Quantitative Risk Assessment for Autonomous Vehicles: Integrating Functional Resonance Analysis Method and Bayesian Network DOI
Chengwen Deng, Yufeng Li, Qi Liu

и другие.

Quality and Reliability Engineering International, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 24, 2024

ABSTRACT In autonomous vehicles (AVs), intricate functional‐level couplings exist among the components. Accidents can occur even when all functions are operating normally, as subtle performance variabilities in these aggregate through couplings, leading to functional resonance. The aim of this study is identify, analyze and quantitatively assess safety issues caused by complex interactions AVs propose appropriate risk management strategies improve vehicle safety. Commonly used modern methods assessment, such system‐theoretical process analysis accident mapping, struggle capture resonance lack quantitative analysis. To end, paper proposes a assessment method that integrates (FRAM) with Bayesian network (BN) reveal quantify risks within AVs. Initially, FRAM model constructed characterize function system, which subsequently aggregated into chains identify potential hazards. Then, develop BN for system risk. A case an automatic emergency braking (AEB) on open‐source conducted verify its effectiveness. results demonstrate proposed approach not only identifies but also effectively quantifies AEB system.

Язык: Английский

Failure Mode and Effects Analysis Method on the Air System of an Aircraft Turbofan Engine in Multi-Criteria Open Group Decision-Making Environment DOI
Yongchuan Tang,

Zhuoxin Fei,

Lei Huang

и другие.

Cybernetics & Systems, Год журнала: 2025, Номер unknown, С. 1 - 32

Опубликована: Фев. 25, 2025

Язык: Английский

Процитировано

2
Quantifying risk of service failure in customer complaints: A textual analysis-based approach DOI
Wenyan Song, Rong Wan,

Yuqi Tang

и другие.

Advanced Engineering Informatics, Год журнала: 2024, Номер 60, С. 102377 - 102377

Опубликована: Фев. 1, 2024

Язык: Английский

Процитировано

8
Integrating fuzzy logic and multi‐criteria decision‐making in a hybrid FMECA for robust risk prioritization DOI
Ammar Chakhrit, Imene Djelamda, Mohammed Bougofa

и другие.

Quality and Reliability Engineering International, Год журнала: 2024, Номер 40(6), С. 3555 - 3580

Опубликована: Май 30, 2024

Abstract Failure mode effects and criticality analysis (FMECA) is widely employed across industries to recognize reduce possible failures. Despite its extensive usage, FMECA encounters challenges in decision‐making. In this paper, a new fuzzy resilience‐based RPN model created develop the method. The transcends limitations associated with traditional risk priority number calculations by incorporating factors beyond frequency, severity, detection. This extension includes considerations impacting system cost, sustainability, safety, providing more comprehensive assessment. addition, create trust decision‐makers, robust assessment approach suggested, integrating three methodologies. initial phase, analytical hierarchy process grey relation method are used determine subjective weights of different resolve flaws deficiency constructed inference rules. second an entropy applied handle uncertainty individual weightage calculated capture conflicting experts' views. suggested validated through case study involving gas turbine. results demonstrate significant differences failure prioritization between approaches. introduction MTTR addresses critical shortcomings FMECA, enhancing predictive capabilities. Furthermore, hybrid improved ranking, classifying modes into fifteen categories, aiding decision‐making, applying appropriate mitigation measures. Overall, findings validate efficacy proposed addressing uncertainties divergent expert judgments for complex systems.

Язык: Английский

Процитировано

6
Adaptive continuous barrier function-based super-twisting global sliding mode stabilizer for chaotic supply chain systems DOI
Mohammadreza Askari Sepestanaki,

Hamidreza Rezaee,

Mohammad Soofi

и другие.

Chaos Solitons & Fractals, Год журнала: 2024, Номер 182, С. 114828 - 114828

Опубликована: Апрель 18, 2024

Язык: Английский

Процитировано

5
An FMEA Risk Assessment Method Based on Social Networks Considering Expert Clustering and Risk Attitudes DOI
Пэйдэ Лю, Yiqiao Xu, Ying Li

и другие.

