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About the Journal
Superintended by：China North Industries Group Corporation Limited
Sponsoredy：Beijing Institute of Technology, Chinese Scoiety for Environmental Sciences, China Occupational Safety and Health Association
Edited & Published by: Editorial Department of Journal of Safety and Environment
Issues per year: 12
ISSN 1009-6094
CN 11-4537/X
Risk analysis of methanol tank area fire accidents based on Bayesian network
Chen Chao;Yuan Boyuan;Wang Haijun;Wang Bo;Zeng Tao;Wang Yujie;Jiang Liqiong;Methanol is an important chemical with both new energy potential and fundamental chemical value, primarily stored in tank farms. Methanol leaks can easily lead to fires, and during combustion, they produce minimal smoke, with gas radiation serving as the primary heat transfer mechanism. This characteristic complicates fire detection and warning, potentially resulting in domino-effect accidents. This study systematically identifies risks in the methanol tank area through fault tree analysis, utilizing vertical storage tanks and pipeline systems as analysis nodes to pinpoint key risk factors. Based on Bayesian network theory, a probability model for tank farm fire accidents was constructed to enable quantitative analysis of accident probabilities. A dynamic Bayesian network method was employed to establish a fire domino effect evolution model, revealing the chain propagation mechanism by which initial accidents trigger secondary incidents. The study identified 56 risk events within the methanol tank farm, including 1 leaf node(T), 21 intermediate nodes(M), and 34 root nodes(X). The calculated probability of a fire accident occurring in the methanol tank area is 2.34×10-6, with the probability of an ignition source being 1.75×10-2 and the probability of a methanol leak at 2.47×10-2. Through Bayesian network sensitivity analysis, the key factors contributing to fire accidents were found to be sparks from vehicle exhaust pipes, illegal smoking mars, and residual fires following hot work. The dynamic Bayesian network model illustrated the domino effect of accidents, simulating the failure process associated with tank area fires. It was observed that the failure probability of each storage tank increased continuously over 1 000 seconds, peaking at 4.22×10-2 from an initial value of 2.34×10-6. The constructed Bayesian network model effectively identifies the key contributing factors of pool fires in methanol tank farms and their potential domino effects, providing a solid foundation for developing fire prevention and control strategies in these facilities.
Application of improved BNT-FRAM model in aircraft departure process safety analysis
Yuan Leping;Wu Xiaoxue;To effectively identify and manage performance changes and propagation within the civil aviation operation system, this paper employs the functional resonance model to analyze the task flow involved in aircraft departure procedures. Utilizing the hierarchical task analysis method, we decompose the task flow based on the organization of the air transport system and the interactions among different entities. A total of 20 functional modules have been identified, including cockpit preparation(F1), ATC clearance(F2), push back request(F3), push back permit(F4), push back(F5), ground traction(F6), startup request(F7), and startup permit(F8), among others. It is important to note that this paper employs blind number theory to estimate the probability distribution of the 20 functional modules concerning changes in time and accuracy. This approach mitigates bias introduced by expert subjectivity. Additionally, this paper abstracts the ability of each functional module to return to a normal state when it confronted with internal and/or external perturbations into a resilience factor. This resilience factor is then used to calculate the variability of each functional module. Finally, the functional coupling value is calculated using the risk coupling formula, enabling the quantification of upstream and downstream functional interactions. This approach identifies key functions and paths that are susceptible to change and formulates risk control measures for managing performance variations. The results indicate that seven functional modules-takeoff request(F14), receipt of aircraft(F15), hand over from tower to approach control(F20), hand over from ramp to tower control(F13), takeoff(F18), instructing aircraft to enter the runway(F16), and takeoff permit(F17)-exhibit greater variability during the aircraft departure process.Ground taxiing(F12)-takeoff request(F14)-takeoff permit(F17)-takeoff(F18)-airbone(F19)-hand over from tower to approach control(F20) and other two key paths are identified. When the system encounters disturbance, these functional modules are more likely to resonate with connected function modules, thereby highlighting potential failure nodes and critical paths within the process is valuable for operation analysis.
Optimizing opportunistic maintenance for offshore wind turbines: addressing uncertainty in life prediction
Li Yanan;Zhang Xinsheng;Wu Chunyang;Zhang Zhenlong;Su Jia;This study introduces a multi-strategy collaborative optimization approach to enhance maintenance decision-making in offshore wind turbines amidst uncertainties in lifetime prediction. In contrast to conventional deterministic strategies, the proposed method integrates uncertainty directly into the optimization framework, ensuring a thorough balance between cost and risk considerations. Initially, a dynamic probabilistic modeling scheme is employed to represent the time-varying nature of prediction errors. By developing a residual life prediction error model grounded in time-dependent probability distributions, this approach effectively captures the evolving stochastic characteristics inherent in degradation forecasts. Second, a cumulative maintenance risk quantification model is developed within the Conditional Value-at-Risk(CVaR) framework. This model addresses the asymmetric impacts of prediction errors on maintenance outcomes and offers a comprehensive measure of risk exposure. Monte Carlo sampling is utilized to generate sets of uncertain scenarios, facilitating robust estimation of potential loss distributions. Leveraging these models, a hierarchical maintenance strategy is formulated that integrates corrective, preventive, and opportunistic maintenance into a cohesive framework. To optimize both maintenance costs and operational risks throughout the turbine's life cycle, a multi-objective maintenance optimization model is established. The Nondominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ) is employed to solve this model, facilitating efficient exploration of the Pareto frontier and enabling coordinated optimization of strategy selection and decision thresholds. Case study results confirm the effectiveness of the proposed approach, demonstrating that the optimized strategies significantly reduce maintenance costs while precisely quantifying and mitigating operational risks. Notably, the integration of probabilistic modeling with scenario-based risk evaluation ensures that maintenance policies remain robust against various sources of uncertainty. In conclusion, the proposed method not only enhances the economic efficiency of offshore wind turbine operations and maintenance but also establishes a rational trade-off between cost and risk. These contributions provide a systematic and practical decision-support framework that can be applied to other complex engineering systems facing similar uncertainty-driven challenges.
