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A Tensor-based t-SVD-LSTM Remaining Useful Life Prediction Model for Industrial Intelligence

IEEE Transactions on Industrial Informatics 2022年 1-12页

作者： Wang, Xiaokang Yang, Laurence T. Cao, Enyuan Guo, Luyang Ren, Lei Deen, M. Jamal School of Computer Science and Technology Hainan University Haikou China School of Artificial Intelligence University of the Chinese Academy of Sciences Beijing China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Automation Science and Electrical Engineering Beihang University Beijing China Department of Electrical and Computer Engineering McMaster University Hamilton Canada

The Industrial Internet of Things (IIoT) integrates smart sensors and actuators for the widespread digitization and enhancement of industrial and manufacturing processes. Smart equipment is used to improve the industrial intelligence and make industrial production more flexible, safer and more efficient. For complex equipment, product life-cycle management (PLM) including remaining useful life (RUL) is one of the essential issues for industrial intelligence. In this paper, a tensor-based remaining useful life prediction model is proposed to facilitate the life-cycle management, which combines features from time domain and frequency domain. For the characteristics of continuous generation of industrial data streaming, tensor singular value decomposition (t-SVD) is combined with long shortterm memory network (LSTM) method to predict the RUL of devices from high-order and high-noise time series data. Finally, experiments are carried out on three different data sets including the battery charge and discharge data set, the bearing acceleration life cycle data set, and the turbofan data set to measure the performance of the proposed model. IEEE

关键词： Time domain analysis

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Gaze Betray You: Abnormal Attention Detection from Human-Robot Interaction Dynamics

Gaze Betray You: Abnormal Attention Detection from Human-Rob...

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IEEE International Conference on Robotics and Biomimetics

作者： Fengjun Mu Jingting Zhang Chaobin Zou Zonghai Huang Rui Huang Hong Cheng Center for Robotics School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Monitoring abnormal attention has a wide range of applications in Human-Robot Interaction systems, such as autonomous driving, virtual reality, and remote operation. Due to factors like fatigue, drowsiness, and distraction, human attention can deviate from tasks that require focus, resulting in abnormal attention during interactions. Monitoring for abnormal attention is essential to ensure the safety of human-robot interactions. However, existing methods often rely on un-explainable algorithms such as neural networks, making them difficult to trust in scenarios like autonomous driving, where explicit decision-making processes and mechanisms are required. This paper presents EyeAAD, an abnormal attention detection method based on dynamic system theory. This method uses gaze signals from a serious game designed to simulate human-robot interaction. We developed several attention-related features for interpretable modeling using a deterministic learning method. By analyzing the matching relationship between the gaze signal and the predicted status from the learned dynamic models, we can detect abnormal human attention patterns. To validate our approach, we designed a serious game for eye movement data acquisition in human-robot interaction. Our analysis showed that EyeAAD enables efficient, real-time monitoring of human attention.

关键词： Solid modeling Human-robot interaction Games Virtual reality Feature extraction Safety Pattern matching Monitoring Autonomous vehicles Robots

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Crack opening shape reconstruction method in magnetic flux leakage imaging 3

Crack opening shape reconstruction method in magnetic flux l...

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3rd International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Li, Shengping Zhang, Jie Liu, Zheng Zhang, Xu Bai, Libing Chen, Cong University of Electronic Science and Technology of China School of Automation Engineering Chengdu China China Petroleum Pipeline Inspection Technology Co. Ltd Langfang China

ISBN: (纸本)9781665492812

Magnetic flux leakage (MFL) is widely used in the detection of ferromagnetic materials. In the study of the reconstruction of cracks, the opening crack shape reconstruction method is the first and crucial step. However, current research concerns little about the reconstruction of the opening profile. This paper proposed an opening shape reconstruction method by introducing the Newton-Raphson and iterative Tikhonov regularization method. And the solenoid model applicable to the derivation of magnetic fields with complex defects is presented as an analytical forward model of magnetic fields. The sensitivity matrix of the analytical model of the magnetic field is adapted by improving the differential method of the magnetic field. The comparative experimental results show that this method has good results for defect profile reconstruction in MFL inspection and has apparent advantages in accuracy compared with the traditional defect profile segmentation method. © 2022 IEEE.

关键词： iterative Tikhonov regularization magnetic flux leakage Newton-Raphson nondestructive testing opening shape reconstruction

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Enhancing Dynamic Parameter Identification for Supernumerary Robotic Limbs Through Uncertainty Compensation

Enhancing Dynamic Parameter Identification for Supernumerary...

