SARNet: A Spike-Aware consecutive validation Framework for Accurate Remaining Useful Life Prediction

• URL: http://arxiv.org/abs/2510.22955v1
• Date: Mon, 27 Oct 2025 03:23:11 GMT
• Title: SARNet: A Spike-Aware consecutive validation Framework for Accurate Remaining Useful Life Prediction
• Authors: Junhao Fan, Wenrui Liang, Wei-Qiang Zhang,
• Abstract summary: We introduce SARNet (Spike-Aware Consecutive Validation Framework), which builds on a Modern Temporal Convolutional Network (ModernTCN)<n>ModernTCN forecasts degradation-sensitive indicators; an adaptive consecutive threshold validates true spikes while suppressing noise.<n>Across benchmark-ported datasets under an event-triggered protocol, SARNet consistently lowers error compared to recent baselines.
• Score: 7.0741499054562995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate prediction of remaining useful life (RUL) is essential to enhance system reliability and reduce maintenance risk. Yet many strong contemporary models are fragile around fault onset and opaque to engineers: short, high-energy spikes are smoothed away or misread, fixed thresholds blunt sensitivity, and physics-based explanations are scarce. To remedy this, we introduce SARNet (Spike-Aware Consecutive Validation Framework), which builds on a Modern Temporal Convolutional Network (ModernTCN) and adds spike-aware detection to provide physics-informed interpretability. ModernTCN forecasts degradation-sensitive indicators; an adaptive consecutive threshold validates true spikes while suppressing noise. Failure-prone segments then receive targeted feature engineering (spectral slopes, statistical derivatives, energy ratios), and the final RUL is produced by a stacked RF--LGBM regressor. Across benchmark-ported datasets under an event-triggered protocol, SARNet consistently lowers error compared to recent baselines (RMSE 0.0365, MAE 0.0204) while remaining lightweight, robust, and easy to deploy.

