自主智能系统(英文)最新文献

This research addresses the diagnosis of broken rotor bar faults in three-phase induction motors, focusing on steady-state conditions under different load levels and fault severity. Although numerous techniques exist, there is still a significant gap in comprehensive comparative evaluations that rigorously assess the interaction between signal processing, feature selection, and pattern classifiers, particularly concerning their robustness to noise and multiple performance criteria. An experimental investigation was carried out with electrical current and mechanical vibration signals, several signal preprocessing techniques, two feature selection strategies, Correlation-Based Feature Selection (CFS) and Wrapper, and a wide range of pattern classifiers, Decision Tree (DT), Naive Bayes (NB), Artificial Neural Network (ANN), and Support Vector Machine (SVM). The performance of the configurations was quantified by a multicriteria indicator, complemented by a dedicated robustness assessment by introducing white noise into the input signals. The most significant results reveal that vibration signals exhibit superior diagnostic robustness compared to electrical current signals, especially under noisy conditions. Furthermore, Wrapper-based feature selection consistently outperforms CFS, and configurations combining Wrapper with DT or NB classifiers emerge as the most suitable for detecting and diagnosing broken bars. Furthermore, the Wrapper-DT configuration efficiently classified defects even with the inclusion of 40% noise. This work provides data-driven insights into robust configurations for broken bar diagnosis, guiding the development of more reliable predictive maintenance systems, emphasizing signal modality, robust feature selection, and real-time applications.

Most machine learning-based remaining useful life (RUL) prediction methods only yield point predictions, and their “black-box” nature results in low interpretability. Stochastic process-based modeling can predict RUL probability density function (PDF), yet it often suffers from inaccurate modeling and failure to fully utilize historical degradation data of the same equipment type. To overcome these limitations, this paper integrates the two approaches and proposes an Attention-Gaussian-LSTM-Wiener (AG-LSTM-Wiener)-based RUL prediction method, enabling dynamic weighted fusion of predicted PDFs. An AG-LSTM-Wiener model with a two-branch structure is constructed. Health indicator (HI) is fed into the corresponding branch models to generate two different PDF curves. Decision blocks are employed to estimate RUL, from which weights are derived to achieve dynamic weighted fusion of the PDFs. Experiments on the CMPASS turbofan engine degradation dataset validate the proposed method’s effectiveness. Results demonstrate that the proposed method not only prevents PDF curve distortion but also improves the prediction accuracy compared with other methods. With the root mean squared error (RMSE) and Score reduced by 32.8% and 46.1% on average, and the mean squared error of PDF ((mathrm{MSE}_{mathrm{PDF}} )) improved by 99.3% compared to AG-LSTM, which exhibits the best performance among the contrast methods.

As autonomous driving technology advances from assisted to higher levels of autonomy, the complexity of operational environments and the uncertainty of driving tasks continue to increase, posing significant challenges to system safety. The key to ensuring safety lies in conducting comprehensive and rational risk assessments to identify potential hazards and inform policy optimization. Consequently, risk assessment has emerged as a critical component for ensuring the safe operation of higher-level autonomous driving systems. This review focuses on research into risk assessment for autonomous driving. It systematically surveys the state-of-the-art literature from three key perspectives: risk sources, assessment methodologies, data foundations, and system architectures. For each perspective, the paper provides an in-depth analysis of representative technical approaches, modeling principles, and typical application scenarios, while summarizing their research characteristics and applicable boundaries. Finally, this paper synthesizes the three fundamental challenges that persist in current research and further explores future directions and development opportunities. It provides a theoretical foundation and methodological references for the development of autonomous driving systems that exhibit high safety and reliability.

The rapid advancement of Large Language Models (LLMs) has created unprecedented opportunities for industrial automation, process optimization, and decision support systems. As industries seek to leverage LLMs for industrial tasks, understanding their architecture, deployment strategies, and fine-tuning methods becomes critical. In this review, we aim to summarize the challenges, key technologies, current status, and future directions of LLM in Prognostics and Health Management(PHM). First, this review introduces deep learning for PHM. We begin by analyzing the architectural considerations and deployment strategies for industrial environments, including acceleration techniques and quantization methods that enable efficient operation on resource-constrained industrial hardware. Second, we investigate Parameter Efficient Fine-Tuning (PEFT) techniques that allow industry-specific adaptation without prohibitive computational costs. Multi-modal capabilities extending LLMs beyond text to process sensor data, images, and time-series information are also discussed. Finally, we explore emerging PHM including anomaly detection systems that identify equipment malfunctions, fault diagnosis frameworks that determine root causes, and specialized question-answering systems that empower workers with instant domain expertise. We conclude by identifying key challenges and future research directions for LLM deployment in PHM. This review provides a timely resource for researchers, engineers, and decision-makers navigating the transformative potential of language models in industry 4.0 environments.

In many industrial settings, fleets of assets are required to operate through alternating missions and breaks. Fleet Selective Maintenance (FSM) is widely used in such contexts to improve the fleet performance. However, existing FSM models assume that upcoming missions are identical and require only a single system configuration for completion. Additionally, these models typically assume that all missions must be completed, overlooking resource constraints that may prevent readying all systems within the available break duration. This makes mission prioritization and assignment a necessary consideration for the decision-maker. This work proposes a novel FSM model that jointly optimizes system to mission assignment, component and maintenance level selection, and repair task allocation. The proposed framework integrates analytical models for standard components and Deep Neural Networks (DNNs) for sensor-monitored ones, enabling a hybrid reliability assessment approach that better reflects real-world multi-component systems. To account for uncertainties in maintenance and break durations, a chance-constrained optimization model is developed to ensure that maintenance is completed within the available break duration with a specified confidence level. The optimization model is reformulated using two well-known techniques: Sample Average Approximation (SAA) and Conditional Value-at-Risk (CVaR) approximation. A case study of military aircraft fleet maintenance is investigated to demonstrate the accuracy and added value of the proposed approach.