IEEE Transactions on Engineering Management, Год журнала: 2024, Номер 71, С. 10783 - 10796

Опубликована: Янв. 1, 2024

Failure mode and effect analysis (FMEA) method has been widely utilized to solve the problem of risk assessment in all walks life. An FMEA decision support model considering expert clustering attitude is constructed. First, information processed cloud environment. The behavior experts simulated based on trust relationship, opinion similarity similarity. Second, consensus opinions are formed through evolution, group weight determination constructed size level. Finally, a linear programming minimizing individual regret used factor problem. Combined with theory TODIM finite rationality, priority determined. novel approach applied address reliability management smart bracelets. Sensitivity comparative analyses demonstrated effectiveness superiority this enrich theoretical research approach.

Язык: Английский

Процитировано

3
An integrated space polyhedral grid grey relational analysis model based on panel interval grey number for seawater quality assessment DOI
Xuemei Li, Zhichao Chen, Yufeng Zhao

и другие.

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 127363 - 127363

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
An effective approach for fault diagnosis: Conflict management and BBA generation DOI Creative Commons

Yuhao Qin,

Zhike Qiu,

Zichong Chen

и другие.

PLoS ONE, Год журнала: 2025, Номер 20(6), С. e0324603 - e0324603

Опубликована: Июнь 5, 2025

Evidence Theory (ET) is widely applied to handle uncertainty issues in fault diagnosis. However, when dealing with highly conflicting evidence, the use of Dempster’s rule may result outcomes that contradict reality. To address this issue, paper proposes a diagnosis decision-making method. The method primarily divided into two parts. First, similarity measurement introduced solve conflict management problem. This combines belief and plausibility functions within ET. It not only considers numerical between pieces evidence but also takes account directional similarity, better capturing differences different evidence. effectiveness validated through several complex examples. Next, based on method, we propose which comparative experiments. Then, considering inherent real-world sensor data, basic assignment (BBA) generation Student’s t-distribution fuzzy membership functions. Finally, by combining proposed BBA derive final decision, its demonstrated an application.

Язык: Английский

Процитировано

0
Robust Rail-Track Section Identification With Multiple Structured Light Sensors and Kernel-Based Belief Sensor-Credibility Evaluation DOI
Jiaxu Zhang, Shengchun Wang,

Kunzhen Liu

и другие.

IEEE Sensors Journal, Год журнала: 2024, Номер 24(8), С. 13217 - 13226

Опубликована: Март 7, 2024

Current single-structured light sensor-based rail-track section identification lacks robustness against unstable signal transmission. The decision-level sensor fusion based on evidence theory is fragile to conflict. Aiming at the listed challenges, this study proposes a robust scheme that combines multistructured sensors and new evidence-theoretic strength of kernel method. In scheme, multiple are involved tackle lack robustness, kernel-induced belief metric (KIBM), which first connects reproducing Hilbert space (RKHS) representation distance-dominated credibility evaluation, newly constructed address conflict in fusion. addition, gap between filled. Experiments reveal efficiency involving multisensors sections.

Язык: Английский

Процитировано

2
A novel multi-criteria conflict evidence combination method and its application to pattern recognition DOI
Yilin Dong,

Ningning Jiang,

Ri‐Gui Zhou

и другие.

Information Fusion, Год журнала: 2024, Номер 108, С. 102346 - 102346

Опубликована: Март 12, 2024

Язык: Английский

Процитировано

2
Advancements in Gas Turbine Fault Detection: A Machine Learning Approach Based on the Temporal Convolutional Network–Autoencoder Model DOI Creative Commons
Al-Tekreeti Watban Khalid Fahmi, Kazem Reza Kashyzadeh, Siamak Ghorbani

и другие.

Applied Sciences, Год журнала: 2024, Номер 14(11), С. 4551 - 4551

Опубликована: Май 25, 2024

To tackle the complex challenges inherent in gas turbine fault diagnosis, this study uses powerful machine learning (ML) tools. For purpose, an advanced Temporal Convolutional Network (TCN)–Autoencoder model was presented to detect anomalies vibration data. By synergizing TCN capabilities and Multi-Head Attention (MHA) mechanisms, introduces a new approach that performs anomaly detection with high accuracy. train test proposed model, bespoke dataset of CA 202 accelerometers installed Kirkuk power plant used. The not only outperforms traditional GRU–Autoencoder, LSTM–Autoencoder, VAE models terms accuracy, but also shows Mean Squared Error (MSE = 1.447), Root (RMSE 1.193), Absolute (MAE 0.712). These results confirm effectiveness TCN–Autoencoder increasing predictive maintenance operational efficiency plants.

Язык: Английский

Процитировано

2