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Chinese automation Congress (CAC)

作者： Lufeng Chen Jingwen Lan Qi'ao Zhan Zhinan Peng Wenjiang Li Jing Qiu Center for Robotics School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Accurate dynamic modeling is fundamental for wearable robotics, such as Supernumerary Robotic Limbs (SRLs), to ensure precise control and effective interaction with the environment. This paper presents an enhanced dynamic model for an SRL, incorporating uncertainty compensation to improve accuracy and robustness. The SRL dynamics are streamlined by linearizing the system and identifying a minimal set of inertia parameters. Using the Coot optimization algorithm, we refine the Fourier excitation trajectory parameters to optimize the condition number of the observation matrix, followed by parameter identification through the least squares method. To further reduce discrepancies between predicted and actual joint torques, an uncertainty term is introduced, validated through a neural network designed to predict torque variations. The method not only addresses but also compensates for uncertainties across various motion states of the SRL. Experimental results demonstrate that the proposed model improves prediction accuracy and robustness for our developed 6-DoF SRL compared to traditional methods.

关键词： Uncertainty Accuracy Torque Parameter estimation Computational modeling Predictive models Aerodynamics Robustness Trajectory Robots

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Multi-Scale Adaptive-Routing Capsule Contrastive Network-Based Intelligent Fault Diagnosis Method for Rotating Machinery Under Noisy Environment and Labels

SSRN

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SSRN 2024年

作者： Xiong, Yucheng Liu, Zhiwen Hao, Liang School of Automation Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China

The robustness and accuracy of fault diagnosis are affected by noisy environments and labels from manual annotation errors in vibration signal data collected during the operation of mechanical equipment. Therefore, this study develops a multi-scale adaptive-routing capsule contrastive network-based intelligent fault diagnosis method for rotating machinery under noisy environment and labels. First, multi-scale residual blocks (MSRB) are introduced into the capsule network to utilize receptive fields of different scales, comprehensively capturing multi-scale feature information. Meanwhile, the structure of capsule network is further enhanced by employing a non-iterative adaptive-routing algorithm, preventing the amplification of irrelevant features due to the sparsification of coupling coefficients and improving the accuracy of classification model. Second, a self-supervised contrastive learning module is added to enhance the model's discriminative ability for multiscale features, improving the model's representation capabilities. Finally, the proposed method achieves "end-to-end" intelligent recognition of multiple faults by optimizing the model parameters via the established loss function and input data. Experimental results demonstrate that the proposed method significantly reduces the impact of noisy environments and labels, achieving high-precision classification. Compared to other classification methods, the proposed method has a clear advantage in noise resistance and stronger robustness. © 2024, The Authors. All rights reserved.

关键词： Rotating machinery

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Multi-Synchronization of Coupled Multi-Stable Memristive Cohen-Grossberg Neural Networks with Mixed Time-Delays

SSRN

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SSRN 2024年

作者： Peng, Libiao Bi, Dongjie Li, Xifeng Li, Hong Tang, Yu Xie, Yongle School of Automation Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China

This paper investigates the multi-synchronization issue for a class of coupled multi-stable memristive Cohen-Grossberg neural networks with both additive time delays and distributed delays. First, the multi-stability of the isolated subnetwork with a general class of activation function is analyzed. By employing state-space decomposition, M-matrix and fixed point theory, some sufficient conditions are deduced to ensure that each subnetwork of the coupled networks has (r + 1)n locally exponential stable equilibrium states. In the scenario where the time delays are continuously differentiable, a unique Lyapunov-Krasovskii function is established and some sufficient conditions are derived for the coupled system to achieve multi-synchronization under feedback control. Additionally, in circumstances where the time delays are immeasurable or discontinuous, a new feedback controller is constructed based on the system's model parameters, and a delay-independent finite-time multi-synchronization criterion is proposed. Finally, three numerical examples are provided to illustrate the effectiveness of the proposed methods. © 2024, The Authors. All rights reserved.

关键词： Feedback control

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Domain-Specific Dual Network With Unsupervised Domain Adaptation for Transfer Fault Prognosis Across Machines Using Multiple Source Domains

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Khanal, Smaran Huang, Hong-Zhong Huang, Cheng-Geng Dahal, Amrit Huang, Tudi Niazi, Sajawal Gul University of Electronic Science and Technology of China School of Mechanical and Electrical Engineering Center for System Reliability and Safety Chengdu611731 China University of Electronic Science and Technology of China School of Automation Engineering Center for System Reliability and Safety Chengdu611731 China

Deep learning (DL)-based fault prognostic methods often require extensive training data, which can be challenging to obtain due to time, cost, or safety constraints. However, labeled data from related but distinct machines, such as from accelerated degradation experiments, can offer valuable insights. Most transfer learning (TL) methods focus on single-source domain adaptation (DA) within the same machine, overlooking variability within the source domain and missing the opportunity to leverage multisource historical data effectively for application on different machines lacking run-to-failure data. To address these limitations, a domain-specific dual network (DSDN) is proposed for transfer fault prognosis across heterogeneous machines using multisource domain data. The DSDN consists of two feature extraction modules: domain-specific degradation feature extraction (DSDFE) and domain-specific high-level feature extraction (DSHLFE). These modules incorporate a deep convolutional neural network (DCNN) enhanced with multiple convolutional block attention modules (CBAMs) and a multilayer perceptron (MLP). The outputs from modules are concatenated to generate domain-invariant representations, effectively capturing both time-series and time-frequency spectrum features. By incorporating the DA technique, this approach mitigates discrepancies between the source and target domains, enabling accurate remaining useful life (RUL). The proposed method enhances the extraction of robust domain-invariant features for each source-target domain pair separately, improving prognostic accuracy by mitigating negative transfer. The effectiveness of the DSDN is validated through extensive case studies on single-source and multisource bidirectional transfer fault prognosis tasks across machines, utilizing bearing datasets from the IEEE prognostic and health management (PHM) Challenge 2012 and XJTU-SY. A comparative analysis further highlights its superiority over existing methods. © 1963-2012

