3D Convolutional Selective Autoencoder For Instability Detection in Combustion Systems

• URL: http://arxiv.org/abs/2101.01877v1
• Date: Wed, 6 Jan 2021 05:29:00 GMT
• Title: 3D Convolutional Selective Autoencoder For Instability Detection in Combustion Systems
• Authors: Tryambak Gangopadhyay, Vikram Ramanan, Adedotun Akintayo, Paige K Boor, Soumalya Sarkar, Satyanarayanan R Chakravarthy, Soumik Sarkar
• Abstract summary: Instabilities arising in combustion chambers of engines are mathematically too complex to model. We propose a novel deep learning architecture called 3D convolutional selective autoencoder (3D-CSAE) to detect the evolution of self-excited oscillations.
• Score: 6.4520575698063105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While analytical solutions of critical (phase) transitions in physical systems are abundant for simple nonlinear systems, such analysis remains intractable for real-life dynamical systems. A key example of such a physical system is thermoacoustic instability in combustion, where prediction or early detection of an onset of instability is a hard technical challenge, which needs to be addressed to build safer and more energy-efficient gas turbine engines powering aerospace and energy industries. The instabilities arising in combustion chambers of engines are mathematically too complex to model. To address this issue in a data-driven manner instead, we propose a novel deep learning architecture called 3D convolutional selective autoencoder (3D-CSAE) to detect the evolution of self-excited oscillations using spatiotemporal data, i.e., hi-speed videos taken from a swirl-stabilized combustor (laboratory surrogate of gas turbine engine combustor). 3D-CSAE consists of filters to learn, in a hierarchical fashion, the complex visual and dynamic features related to combustion instability. We train the 3D-CSAE on frames of videos obtained from a limited set of operating conditions. We select the 3D-CSAE hyper-parameters that are effective for characterizing hierarchical and multiscale instability structure evolution by utilizing the dynamic information available in the video. The proposed model clearly shows performance improvement in detecting the precursors of instability. The machine learning-driven results are verified with physics-based off-line measures. Advanced active control mechanisms can directly leverage the proposed online detection capability of 3D-CSAE to mitigate the adverse effects of combustion instabilities on the engine operating under various stringent requirements and conditions.

