engineering and Automation Shanghai University Shanghai China Department of Mechanical and Electrical Engineering Jiangyin Polytechnic College Jiangyin China

ISBN: (纸本)9783030002138

The effect of revolute joint clearance on reciprocating compressor’s motion is studied. The dynamic equations of the system are established through the Lagrange equation, and the clearance is equivalent to a rod without mass. The effects of clearance size and crank speed on the acceleration of the connecting rod in horizontal and vertical directions and the angular acceleration are discussed. The results show that the clearance has great influence on the motion parameters of the connecting rod. At last, the Lyapunov exponent and phase portrait are utilized in the analysis of nonlinear. © Springer Nature Switzerland AG 2019.

关键词： Connecting rods

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Nonlinear dynamic analysis of reciprocating compressor considering different clearance 3

Nonlinear dynamic analysis of reciprocating compressor consi...

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3rd IEEE International Conference on Electronic Information Technology and Computer engineering, EITCE 2019

作者： Cheng, Shouguo Shanghai University School of Mechatronics Engineering and Automation Shanghai China Department of Mechanical and Electrical Engineering Jiangyin Polytechnic College Shanghai China

ISBN: (纸本)9781728135847

The dynamic model of reciprocating compressor was established, and then it's dynamic behavior with different clearance was studied. The effect of clearance is modeled by elastic damped internal force to establish collision contact relationship. The dynamic simulation of reciprocating compressor under different crankshaft speeds and different clearance is carried out respectively. From the simulation results, it can be seen that the dynamic behavior of reciprocating compressor is greatly affected by different clearance and crankshaft speed. The number and time interval of two large contact collisions are directly related to crankshaft speed. Clearance shapes have little effect on two large collision frequencies and collision intervals. The Lyapunov exponent is used to study the nonlinear characteristics of the system. The results show that chaos occurs when the system contains different clearance. © 2019 IEEE.

关键词： Lyapunov methods

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