A Digital twin for Diesel Engines: Operator-infused PINNs with Transfer Learning for Engine Health Monitoring

• URL: http://arxiv.org/abs/2412.11967v1
• Date: Mon, 16 Dec 2024 16:47:15 GMT
• Title: A Digital twin for Diesel Engines: Operator-infused PINNs with Transfer Learning for Engine Health Monitoring
• Authors: Kamaljyoti Nath, Varun Kumar, Daniel J. Smith, George Em Karniadakis,
• Abstract summary: The objective of this study is to develop a computationally efficient neural network-based approach for identifying unknown parameters of a mean value diesel engine model. We propose a hybrid method combining physics informed neural networks, PINNs, and a deep neural operator, DeepONet. The operator network predicts independent actuator dynamics learnt through offline training, thereby reducing the PINNs online computational cost.
• Score: 6.106782783314526
• License:
• Abstract: Improving diesel engine efficiency and emission reduction have been critical research topics. Recent government regulations have shifted this focus to another important area related to engine health and performance monitoring. Although the advancements in the use of deep learning methods for system monitoring have shown promising results in this direction, designing efficient methods suitable for field systems remains an open research challenge. The objective of this study is to develop a computationally efficient neural network-based approach for identifying unknown parameters of a mean value diesel engine model to facilitate physics-based health monitoring and maintenance forecasting. We propose a hybrid method combining physics informed neural networks, PINNs, and a deep neural operator, DeepONet to predict unknown parameters and gas flow dynamics in a diesel engine. The operator network predicts independent actuator dynamics learnt through offline training, thereby reducing the PINNs online computational cost. To address PINNs need for retraining with changing input scenarios, we propose two transfer learning (TL) strategies. The first strategy involves multi-stage transfer learning for parameter identification. While this method is computationally efficient as compared to online PINN training, improvements are required to meet field requirements. The second TL strategy focuses solely on training the output weights and biases of a subset of multi-head networks pretrained on a larger dataset, substantially reducing computation time during online prediction. We also evaluate our model for epistemic and aleatoric uncertainty by incorporating dropout in pretrained networks and Gaussian noise in the training dataset. This strategy offers a tailored, computationally inexpensive, and physics-based approach for parameter identification in diesel engine sub systems.

Related papers

• Brain-Inspired Online Adaptation for Remote Sensing with Spiking Neural Network [17.315710646752176]
  This work proposes an online adaptation framework based on spiking neural networks (SNNs) for remote sensing. To our knowledge, this work is the first to address the online adaptation of SNNs. The proposed method enables energy-efficient and fast online adaptation on edge devices, and has much potential in applications such as remote perception on on-orbit satellites and UAV.
  arXiv  Detail & Related papers  (2024-09-03T08:47:53Z)
• DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach [49.56404236394601]
  We formulate the problem of joint DNN partitioning, task offloading, and resource allocation in Vehicular Edge Computing. Our objective is to minimize the DNN-based task completion time while guaranteeing the system stability over time. We propose a Multi-Agent Diffusion-based Deep Reinforcement Learning (MAD2RL) algorithm, incorporating the innovative use of diffusion models.
  arXiv  Detail & Related papers  (2024-06-11T06:31:03Z)
• Mobile Traffic Prediction at the Edge through Distributed and Transfer Learning [2.687861184973893]
  The research in this topic concentrated on making predictions in a centralized fashion, by collecting data from the different network elements. We propose a novel prediction framework based on edge computing which uses datasets obtained on the edge through a large measurement campaign.
  arXiv  Detail & Related papers  (2023-10-22T23:48:13Z)
• Physics-informed neural networks for predicting gas flow dynamics and unknown parameters in diesel engines [0.0]
  The aim is to evaluate the engine dynamics, identify unknown parameters in a "mean value" model, and anticipate maintenance requirements. The PINN model is applied to diesel engines with a variable-geometry turbocharger and exhaust gas recirculation. The study considers the use of deep neural networks (DNNs) in addition to the PINN model.
  arXiv  Detail & Related papers  (2023-04-26T19:37:18Z)
• Intelligence Processing Units Accelerate Neuromorphic Learning [52.952192990802345]
  Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy consumption and latency. We present an IPU-optimized release of our custom SNN Python package, snnTorch.
  arXiv  Detail & Related papers  (2022-11-19T15:44:08Z)
• Topics in Deep Learning and Optimization Algorithms for IoT Applications in Smart Transportation [0.0]
  This thesis investigates how different optimization algorithms and machine learning techniques can be leveraged to improve system performance. In the first topic, we propose an optimal transmission frequency management scheme using decentralized ADMM-based method. In the second topic, we leverage graph neural network (GNN) for demand prediction for shared bikes. In the last topic, we consider a highway traffic network scenario where frequent lane changing behaviors may occur with probability.
  arXiv  Detail & Related papers  (2022-10-13T11:45:30Z)
• Learning to Learn with Generative Models of Neural Network Checkpoints [71.06722933442956]
  We construct a dataset of neural network checkpoints and train a generative model on the parameters. We find that our approach successfully generates parameters for a wide range of loss prompts. We apply our method to different neural network architectures and tasks in supervised and reinforcement learning.
  arXiv  Detail & Related papers  (2022-09-26T17:59:58Z)
• Recursive Least-Squares Estimator-Aided Online Learning for Visual Tracking [58.14267480293575]
  We propose a simple yet effective online learning approach for few-shot online adaptation without requiring offline training. It allows an in-built memory retention mechanism for the model to remember the knowledge about the object seen before. We evaluate our approach based on two networks in the online learning families for tracking, i.e., multi-layer perceptrons in RT-MDNet and convolutional neural networks in DiMP.
  arXiv  Detail & Related papers  (2021-12-28T06:51:18Z)
• A Meta-Learning Approach to the Optimal Power Flow Problem Under Topology Reconfigurations [69.73803123972297]
  We propose a DNN-based OPF predictor that is trained using a meta-learning (MTL) approach. The developed OPF-predictor is validated through simulations using benchmark IEEE bus systems.
  arXiv  Detail & Related papers  (2020-12-21T17:39:51Z)
• Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks [55.0627904986664]
  Spiking Neural Networks (SNNs) usetemporal spike patterns to represent and transmit information, which is not only biologically realistic but also suitable for ultra-low-power event-driven neuromorphic implementation. This paper investigates the contribution of spike timing dynamics to information encoding, synaptic plasticity and decision making, providing a new perspective to design of future DeepSNNs and neuromorphic hardware systems.
  arXiv  Detail & Related papers  (2020-03-26T11:13:07Z)
• Indirect and Direct Training of Spiking Neural Networks for End-to-End Control of a Lane-Keeping Vehicle [12.137685936113384]
  Building spiking neural networks (SNNs) based on biological synaptic plasticities holds a promising potential for accomplishing fast and energy-efficient computing. In this paper, we introduce both indirect and direct end-to-end training methods of SNNs for a lane-keeping vehicle.
  arXiv  Detail & Related papers  (2020-03-10T09:35:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.