Related papers

• Hybrid Temporal Differential Consistency Autoencoder for Efficient and Sustainable Anomaly Detection in Cyber-Physical Systems [0.0]
  Cyberattacks on critical infrastructure, particularly water distribution systems, have increased due to rapid digitalization. This study addresses key challenges in anomaly detection by leveraging time correlations in sensor data. We propose a hybrid autoencoder-based approach, referred to as hybrid TDC-AE, which extends TDC by incorporating both deterministic nodes and conventional statistical nodes.
  arXiv  Detail & Related papers  (2025-04-08T09:22:44Z)
• Stabilization Analysis and Mode Recognition of Kerosene Supersonic Combustion: A Deep Learning Approach Based on Res-CNN-beta-VAE [13.734716568876378]
  scramjet engine is a key propulsion system for hypersonic vehicles. Complex interactions between fuel injection, turbulent combustion, and aerodynamic effects are crucial for ensuring combustion stable in scramjet engines. This work introduces an innovative learning framework that combines dimensionality reduction via the Residual Contemporalal Neural Network-beta-volutional Autoencoder (Res-CNN-beta-VAE) model with unsupervised clustering (K-means)
  arXiv  Detail & Related papers  (2025-03-17T03:00:01Z)
• A Supervised Machine-Learning Approach For Turboshaft Engine Dynamic Modeling Under Real Flight Conditions [0.0]
  In this work, we explore different Neural Network architectures to model the turboshaft engine of Leonardo's AW189P4 prototype. The models are trained on an extensive database of real flight tests featuring a variety of operational maneuvers. To complement the neural network approach, we apply Sparse Identification of Dynamics (SINDy) to derive a low-dimensional dynamical model.
  arXiv  Detail & Related papers  (2025-02-19T21:45:53Z)
• Learning Physics From Video: Unsupervised Physical Parameter Estimation for Continuous Dynamical Systems [49.11170948406405]
  State-of-the-art in automatic parameter estimation from video is addressed by training supervised deep networks on large datasets. We propose a method to estimate the physical parameters of any known, continuous governing equation from single videos.
  arXiv  Detail & Related papers  (2024-10-02T09:44:54Z)
• Efficient Materials Informatics between Rockets and Electrons [0.0]
  This dissertation focuses on the design of functionally graded materials (FGMs) incorporating ultra-high temperature refractory high entropy alloys (RHEAs) At the atomistic level, a data ecosystem optimized for machine learning (ML) from over 4.5 million relaxed structures, called MPDD, is used to inform experimental observations and improve thermodynamic models. The resulting multi-level discovery infrastructure is highly generalizable as it focuses on encoding problems to solve them easily rather than looking for an existing solution.
  arXiv  Detail & Related papers  (2024-07-05T17:03:26Z)
• Atlas3D: Physically Constrained Self-Supporting Text-to-3D for Simulation and Fabrication [50.541882834405946]
  We introduce Atlas3D, an automatic and easy-to-implement text-to-3D method. Our approach combines a novel differentiable simulation-based loss function with physically inspired regularization. We verify Atlas3D's efficacy through extensive generation tasks and validate the resulting 3D models in both simulated and real-world environments.
  arXiv  Detail & Related papers  (2024-05-28T18:33:18Z)
• Dynamical Mode Recognition of Coupled Flame Oscillators by Supervised and Unsupervised Learning Approaches [7.088178570214894]
  Combustion instability is one of the most challenging problems in combustion research. Identifying and understanding combustion instability is essential to ensure the safe and reliable operation of many combustion systems.
  arXiv  Detail & Related papers  (2024-04-27T06:44:39Z)
• An Unsupervised Adversarial Autoencoder for Cyber Attack Detection in Power Distribution Grids [0.0]
  This paper proposes an unsupervised adversarial autoencoder (AAE) model to detect false data injection attacks (FDIAs) in unbalanced power distribution grids. The proposed method utilizes long short-term memory (LSTM) in the structure of the autoencoder to capture the temporal dependencies in the time-series measurements. It is tested on IEEE 13-bus and 123-bus systems with historical meteorological data and historical real-world load data.
  arXiv  Detail & Related papers  (2024-03-31T01:20:01Z)
• DyEdgeGAT: Dynamic Edge via Graph Attention for Early Fault Detection in IIoT Systems [12.641578474466646]
  DyEdgeGAT is a novel approach for early-stage fault detection in IIoT systems. It incorporates operating condition contexts into node dynamics modeling, enhancing its accuracy and robustness. We rigorously evaluated DyEdgeGAT using both a synthetic dataset and a real-world industrial-scale flow facility benchmark.
  arXiv  Detail & Related papers  (2023-07-07T12:22:16Z)
• Advancing Reacting Flow Simulations with Data-Driven Models [50.9598607067535]
  Key to effective use of machine learning tools in multi-physics problems is to couple them to physical and computer models. The present chapter reviews some of the open opportunities for the application of data-driven reduced-order modeling of combustion systems.
  arXiv  Detail & Related papers  (2022-09-05T16:48:34Z)
• Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking [76.27433308688592]
  Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience. Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
  arXiv  Detail & Related papers  (2022-06-07T13:51:35Z)
• In-flight Novelty Detection with Convolutional Neural Networks [0.0]
  This paper proposes that system output measurements are prioritised in real-time for the attention of preventative maintenance decision makers. We present a data-driven system for online detection and prioritisation of anomalous data. The system is capable of running in real-time on low-power embedded hardware and is currently in deployment on the Rolls-Royce Pearl 15 engine flight trials.
  arXiv  Detail & Related papers  (2021-12-07T15:19:41Z)
• On-board Fault Diagnosis of a Laboratory Mini SR-30 Gas Turbine Engine [54.650189434544146]
  A data-driven fault diagnosis and isolation scheme is explicitly developed for failure in the fuel supply system and sensor measurements. A model is trained using machine learning classifiers to detect a given set of fault scenarios in real-time on which it is trained. Several simulation studies were carried out to demonstrate and illustrate the proposed fault diagnosis scheme's advantages, capabilities, and performance.
  arXiv  Detail & Related papers  (2021-10-17T13:42:37Z)
• OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation [50.59541802645156]
  Operational Space Control (OSC) has been used as an effective task-space controller for manipulation. We propose OSC for Adaptation and Robustness (OSCAR), a data-driven variant of OSC that compensates for modeling errors. We evaluate our method on a variety of simulated manipulation problems, and find substantial improvements over an array of controller baselines.
  arXiv  Detail & Related papers  (2021-10-02T01:21:38Z)
• gradSim: Differentiable simulation for system identification and visuomotor control [66.37288629125996]
  We present gradSim, a framework that overcomes the dependence on 3D supervision by leveraging differentiable multiphysics simulation and differentiable rendering. Our unified graph enables learning in challenging visuomotor control tasks, without relying on state-based (3D) supervision.
  arXiv  Detail & Related papers  (2021-04-06T16:32:01Z)

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.