Brain-Inspired Spike Echo State Network Dynamics for Aero-Engine Intelligent Fault Prediction

• URL: http://arxiv.org/abs/2406.12918v1
• Date: Fri, 14 Jun 2024 04:06:17 GMT
• Title: Brain-Inspired Spike Echo State Network Dynamics for Aero-Engine Intelligent Fault Prediction
• Authors: Mo-Ran Liu, Tao Sun, Xi-Ming Sun,
• Abstract summary: We propose a brain-inspired spike state network (Spike-ES) model for aero-engine intelligent fault prediction. Spike-ES is used to effectively capture the evolution process of aero-engine time series data.
• Score: 4.945898510368636
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aero-engine fault prediction aims to accurately predict the development trend of the future state of aero-engines, so as to diagnose faults in advance. Traditional aero-engine parameter prediction methods mainly use the nonlinear mapping relationship of time series data but generally ignore the adequate spatiotemporal features contained in aero-engine data. To this end, we propose a brain-inspired spike echo state network (Spike-ESN) model for aero-engine intelligent fault prediction, which is used to effectively capture the evolution process of aero-engine time series data in the framework of spatiotemporal dynamics. In the proposed approach, we design a spike input layer based on Poisson distribution inspired by the spike neural encoding mechanism of biological neurons, which can extract the useful temporal characteristics in aero-engine sequence data. Then, the temporal characteristics are input into a spike reservoir through the current calculation method of spike accumulation in neurons, which projects the data into a high-dimensional sparse space. In addition, we use the ridge regression method to read out the internal state of the spike reservoir. Finally, the experimental results of aero-engine states prediction demonstrate the superiority and potential of the proposed method.

Related papers

• Generative Spatio-temporal GraphNet for Transonic Wing Pressure Distribution Forecasting [0.0]
  The framework compresses high-dimensional pressure distribution data into a lower-dimensional latent space using an autoencoder. graph-based temporal layers are employed to predict future wing pressures based on past data. The effectiveness of the proposed framework is validated through its application to the Benchmark Super Critical Wing test case.
  arXiv  Detail & Related papers  (2024-11-18T14:10:20Z)
• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling [55.13352174687475]
  This paper proposes a physics-AI hybrid model (i.e., WeatherGFT) which Generalizes weather forecasts to Finer-grained Temporal scales. Specifically, we employ a carefully designed PDE kernel to simulate physical evolution on a small time scale. We introduce a lead time-aware training framework to promote the generalization of the model at different lead times.
  arXiv  Detail & Related papers  (2024-05-22T16:21:02Z)
• GRANP: A Graph Recurrent Attentive Neural Process Model for Vehicle Trajectory Prediction [3.031375888004876]
  We propose a novel model named Graph Recurrent Attentive Neural Process (GRANP) for vehicle trajectory prediction. GRANP contains an encoder with deterministic and latent paths, and a decoder for prediction. We show that GRANP achieves state-of-the-art results and can efficiently quantify uncertainties.
  arXiv  Detail & Related papers  (2024-04-09T05:51:40Z)
• FengWu-4DVar: Coupling the Data-driven Weather Forecasting Model with 4D Variational Assimilation [67.20588721130623]
  We develop an AI-based cyclic weather forecasting system, FengWu-4DVar. FengWu-4DVar can incorporate observational data into the data-driven weather forecasting model. Experiments on the simulated observational dataset demonstrate that FengWu-4DVar is capable of generating reasonable analysis fields.
  arXiv  Detail & Related papers  (2023-12-16T02:07:56Z)
• Machine learning enhanced real-time aerodynamic forces prediction based on sparse pressure sensor inputs [7.112725255953468]
  This paper presents a data-driven aerodynamic force prediction model based on a small number of pressure sensors. The model is tested on numerical and experimental dynamic stall data of a 2D NACA0015 airfoil, and numerical simulation data of dynamic stall of a 3D drone.
  arXiv  Detail & Related papers  (2023-05-16T06:15:13Z)
• Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction [60.60223171143206]
  Trajectory prediction is a crucial undertaking in understanding entity movement or human behavior from observed sequences. Current methods often assume that the observed sequences are complete while ignoring the potential for missing values. This paper presents a unified framework, the Graph-based Conditional Variational Recurrent Neural Network (GC-VRNN), which can perform trajectory imputation and prediction simultaneously.
  arXiv  Detail & Related papers  (2023-03-28T14:27:27Z)
• 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)
• An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions [73.4962254843935]
  We study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history. This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
  arXiv  Detail & Related papers  (2022-04-18T23:42:18Z)
• Spatiotemporal convolutional network for time-series prediction and causal inference [21.895413699349966]
  A neural network computing framework, i.N.N., was developed to efficiently and accurately render a multistep-ahead prediction of a time series. The framework has great potential in practical applications in artificial intelligence (AI) or machine learning fields.
  arXiv  Detail & Related papers  (2021-07-03T06:20:43Z)

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.