Particle Filter and Its Variants for Degradation State Estimation and Remaining Useful Life Prediction
Bibliographic record
Abstract
State estimation and future state prediction is the main objective of prognostics and health management (PHM) frameworks. The health state can sometimes be either inaccessible or complicated to be measured directly under operating conditions. The degradation process estimation is usually described as a nonlinear or non-Gaussian online tracking problem, and the uncertainty due to multi-source variability makes the estimation challenging. Inference through Particle Filter (PF) is one of the Bayesian paradigms to allow the estimation possible. The motivation is to compute the posterior of a Markov process’s hidden health state with noisy and partial observations. Particle Filter is a sequential Monte Carlo method. It uses particle representation to recursively update the posterior of a stochastic process when new observations become available. However, the generic algorithm, also known as Sequential Importance Sampling with Resampling (SISR), suffers the well-known drawbacks of particle degeneration and impoverishment. With rising expectations, Regularized Particle Filter (RPF) and Auxiliary Particle Filter (APF) are proposed to alleviate the problems. In a recent development, Regularized Auxiliary Particle Filter (RAPF) reportedly performs better than the other variants. In this work, we reviewed and assessed the framework and performance of each PF variant using the benchmark model and a case study of lithium batteries to estimate the health state and remaining useful life. The results reflect the previous research work, indicating that RAPF gives the best estimation among all methods in both experiments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".