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2024 Prognostics and System Health Management Conference, PHM 2024

作者： Cai, Congrui Lu, Zhonghai School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Embedded Systems Master Program Sweden School of Electrical Engineering and Computer Science Department of Electrical Engineering KTH Royal Institute of Technology Sweden

ISBN: (纸本)9798350360585

Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms are better suited to model time-series data. However, the impact of RNN complexity on estimation accuracy is rarely discussed in the literature. This issue is important because choosing a lower-complexity model that delivers the same or similar performance as a higher-complexity model can increase implementation efficiency. In the paper, we use three RNN models, namely, the vanilla version, LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) to conduct RUL estimation for power electronic devices. We use two accelerated aging datasets, one dataset targeting the package failure of MOSFETs, and the other dataset targeting package failure of power diodes. Our study shows that a lower-complexity RNN does not necessarily deliver a lower performance. Similarly, a higher-complexity model does not assure a higher performance. As such, our work highlights the importance of selecting a proper neural network for RUL estimation not biased towards complex models. This is especially useful and important for implementing such RUL estimation techniques in embedded resource-constrained and speed-limited computins platforms. © 2024 IEEE.

关键词： Long short-term memory

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Giant Trevally Optimizer (GTO): Enhancing HVDC Transmission Capacity with Optimized Fault Current Limiters and HVDC Circuit Breaker Parameters 3

Giant Trevally Optimizer (GTO): Enhancing HVDC Transmission ...

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Ramkumar, M. Siva Saranya, N. Gopan, Gokul Baig, Rahmath Ulla Josha Daniel, S. Babu, M. Sns College of Technology Department of Ece India Karpagam College of Engineering Department of Artificial Intelligence and Data Science India Department of Mechanical Engineering Chennai602105 India Cambridge Institute of Technology Department of Artificial Intelligence and Machine Learning K R Puram Bangalore560086 India Hindustan College of Engineering and Technology Department of Eee India Hindusthan College of Engineering and Technology Master of Engineering in Embedded Systems India

ISBN: (纸本)9798331509675

The multi-terminal HVDC system relies heavily on circuit breakers (CBs) and fault current limiters (FCLs) for protection and performance reliability. This research describes a new Giant Trevally optimizer-based strategy for improving the performance of 4-terminal HVDC systems by optimizing crucial circuit breakers and fault current limiters. The proposed method uses time domain estimation to correctly capture fault dynamics, such as traveling waves and reflection coefficients and finds the worst-case fault locations. The multi-objective optimization seeks to optimize the overcurrent, overvoltage handling, fault clearance time, and energy absorption in arresters. This technology provides an effective, dependable protection solution that reduces fault recovery time and increases system stability. The output pulse of the HVDC system shows very low THD results 1.98% for the minimum load condition, 0.87% for median load, and 0.04% for peak load conditions. The results established the simulation of the model in a MATLAB environment. © 2025 IEEE.

关键词： MATLAB

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RUL Estimation for Power Electronic Devices Using RNNs

RUL Estimation for Power Electronic Devices Using RNNs

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Prognostics and System Health Management Conference (PHM)

作者： Congrui Cai Zhonghai Lu Embedded Systems Master Program School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Department of Electrical Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology

ISBN: (数字)9798350360585

ISBN: (纸本)9798350360592

关键词： Performance evaluation Semiconductor device modeling Recurrent neural networks Computational modeling Estimation Power electronics Data models

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Computational Network-on-Chip as Convolution Engine

Computational Network-on-Chip as Convolution Engine

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Technology, systems and Applications (VLSI-TSA/VLSI-DAT), International VLSI Symposium on

作者： Zhonghai Lu Mingrui Liu Department of Electrical Engineering School of EECS KTH Royal Institute of Technology Stockholm Sweden Embedded Systems master program School of EECS KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9798350360349

ISBN: (纸本)9798350360356

Inspired by PiN, Processing in Network-on-Chip (NoC), we propose a computational NoC as a convolution engine for accelerating convolutional neural networks in hardware. In contrast to traditional compute architectures where computation and communication are conducted serially and in separation, our computational NoC enables in-transit computation, meaning that computation is performed while packets are transferred in the network. In the paper, we present the router architecture that supports the novel in-transit computation concept, and use a running example to detail the entire convolution process in the computational NoC. Finally, we show simulated performance results in comparison with traditional NoC-based convolution engine.

关键词： Convolution Simulation Computer architecture Network-on-chip Very large scale integration Hardware Pins

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Age Feature Enhanced Neural Network for RUL Estimation of Power Electronic Devices

Age Feature Enhanced Neural Network for RUL Estimation of Po...

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International Conference on Prognostics and Health Management, PHM

作者： Zhonghai Lu Rui Shi Chao Guo Mingrui Liu Department of Electrical Engineering KTH Royal Institute of Technology Stockholm Sweden Embedded Systems Master Program KTH Royal Institute of Technology Stockholm Sweden

Like other deep learning problems, critical features are critical to enable effective estimation of Remaining Useful Lifetime (RUL) for power electronic devices using Neural Networks (NNs). However, these critical features are often indirectly obtained after data pre-processing, complicated either in form (high dimension) or in computation (computation-intensive pre-processing). In the paper, we suggest adding a simple direct feature, age, into the NN based RUL estimation technique. The rationale for incorporating this feature is that the device lifetime is a sum of past time (age) plus RUL. Thus it has a strong correlation to RUL. In our experiments using accelerated aging tests, we show that the new age feature enhanced Recurrent Neural Network (RNN) model can significantly improve estimation accuracy and shorten training convergence time. It also outperforms a state-of-the-art RNN model using derived time-domain statistical features.

