Pub Date : 2024-10-24DOI: 10.1016/j.ress.2024.110592
Lingli Cui , Qiang Shen , Yongchang Xiao , Dongdong Liu , Huaqing Wang
Effective prediction of machinery remaining useful life (RUL) is prominent to achieve intelligent preventive maintenance in manufacturing systems. In this paper, a sparse graph structure fusion convolutional network (SGSFCN) is proposed for more accurate end-to-end RUL prediction of machine. A novel node-level graph structure called time series shapelet distance graph (TSSDG) is designed to convert the time series to node feature. The SGSFCN model is proposed to learn degradation information from the graph structure. In SGSFCN, a sparse graph structure (SGS) layer and a fusion graph structure (FGS) layer preceding the graph convolutional network (GCN) are designed to learn the SGS from node representation and fuse the original graph structure, enabling the graph structure and node update iteratively in subsequent layers. Concurrently, a bidirectional long short-term memory network (BiLSTM) layer is integrated to capture the global temporal dependencies. The method is validated by two test rig data, and results demonstrate that the proposed method offers significantly higher prediction accuracy of RUL compared to several state-of-art methods.
{"title":"Sparse graph structure fusion convolutional network for machinery remaining useful life prediction","authors":"Lingli Cui , Qiang Shen , Yongchang Xiao , Dongdong Liu , Huaqing Wang","doi":"10.1016/j.ress.2024.110592","DOIUrl":"10.1016/j.ress.2024.110592","url":null,"abstract":"<div><div>Effective prediction of machinery remaining useful life (RUL) is prominent to achieve intelligent preventive maintenance in manufacturing systems. In this paper, a sparse graph structure fusion convolutional network (SGSFCN) is proposed for more accurate end-to-end RUL prediction of machine. A novel node-level graph structure called time series shapelet distance graph (TSSDG) is designed to convert the time series to node feature. The SGSFCN model is proposed to learn degradation information from the graph structure. In SGSFCN, a sparse graph structure (SGS) layer and a fusion graph structure (FGS) layer preceding the graph convolutional network (GCN) are designed to learn the SGS from node representation and fuse the original graph structure, enabling the graph structure and node update iteratively in subsequent layers. Concurrently, a bidirectional long short-term memory network (BiLSTM) layer is integrated to capture the global temporal dependencies. The method is validated by two test rig data, and results demonstrate that the proposed method offers significantly higher prediction accuracy of RUL compared to several state-of-art methods.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"254 ","pages":"Article 110592"},"PeriodicalIF":9.4,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-24DOI: 10.1016/j.ress.2024.110602
Yang Liu , Guangda Zhou , Shujian Zhao , Liang Li , Wenhua Xie , Bengan Su , Yongwei Li , Zhen Zhao
It is critical to accurately predict the remaining useful life (RUL) of rolling bearings to avoid severe accidents and financial losses in the industry. Nevertheless, accurately determining the initial prediction time (IPT) continues to pose a challenge, and significant differences in the data distribution of bearings under different operating conditions are frequently overlooked. To deal with these problems, we propose a novel two-stage method based on the adversarial strategy for RUL prediction of bearings under variable conditions. Firstly, we create reliable health indicators in an unsupervised manner by recording the coded characteristics of the bearing’s state of health. Secondly, an adaptive threshold method based on rate-of-change (ATMROC) is developed to perform accurate health state classification. Finally, we propose a RUL prediction network based on the attention depth-gated recurrent unit with domain invariance (DIADGRU) to handle the inconsistent distribution of degradation features under different operating conditions. Experiments of RUL prediction on PHM2012 and XITU-SY datasets are implemented, and the promising results validate the effectiveness of the proposed method.
