A Cloud-based Real-time Probabilistic Remaining Useful Life (RUL) Estimation using the Sequential Monte Carlo (SMC) Method
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arXiv
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913589907947520 |
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| author | Lyathakula, Karthik Reddy Yuan, Fuh-Gwo |
| author_facet | Lyathakula, Karthik Reddy Yuan, Fuh-Gwo |
| contents | The remaining useful life (RUL) estimation is an important metric that helps in condition-based maintenance. Damage data obtained from the diagnostics techniques are often noisy and the RUL estimated from the data is less reliable. Estimating the probabilistic RUL by quantifying the uncertainty in the predictive model parameters using the noisy data increases confidence in the predicted values. Uncertainty quantification methods generate statistical samples for the model parameters, that represent the uncertainty, by evaluating the predictive model several times. The computational time for solving a physics-based predictive model is significant, which makes the statistical techniques to be computationally expensive. It is essential to reduce the computational time to estimate the RUL in a feasible time. In this work, real-time probabilistic RUL estimation is demonstrated in adhesively bonded joints using the Sequential Monte Carlo (SMC) sampling method and cloud-based computations. The SMC sampling method is an alternative to traditional MCMC methods, which enables generating the statistical parameter samples in parallel. The parallel computational capabilities of the SMC methods are exploited by running the SMC simulation on multiple cloud calls. This approach is demonstrated by estimating fatigue RUL in the adhesively bonded joint. The accuracy of probabilistic RUL estimated by SMC is validated by comparing it with RUL estimated by the MCMC and the experimental values. The SMC simulation is run on the cloud and the computational speedup of the SMC is demonstrated. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_17824 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Cloud-based Real-time Probabilistic Remaining Useful Life (RUL) Estimation using the Sequential Monte Carlo (SMC) Method Lyathakula, Karthik Reddy Yuan, Fuh-Gwo Computational Engineering, Finance, and Science Distributed, Parallel, and Cluster Computing The remaining useful life (RUL) estimation is an important metric that helps in condition-based maintenance. Damage data obtained from the diagnostics techniques are often noisy and the RUL estimated from the data is less reliable. Estimating the probabilistic RUL by quantifying the uncertainty in the predictive model parameters using the noisy data increases confidence in the predicted values. Uncertainty quantification methods generate statistical samples for the model parameters, that represent the uncertainty, by evaluating the predictive model several times. The computational time for solving a physics-based predictive model is significant, which makes the statistical techniques to be computationally expensive. It is essential to reduce the computational time to estimate the RUL in a feasible time. In this work, real-time probabilistic RUL estimation is demonstrated in adhesively bonded joints using the Sequential Monte Carlo (SMC) sampling method and cloud-based computations. The SMC sampling method is an alternative to traditional MCMC methods, which enables generating the statistical parameter samples in parallel. The parallel computational capabilities of the SMC methods are exploited by running the SMC simulation on multiple cloud calls. This approach is demonstrated by estimating fatigue RUL in the adhesively bonded joint. The accuracy of probabilistic RUL estimated by SMC is validated by comparing it with RUL estimated by the MCMC and the experimental values. The SMC simulation is run on the cloud and the computational speedup of the SMC is demonstrated. |
| title | A Cloud-based Real-time Probabilistic Remaining Useful Life (RUL) Estimation using the Sequential Monte Carlo (SMC) Method |
| topic | Computational Engineering, Finance, and Science Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2411.17824 |