Summary of Physics Informed Machine Learning (piml) Methods For Estimating the Remaining Useful Lifetime (rul) Of Aircraft Engines, by Sriram Nagaraj and Truman Hickok
Physics Informed Machine Learning (PIML) methods for estimating the remaining useful lifetime (RUL) of aircraft engines
by Sriram Nagaraj, Truman Hickok
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Numerical Analysis (math.NA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to predict the remaining useful lifetime (RUL) of aircraft engines using Physics-Informed Machine Learning (PIML). The method leverages the NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset, which comprises sensor outputs from various operating modes. To tackle this problem, the authors first employ stochastic methods to estimate physical laws governing the noisy time-series data. They model sensor readings as governed by stochastic differential equations and estimate transition density mean and variance functions of underlying processes. The learned mean and variance functions are then incorporated into Long-Short Term Memory (LSTM) models for training and inference. The PIML-based approach outperforms previous data-only deep learning methods, demonstrating its potential for RUL prediction in this context. Furthermore, the framework can be adapted to other scenarios, including partially observed or known underlying physics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses a new way of combining machine learning and physics to predict how long aircraft engines will last. The method is tested on data from NASA about different engine operating modes. First, the authors use statistical methods to figure out what physical laws are governing the noisy sensor data. They then create models that take into account these physical laws and train them using a type of recurrent neural network called LSTM. The results show that this approach works better than previous methods that only used machine learning. This method could be useful for predicting engine lifespan in different situations. |
Keywords
* Artificial intelligence * Deep learning * Inference * Lstm * Machine learning * Neural network * Time series
