2025· Neural Information Processing Systems· 1 citation· 43 references
Computer Science
TL;DR
This work proposes a self-supervised masking scheme that simulates common sensor failures and explicitly trains the model to recover the original signal, and demonstrates that the resulting representations significantly improve the robustness of predictions to seen and unseen sensor failures on a vehicle dynamics dataset.
Abstract
Machine learning models play a key role in safety-critical applications, such as autonomous vehicles and advanced driver assistance systems, where their robustness during inference is essential to ensure reliable operation. Sensor faults, however, can corrupt input signals, potentially leading to severe model failures that compromise reliability. In this context, pretraining emerges as a powerful approach for learning expressive representations applicable to various downstream tasks. Among existing techniques, masking represents a promising direction for learning representations that are robust to corrupted input data. In this work, we extend this concept by specifically targeting robustness to sensor outages during pretraining. We propose a self-supervised masking scheme that simulates common sensor failures and explicitly trains the model to recover the original signal. We demonstrate that the resulting representations significantly improve the robustness of predictions to seen and unseen sensor failures on a vehicle dynamics dataset, maintaining strong downstream performance under both nominal and various fault conditions. As a practical application, we deploy the method on a modified Lexus LC 500 and show that the pretrained model successfully operates as a substitute for a physical sensor in a closed-loop control system. In this autonomous racing application, a supervised baseline trained without sensor failures may cause the vehicle to leave the track. In contrast, a model trained using the proposed masking scheme enables reliable racing performance in the presence of sensor failures.
As industrial robots become increasingly essential to modern manufacturing and automation systems, ensuring their durability and operational integrity has emerged as a key concern. Traditional defect detection methods typically depend on labeled datasets and supervised learning techniques, which can be difficult and impractical to implement in real-world industries. In contrast, unsupervised learning presents a compelling alternative by facilitating anomaly detection and fault diagnosis without the need for labeled data. This article offers a thorough analysis of unsupervised learning techniques used in the health monitoring of industrial robots. We explore significant trends and key algorithms, such as clustering, autoencoders, and generative models, assessing their effectiveness in identifying faults and performance degradation. The research addresses the unique challenges associated with high-dimensional sensor data, variable operating conditions, and the lack of ground truth labels. Additionally, we highlight unresolved research questions and potential future directions, emphasizing the need for scalable, interpretable, and real-time solutions. This survey serves as a foundational reference for researchers and practitioners aiming to develop resilient and autonomous health monitoring systems for industrial robots.
Muhammad Umar Elahi, Rana Talal Ahmad Khan, Muhammad Haris Yazdani et al.· Mathematics· 0 citations
While conventional fault diagnosis excels in steady-state scenarios, reliable detection under variable operating conditions remains a critical challenge in modern industrial drive systems. Although domain adaptation techniques address cross-domain distribution shifts, their deployment is often hindered by data privacy concerns and the impracticality of labeling massive target data in real-world factories. Furthermore, standard methods typically require concurrent access to source and target data, which violates the data isolation protocols common in industrial scenarios. To overcome these barriers, this paper proposes a novel and robust Source-Free Domain Adaptation (SFDA) framework equipped with post-hoc explainability for bearing fault diagnosis. First, to combat complex environmental interference and uneven sample distributions, we introduce a Gaussian noise-based stochastic perturbation strategy, forcing the model to learn robust decision boundaries. Second, high-confidence pseudo-labels are mined as reliable supervision signals. To fundamentally optimize the feature manifold, we introduce a contrastive learning-based representation strategy. Specifically, a mixed loss combining Supervised Contrastive loss and dot product loss is proposed to explicitly enhance feature discriminability while preventing feature collapse, achieving a delicate balance between intra-class compactness and overall diversity. Finally, a post-hoc visualization mechanism is incorporated to explain the model's decision-making process, thereby enhancing the transparency and credibility of the diagnosis for practical industrial deployment. Extensive experiments on two publicly available bearing fault datasets demonstrate that the proposed method significantly outperforms existing SFDA-based baselines. On the PU dataset, our approach achieves an average diagnostic accuracy of 99.11%, yielding a performance gain of 1.86% over the state-of-the-art SDALR method. Similarly, on the JNU dataset, the proposed framework attains an average accuracy of 98.36%, demonstrating superior robustness and trustworthiness in real-world fault diagnosis.