Research on radio fingerprint positioning method with extremely sparse reference points in GNSS-denied environments
Chen Naixin;Zhu Chunli;Chen Lei;Gao Zhen;To tackle the significant decline in positioning accuracy caused by sparse reference points in Channel State Information(CSI) fingerprinting, this paper presents a fingerprint augmentation model called L2C-GAN(Location-to-CSI Generative Adversarial Network). The proposed method reconstructs fine-grained radio maps by generating virtual fingerprints based on the learned non-linear mapping between spatial locations and Radio Frequency(RF) features. The methodology consists of three key stages. First, a Density-Based Spatial Clustering of Applications with Noise(DBSCAN) is employed to preprocess the raw CSI amplitude data. This approach adaptively identifies and retains dominant signal clusters while eliminating outliers arising from environmental noise and hardware instability, thereby ensuring the purity and spatial consistency of the training data. Second, to accommodate convolutional operations, high-dimensional CSI matrices are segmented along subcarrier indices and reshaped into single-channel 2D feature maps. Third, the L2C-GAN architecture is constructed using a conditional generation mechanism. To address the low dimensionality of spatial coordinates, a Fourier-feature-based Positional Encoding mechanism is designed to map 2D coordinates into high-dimensional embedding vectors. Both the generator and discriminator integrate Self-Attention modules to capture long-range dependencies among subcarriers, enabling the model to learn global context. Experimental validation is conducted using the OpenCSI dataset in an extremely sparse setting with only 2%(88) real reference points. The results demonstrate that L2C-GAN effectively enhances radio map reconstruction. The average positioning error decreases from 2.01 m to 1.67 m, representing a 16.92% improvement over the baseline without augmentation. Furthermore, experiments with varying parameter settings show that augmenting with 400 virtual reference points yields the optimal positioning accuracy of 1.67 m. Comparative results also indicate that the proposed method significantly outperforms state-of-the-art augmentation techniques, such as SSIM-Aug(Structural Similarity-based Augmentation), AF-DCGAN(Amplitude Feature Deep Convolutional GAN), and LESS(Adaptive Fingerprint-based Localization with Less Site Survey). Notably, the model exhibits robust performance even under limited hardware configurations, achieving a positioning error of 1.92 m with only two receiving antennas, which surpasses the unaugmented four-antenna baseline.
Application of YOLOv5 in intelligent recognition of subgrade diseases based on ground penetrating radar images
Zhou Suhua;Huang Chuting;Sha Linchuan;Huang Minghua;As an advanced non-destructive detection technology and one of the most effective tools available, Ground Penetrating Radar(GPR) has been widely applied in identifying subgrade diseases due to its efficiency and high resolution. However, manual interpretation of GPR images is highly challenging, time-consuming, and susceptible to subjective errors. To address these issues, this study proposes an improved YOLOv5-based method for the automated identification of GPR images depicting subgrade cracks and loosening defects. This method is specifically designed to balance real-time performance with detection accuracy. A carefully constructed dataset comprising 1 290 VOC-format annotated radar images of typical subgrade defects was utilized for model training and validation. Several key architectural enhancements were introduced: the Convolutional Block Attention Module(CBAM) was embedded before the Spatial Pyramid Pooling Fast(SPPF) layer to enhance focus on meaningful features while reducing redundant parameters. Additionally, three C3 modules in the neck of the original YOLOv5 were replaced with Context Guided Blocks(CG Blocks) to strengthen contextual feature perception and improve computational efficiency. Further refinements included a Dynamic Head mechanism to enhance scale-aware representation. Ablation studies demonstrated that the combined improvements from the attention and context-guided modules yielded optimal results. The enhanced C1-CG-YOLOv5 model achieved a mean Average Precision at IoU threshold of 0.5(mAP50) of 90.8% and a mean Average Precision at IoU thresholds of 0.5 to 0.95(mAP50-95) of 64.2%, significantly outperforming the baseline model. Moreover, the model parameters were reduced by 12.8%, indicating superior lightweight performance. Finally, we developed real-time subgrade disease detection software based on the improved model, validating its detection performance, processing efficiency, and practical utility in GPR image analysis. The proposed approach offers an efficient and reliable tool for automated GPR image interpretation, demonstrating strong potential for practical application in infrastructure maintenance and non-destructive evaluation.