{"title":"A novel two-stage method via adversarial strategy for remaining useful life prediction of bearings under variable conditions","authors":"Yang Liu , Guangda Zhou , Shujian Zhao , Liang Li , Wenhua Xie , Bengan Su , Yongwei Li , Zhen Zhao","doi":"10.1016/j.ress.2024.110602","DOIUrl":"10.1016/j.ress.2024.110602","url":null,"abstract":"<div><div>It is critical to accurately predict the remaining useful life (RUL) of rolling bearings to avoid severe accidents and financial losses in the industry. Nevertheless, accurately determining the initial prediction time (IPT) continues to pose a challenge, and significant differences in the data distribution of bearings under different operating conditions are frequently overlooked. To deal with these problems, we propose a novel two-stage method based on the adversarial strategy for RUL prediction of bearings under variable conditions. Firstly, we create reliable health indicators in an unsupervised manner by recording the coded characteristics of the bearing’s state of health. Secondly, an adaptive threshold method based on rate-of-change (ATMROC) is developed to perform accurate health state classification. Finally, we propose a RUL prediction network based on the attention depth-gated recurrent unit with domain invariance (DIADGRU) to handle the inconsistent distribution of degradation features under different operating conditions. Experiments of RUL prediction on PHM2012 and XITU-SY datasets are implemented, and the promising results validate the effectiveness of the proposed method.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"254 ","pages":"Article 110602"},"PeriodicalIF":9.4,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-23DOI: 10.1016/j.ress.2024.110597
Xinyu Li , Changming Cheng , Zhike Peng
Exploring fault diagnosis methods for mechanical equipment with weak dependency on annotated data is essential for industrial production. Contrastive learning (CL), capable of learning representations without labeling information, has achieved satisfactory performance in mechanical fault diagnosis. However, current CL-based approaches mainly encounter two limitations. First, the pre-training stage uses either unannotated or annotated samples exclusively while the fine-tuning stage solely relies on annotated ones, leading to inefficient sample utilization. Second, the representation learned by contrastive loss alone in the pretext task is sub-optimal for downstream diagnostic tasks. To address these issues, this paper proposed a novel diagnostic framework based on label-guided contrastive learning (LgCL) and weighted pseudo-labeling (WPL) strategy to improve fault diagnosis accuracy. In the pre-training stage, the proposed LgCL integrates two types of contrastive loss together with classification loss, enabling the encoder to learn discriminative representations that directly benefit the downstream diagnostic task. The devised hybrid fine-tuning strategy allows both labeled and unlabeled data to participate in fine-tuning via pseudo-labeling, enhancing model generalization. The pertinently designed WPL strategy mitigates the defect of noisy pseudo labels. Comparison and ablation experiments on two public datasets and one self-designed dataset validate the superiority of the proposed method for fault diagnosis with limited annotated data, with diagnostic accuracies improved by 25.30%, 5.47% and 10.02% over supervised, semi-supervised and contrastive learning methods, respectively.
{"title":"Label-guided contrastive learning with weighted pseudo-labeling: A novel mechanical fault diagnosis method with insufficient annotated data","authors":"Xinyu Li , Changming Cheng , Zhike Peng","doi":"10.1016/j.ress.2024.110597","DOIUrl":"10.1016/j.ress.2024.110597","url":null,"abstract":"<div><div>Exploring fault diagnosis methods for mechanical equipment with weak dependency on annotated data is essential for industrial production. Contrastive learning (CL), capable of learning representations without labeling information, has achieved satisfactory performance in mechanical fault diagnosis. However, current CL-based approaches mainly encounter two limitations. First, the pre-training stage uses either unannotated or annotated samples exclusively while the fine-tuning stage solely relies on annotated ones, leading to inefficient sample utilization. Second, the representation learned by contrastive loss alone in the pretext task is sub-optimal for downstream diagnostic tasks. To address these issues, this paper proposed a novel diagnostic framework based on label-guided contrastive learning (LgCL) and weighted pseudo-labeling (WPL) strategy to improve fault diagnosis accuracy. In the pre-training stage, the proposed LgCL integrates two types of contrastive loss together with classification loss, enabling the encoder to learn discriminative representations that directly benefit the downstream diagnostic task. The devised hybrid fine-tuning strategy allows both labeled and unlabeled data to participate in fine-tuning via pseudo-labeling, enhancing model generalization. The pertinently designed WPL strategy mitigates the defect of noisy pseudo labels. Comparison and ablation experiments on two public datasets and one self-designed dataset validate the superiority of the proposed method for fault diagnosis with limited annotated data, with diagnostic accuracies improved by 25.30%, 5.47% and 10.02% over supervised, semi-supervised and contrastive learning methods, respectively.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"254 ","pages":"Article 110597"},"PeriodicalIF":9.4,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-22DOI: 10.1016/j.ress.2024.110594
Lukáš Novák , Marcos Valdebenito , Matthias Faes
Fractional statistical moments are utilized for various tasks of uncertainty quantification, including the estimation of probability distributions. However, an estimation of fractional statistical moments of costly mathematical models by statistical sampling is challenging since it is typically not possible to create a large experimental design due to limitations in computing capacity. This paper presents a novel approach for the analytical estimation of fractional moments, directly from polynomial chaos expansions. Specifically, the first four statistical moments obtained from the deterministic coefficients of polynomial chaos expansion are used for an estimation of arbitrary fractional moments via Hölder’s inequality. The proposed approach is utilized for an estimation of statistical moments and probability distributions in four numerical examples of increasing complexity. Obtained results show that the proposed approach achieves a superior performance in estimating the distribution of the response, in comparison to a standard Latin hypercube sampling in the presented examples.