chenghao yan, Dongsheng Liu, Tong Wu et al.· Measurement science and tech...· 0 citations
Deep Reinforcement Learning (DRL) agents have been widely adopted across diverse domains to address challenging decision-making problems, such as autonomous driving and robotic control. Given that many of these applications are safety- and security-critical, rigorous testing of DRL agents is indispensable. Existing testing methods are typically guided by reward signals to detect failures. However, for well-trained agents, whose performance approaches optimal levels in standard operating conditions, reward signals remain generally high, making current methods ineffective at uncovering critical failures. To address these challenges, we propose a novel failure-based method that leverages task-induced failure insights to enhance failure detection capability while reducing the number of tests required. Since DRL agents are inherently designed with human-defined tasks, they provide valuable cues about task difficulty. Intuitively, a DRL agent is more likely to fail when confronted with a more difficult task; therefore, PRT prioritizes these tasks. Building on this foundation, we propose Prior Random Testing, a black-box failure-based testing method that enables targeted prioritization while preserving the diversity of generated test cases. Guided by task-induced failure insights, PRT prioritizes failure-prone regions of the input domain, thereby facilitating efficient failure detection. PRT is evaluated on four widely used benchmarks and compared with different state-of-the-art methods including fuzzing, search-based and generative-based methods. PRT ranks among the top performers in terms of both the cost of finding the first failure and the diversity of test cases. Notably, compared to random testing, PRT achieves better diversity and reduces the testing cost by over 50%.
Weibin Lin, Jiangtao Meng, Zheng Zheng· Proceedings of the ACM on So...· 0 citations
To maintain high efficiency and reduce operational downtime in industrial manufacturing, effective Predictive Maintenance (PdM) for robotic manipulators is essential. Although combining Model-Agnostic Meta-Learning (MAML) with digital twin technology offers a solid basis for quickly identifying faults, conventional methods often face challenges regarding parameter sensitivity and generalizing to new domains. To mitigate these issues, we introduce an ensemble-based metalearning framework that combines MAML with majority voting and operational grouping. This methodology improves generalization, stabilizes performance across diverse conditions, and strengthens few-shot learning capabilities. We validated the framework using a synthetic vibration dataset generated via a digital twin to simulate various robotic arm faults. Our findings demonstrate that this method achieves 93.8% accuracy and 93.1% precision in the ten-shot regime, outperforming the MAML baseline by 11.1%, across a broad range of mechanical faults, showing strength in cross-domain few-shot (CDFS) scenarios. Comparisons with established frameworks - including Reptile, Protonet, and ANIL, confirm the effectiveness of our model. By employing ensemble learning, we attain greater robustness and classification accuracy, establishing the method as a viable solution for industrial PdM. Furthermore, the integration of digital twins bridges the gap between simulation and real-world deployment, reducing data dependency and enabling effective fault classification even in dynamic environments with limited labeled data.
Mainak Mallick, Seung-Kyum Choi· 2026 6th International Confe...· 0 citations
Deep Reinforcement Learning (DRL) has achieved significant success in robotics and autonomous systems, yet remains vulnerable to adversarial perturbations that can severely degrade performance. Research in adversarial reinforcement learning is often limited by fragmented implementations, inconsistent evaluation protocols, and poor reproducibility. To address these challenges, we present \textbf{RoAd-RL}, an open-source benchmarking framework that provides unified abstractions for policies, attacks, defenses, and robustness metrics, together with reproducible evaluation pipelines and seamless integration with Stable-Baselines3 and Gymnasium. We evaluate DQN, PPO, and SAC agents in LunarLander and Highway-v0 under 192 attack-defense configurations. Results reveal substantial variations in robustness across environments and show that some commonly used defenses can be more detrimental than the attacks they aim to mitigate, while temporal smoothing consistently achieves strong performance. RoAd-RL establishes a standardized benchmark for adversarial reinforcement learning research and is publicly available at https://pypi.org/project/road-rl.
Adithya Mohan, Daniel Kriegl, Torsten Schon· arXiv.org· 0 citations
Unexpected breakdowns in rotating machinery can result in substantial economic losses and safety hazards, underscoring the critical need for reliable bearing fault diagnosis under variable operating conditions. Although deep learning has demonstrated a strong diagnostic capability, its practical deployment is often hindered by domain shifts caused by variations in speed or load, along with the difficulty of obtaining labeled data for all possible operating conditions. To address this challenge, a physics-informed single-source domain generalization (PI-SSDG) framework is proposed that learns from vibration signals collected from a single-source domain corresponding to a specific operating condition and effectively generalizes to unseen conditions without requiring target-domain data. The proposed method introduces a log-mean-removed, low-pass liftered cepstrum that explicitly suppresses global amplitude scaling induced by operating conditions and excitation-related periodicities while preserving fault-discriminative transfer-function-related features. In addition, a dual-branch architecture is designed that jointly exploits the preprocessed cepstrum and raw vibration signals, together with a joint training strategy using branch-specific losses that promotes complementary feature representations and mitigates residual domain variability. Extensive experiments conducted on three public bearing datasets demonstrate that the proposed method consistently achieves superior cross-domain accuracy under speed and load variations and outperforms recent domain-generalization baselines. Comprehensive ablation studies and feature visualization further confirm the effectiveness of the proposed components in improving diagnostic robustness.
Suhyun Kim, Taehyoun Kim· IEEE Access· 0 citations