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Enhanced Prognostics and Health Management in Automated Driving systems: Using Graph Neural Networks to Recognize Operational Contexts

Enhanced Prognostics and Health Management in Automated Driv...

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Prognostics and System Health Management Conference (PHM)

作者： Peng Su Yeqi Wang Conglei Xiang Erik Wendel Madhav Mishra DeJiu Chen Department of Engineering Design Mechatronics Division KTH Royal Institute of Technology Stockholm Sweden Embedded Systems Master Program of Electrical Engineering and Computer Science (EECS) School KTH Royal Institute of Technology Stockholm Sweden Digital Systems Division RISE Research Institutes of Sweden Gothenburg Sweden

ISBN: (数字)9798350360585

ISBN: (纸本)9798350360592

Prognostics and health management (PHM) is an engineering discipline that aims to maintain system behaviour and function and ensure mission success, safety and effectiveness. Addressing the challenges in prognostics and health management for modern intelligent systems, especially automated driving systems, is complex due to the contextual nature of faults. This complexity necessitates a thorough understanding of spatial, and temporal conditions, and relationships within operational scenarios and life-cycle stages. This paper introduces a framework designed to automatically recognize driving scenarios in automated driving systems using graph neural networks (GNNs). The framework extracts relational data from image frames, constructing graph-based models and transforming unstructured sensory data into structured data with diverse node types and relationships. A specific graph neural network processes the graph model to reveal and detect operational conditions and relationships. The proposed framework is evaluated using the KITTI dataset, demonstrating superior performance compared to conventional feed-forward networks such as MLP, particularly in handling relational data.

关键词： Graph neural networks Data models Safety Complexity theory Data mining Prognostics and health management Intelligent systems

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Hybrid energy harvesting based on piezoelectric and electromagnetic systems 1

Hybrid energy harvesting based on piezoelectric and electrom...

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1st International Conference on Advances in Smart Sensor, Signal Processing and Communication Technology, ICASSCT 2021

作者： Inba Rexy, A. Nancy Mary, J.S. Priyaadarshini, K.G. Rachael, Therese Department of Electrical and Electronics Engineering Loyola-ICAM College of Engineering and Technology Chennai India Department of Electrical and Electronics Engineering Loyola-ICAM College of Engineering and Technology Chennai India Master of Science in Electrical Engineering and Embedded Systems Hochschule Ravensburg Weingarten Germany Robert Bosch Engineering and Business Solutions Pvt Ltd Coimbatore India

The aim of this paper is to boost the power output of the hybrid energy harvester effectively and to provide electrical optimization for a hybrid energy harvesting system that produces two electrical outputs from a single source of mechanical input namely vibration. Two different electrical outputs are produced by using the concept of piezoelectricity and electromagnetic induction. In this paper, an electrical equivalent is obtained for the standalone piezoelectric system and the electromagnetic system. The rectification of the piezoelectric system along with its results is discussed. Different circuits for amplification is studied and simulated to improve the voltage. All the practical experimental outputs and the different software used to gain these outputs are recorded. The various applications of the hybrid system are reviewed and discussed. © Published under licence by IOP Publishing Ltd.

关键词： Piezoelectricity

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Giant Trevally Optimizer (GTO): Enhancing HVDC Transmission Capacity with Optimized Fault Current Limiters and HVDC Circuit Breaker Parameters

Giant Trevally Optimizer (GTO): Enhancing HVDC Transmission ...

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Electronics and Renewable systems (ICEARS), International Conference on

作者： M. Siva Ramkumar Saranya. N Gokul Gopan Rahmath Ulla Baig Josha Daniel S Babu M Department of ECE SNS College of Technology India Department of Artificial Intelligence and Data Science Karpagam College of Engineering India Department of Mechanical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai India Department of Artificial Intelligence and Machine Learning Cambridge Institute of Technology K R Puram Bangalore Department of EEE Hindustan College of Engineering and Technology India Master of Engineering in Embedded Systems Hindusthan College of Engineering and Technology India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

关键词： HVDC transmission Circuit breakers Stability analysis Circuit faults Fault current limiters Time-domain analysis Protection Voltage control Integrated circuit reliability Optimization

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Comparison between CMOS full adder and PTL full adder

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IOP Conference Series: Materials Science and engineering 2021年第1期1065卷

作者： M Amitha Deepa Master of Technology in VLSI Design and Embedded Systems Dept. of Electronics and Instrumentation Engineering Siddaganga Institute of Technology Tumakuru Assistant Professor Dept. of Electronics and Instrumentation Engineering Siddaganga Institute of Technology Tumakuru

In electronic industry the level of integration is an important aspect as makes the electronic device simpler and more reliable. The device density increases with the better level of integration. Power dissipation, area occupied and delay are some of the important factors that need to be considered. These parameters play a vital role in manufacturing portable electronic gadgets. Designing of full adder using conventional CMOS design and PTL has been shown in this paper. A comparison is made between the two designs with respect to power dissipation, delay and area (number of transistors). Mentor Graphics Tool is used in design and simulation of the full adder. The PTL full adder has a smaller number of transistors and lower power dissipation compared to CMOS full adder. With all the comparison made it will be concluded that the PTL full adder performance is better than the CMOS full adder.

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Hybrid energy harvesting based on piezoelectric and electromagnetic systems

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Journal of Physics: Conference Series 2021年第1期1921卷

作者： A Inba Rexy J S Nancy Mary K G Priyaadarshini Therese Rachael Department of Electrical and Electronics Engineering Loyola-ICAM College of Engineering and Technology Chennai India Master of Science in Electrical Engineering and Embedded Systems Hochschule Ravensburg-Weingarten Germany Robert Bosch Engineering And Business Solutions Pvt Ltd Coimbatore India

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