Related papers

• When Sensors Fail: Temporal Sequence Models for Robust PPO under Sensor Drift [64.37959940809633]
  We study robustness of Proximal Policy Optimization (PPO) under temporally persistent sensor failures.<n>We show Transformer-based sequence policies substantially outperform, RNN, and SSMs in robustness, maintaining high returns even when large fractions of sensors are unavailable.
  arXiv  Detail & Related papers  (2026-03-04T22:21:54Z)
• Physics-Guided Tiny-Mamba Transformer for Reliability-Aware Early Fault Warning [2.491980790083337]
  We propose the Physics-Guided Tiny-Mamba Transformer (PG-TMT), a compact tri-branch encoder tailored for online condition monitoring.<n>A depthwise-separable convolutional stem captures micro-transients, a Tiny-Mamba state-space branch models near-linear long-range dynamics, and a lightweight local Transformer encodes cross-channel resonances.<n>PG-TMT delivers, interpretable, and deployment-ready early warnings for reliability-centric prognostics and health management.
  arXiv  Detail & Related papers  (2026-01-29T05:46:12Z)
• Lightweight Test-Time Adaptation for EMG-Based Gesture Recognition [2.414036142474149]
  We propose a lightweight framework for Test-Time Adaptation (TTA) using a Temporal Convolutional Network (TCN) backbone.<n>We introduce three deployment-ready strategies: causal adaptive batch normalization for real-time statistical alignment; (ii) a Gaussian Mixture Model (GMM) alignment with experience replay to prevent forgetting; and (iii) meta-learning for rapid, few-shot calibration.<n>Our results show that experience-replay updates yield superior stability under limited data, while meta-learning achieves competitive performance in one- and two-shot regimes.
  arXiv  Detail & Related papers  (2026-01-07T18:48:31Z)
• ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving [64.42138266293202]
  ResAD is a Normalized Residual Trajectory Modeling framework.<n>It reframes the learning task to predict the residual deviation from an inertial reference.<n>On the NAVSIM benchmark, ResAD achieves a state-of-the-art PDMS of 88.6 using a vanilla diffusion policy.
  arXiv  Detail & Related papers  (2025-10-09T17:59:36Z)
• Revisiting Multivariate Time Series Forecasting with Missing Values [74.56971641937771]
  Missing values are common in real-world time series.<n>Current approaches have developed an imputation-then-prediction framework that uses imputation modules to fill in missing values, followed by forecasting on the imputed data.<n>This framework overlooks a critical issue: there is no ground truth for the missing values, making the imputation process susceptible to errors that can degrade prediction accuracy.<n>We introduce Consistency-Regularized Information Bottleneck (CRIB), a novel framework built on the Information Bottleneck principle.
  arXiv  Detail & Related papers  (2025-09-27T20:57:48Z)
• A Spatio-Temporal Graph Neural Networks Approach for Predicting Silent Data Corruption inducing Circuit-Level Faults [5.2974276480448195]
  Functional testing SDE-related faults is expensive to simulate.<n>We present a unified-temporal graph convolutional network (ST-GCN) for fast, accurate prediction of long-cycle fault impact probabilities.
  arXiv  Detail & Related papers  (2025-09-08T02:23:51Z)
• Error-quantified Conformal Inference for Time Series [55.11926160774831]
  Uncertainty quantification in time series prediction is challenging due to the temporal dependence and distribution shift on sequential data.<n>We propose itError-quantified Conformal Inference (ECI) by smoothing the quantile loss function.<n>ECI can achieve valid miscoverage control and output tighter prediction sets than other baselines.
  arXiv  Detail & Related papers  (2025-02-02T15:02:36Z)
• Constrained Reinforcement Learning using Distributional Representation for Trustworthy Quadrotor UAV Tracking Control [2.325021848829375]
  We propose a novel trajectory tracker integrating a Distributional Reinforcement Learning disturbance estimator for unknown aerodynamic effects. The proposed estimator Constrained Distributional Reinforced disturbance estimator' (ConsDRED) accurately identifies uncertainties between true and estimated values of aerodynamic effects. We demonstrate our system improves accumulative tracking errors by at least 70% compared with the recent art.
  arXiv  Detail & Related papers  (2023-02-22T23:15:56Z)
• Ranking-Based Physics-Informed Line Failure Detection in Power Grids [66.0797334582536]
  Real-time and accurate detecting of potential line failures is the first step to mitigating the extreme weather impact and activating emergency controls. Power balance equations nonlinearity, increased uncertainty in generation during extreme events, and lack of grid observability compromise the efficiency of traditional data-driven failure detection methods. This paper proposes a Physics-InformEd Line failure Detector (FIELD) that leverages grid topology information to reduce sample and time complexities and improve localization accuracy.
  arXiv  Detail & Related papers  (2022-08-31T18:19:25Z)
• GNN4REL: Graph Neural Networks for Predicting Circuit Reliability Degradation [7.650966670809372]
  We employ graph neural networks (GNNs) to accurately estimate the impact of process variations and device aging on the delay of any path within a circuit. GNN4REL is trained on a FinFET technology model that is calibrated against industrial 14nm measurement data. We successfully estimate delay degradations of all paths -- notably within seconds -- with a mean absolute error down to 0.01 percentage points.
  arXiv  Detail & Related papers  (2022-08-04T20:09:12Z)
• Evaluating Short-Term Forecasting of Multiple Time Series in IoT Environments [67.24598072875744]
  Internet of Things (IoT) environments are monitored via a large number of IoT enabled sensing devices. To alleviate this issue, sensors are often configured to operate at relatively low sampling frequencies. This can hamper dramatically subsequent decision-making, such as forecasting.
  arXiv  Detail & Related papers  (2022-06-15T19:46:59Z)
• Certified Adversarial Defenses Meet Out-of-Distribution Corruptions: Benchmarking Robustness and Simple Baselines [65.0803400763215]
  This work critically examines how adversarial robustness guarantees change when state-of-the-art certifiably robust models encounter out-of-distribution data. We propose a novel data augmentation scheme, FourierMix, that produces augmentations to improve the spectral coverage of the training data. We find that FourierMix augmentations help eliminate the spectral bias of certifiably robust models enabling them to achieve significantly better robustness guarantees on a range of OOD benchmarks.
  arXiv  Detail & Related papers  (2021-12-01T17:11:22Z)

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.