关键词： Convolutional neural networks

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Magneto-Optical Imaging and Defect Detection Under Pulsed Magnetic Field Excitation

Magneto-Optical Imaging and Defect Detection Under Pulsed Ma...

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China Instrument and Control Society (ACCIS), Academic Conference of

作者： Yiling Liang Lulu Tian dept. The School of Automation Engineering University of Electronic Science and Technology of China Chengdu Sichuan

ISBN: (数字)9798350350326

ISBN: (纸本)9798350350333

Eddy current magneto-optical detection technology has been widely applied in various fields, including non-destructive testing, aerospace industry, archaeological artifacts, and art inspection. It enables the acquisition of internal defect and stress information without damaging the surface of the object. This study aims to investigate the fundamental principles of defect detection using a magneto-optical imaging system under different laser frequencies. An experimental setup was designed to expand the laser spot to a 5cmx 5cm magnetic field region using a beam expander, and tests were conducted within the range of 0.25mT-1mT. The feasibility of defect detection at different laser frequencies was experimentally verified. The results demonstrate that the system maintains high stability in dynamic measurements at various laser frequencies and possesses a wide range of defect detection capabilities. Optical image signals were extracted using a CCD camera, and defect information was oriented and rapidly acquired through image processing methods. The research indicates that this method can effectively detect and extract defect information within the maximum brightness of the laser source and the peak excitation of the signal generator, showing significant application prospects.

关键词： Measurement by laser beam Production Inspection Laser stability Frequency measurement Steel Magnetic fields Magnetooptic effects Defect detection Testing

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Design and Analysis of High-precision Frame-type Optomechanical Gyroscope

Design and Analysis of High-precision Frame-type Optomechani...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Chengwei Xian Pengju Kuang Zihan Huang Senyu Zhang Zhe Li Guangjun Wen Yongjun Huang Sichuan Provincial Engineering Research Center of Communication Technology for Intelligent IoT School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electronic Engineering Chengdu Technological University Chengdu China School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

In this paper, a new structure of high precision frame-type optomechanical gyroscope is proposed. Based on the basic theory of photonic crystal cavity optomechanical system, the working mechanism of the new high precision micro gyroscope is analyzed. Through the simulation optimization and numerical calculation of the mechanical structure and optical structure of the gyroscope, the theoretical measurement sensitivity of angular velocity is 261.9mV/(°/s), and the rate noise density is 0.011(°/s)/Hz 1/2 . Based on the micro-nano fabrication process, the sample of the cavity optomechanical gyroscope chip was fabricated on SOI, and the preliminary test of the gyroscope chip was carried out on the vacuum test platform. With the same magnitude of inertia mass, the precision of this new micro gyroscope is obviously better than that of the traditional microelectromechanical system (MEMS) gyroscope, which provides a new path for realizing a high-precision, easy-to-integrate-on-chip, and strongly anti-electromagnetic interference gyroscope, and has great potential application value in high precision inertial navigation, micro vibration detection and other technical fields.

关键词： Vibrations Sensitivity Optical device fabrication Noise Photonic crystals Optical variables measurement Adaptive optics

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Coverage control for mobile sensor networks with unknown terrain roughness and nonuniform time-varying communication delays

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Science China(Information Sciences) 2023年第12期66卷 80-93页

作者： Peng WANG Cheng SONG Lu LIU Department of Biomedical Engineering City University of Hong Kong School of Automation Nanjing University of Science and Technology City University of Hong Kong Chengdu Research Institute

This paper investigates the coverage control problem on a circle with unknown terrain roughness and nonuniform time-varying communication delays. Adaptive coverage control laws are proposed for mobile sensors to collaboratively estimate the unknown roughness function using the basis function approximation approach. Moreover, contrary to existing studies, nonuniform time-varying communication delays are considered in this paper. Under the proposed adaptive coverage control laws, the sensor network can be driven to its optimal configuration minimizing the coverage cost function in the presence of nonuniform communication delays, and each sensor can learn the true roughness function. Finally, a simulation example is provided to show the effectiveness of the proposed control laws.

关键词： multi-agent systems cooperative control adaptive control cost function time-varying communication delays

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