{"title":"On fractional moment estimation from polynomial chaos expansion","authors":"Lukáš Novák , Marcos Valdebenito , Matthias Faes","doi":"10.1016/j.ress.2024.110594","DOIUrl":"10.1016/j.ress.2024.110594","url":null,"abstract":"<div><div>Fractional statistical moments are utilized for various tasks of uncertainty quantification, including the estimation of probability distributions. However, an estimation of fractional statistical moments of costly mathematical models by statistical sampling is challenging since it is typically not possible to create a large experimental design due to limitations in computing capacity. This paper presents a novel approach for the analytical estimation of fractional moments, directly from polynomial chaos expansions. Specifically, the first four statistical moments obtained from the deterministic coefficients of polynomial chaos expansion are used for an estimation of arbitrary fractional moments via Hölder’s inequality. The proposed approach is utilized for an estimation of statistical moments and probability distributions in four numerical examples of increasing complexity. Obtained results show that the proposed approach achieves a superior performance in estimating the distribution of the response, in comparison to a standard Latin hypercube sampling in the presented examples.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"254 ","pages":"Article 110594"},"PeriodicalIF":9.4,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-22DOI: 10.1016/j.ress.2024.110589
Xiaolei Chu, Ziqi Wang
Perturbed by natural hazards, community-level infrastructure networks operate like many-body systems, with behaviors emerging from coupling individual component dynamics with group correlations and interactions. It follows that we can borrow methods from statistical physics to study the response of infrastructure systems to natural disasters. This study aims to construct a joint probability distribution model to describe the post-hazard state of infrastructure networks and propose an efficient surrogate model of the joint distribution for large-scale systems. Specifically, we present maximum entropy modeling of the regional impact of natural hazards on civil infrastructures. Provided with the current state of knowledge, the principle of maximum entropy yields the “most unbiased“ joint distribution model for the performances of infrastructures. In the general form, the model can handle multivariate performance states and higher-order correlations. In a particular yet typical scenario of binary performance state variables with knowledge of their mean and pairwise correlation, the joint distribution reduces to the Ising model in statistical physics. In this context, we propose using a dichotomized Gaussian model as an efficient surrogate for the maximum entropy model, facilitating the application to large systems. Using the proposed method, we investigate the seismic collective behavior of a large-scale road network (with 8,694 nodes and 26,964 links) in San Francisco, showcasing the non-trivial collective behaviors of infrastructure systems.
{"title":"Maximum entropy-based modeling of community-level hazard responses for civil infrastructures","authors":"Xiaolei Chu, Ziqi Wang","doi":"10.1016/j.ress.2024.110589","DOIUrl":"10.1016/j.ress.2024.110589","url":null,"abstract":"<div><div>Perturbed by natural hazards, community-level infrastructure networks operate like many-body systems, with behaviors emerging from coupling individual component dynamics with group correlations and interactions. It follows that we can borrow methods from statistical physics to study the response of infrastructure systems to natural disasters. This study aims to construct a joint probability distribution model to describe the post-hazard state of infrastructure networks and propose an efficient surrogate model of the joint distribution for large-scale systems. Specifically, we present maximum entropy modeling of the regional impact of natural hazards on civil infrastructures. Provided with the current state of knowledge, the principle of maximum entropy yields the “most unbiased“ joint distribution model for the performances of infrastructures. In the general form, the model can handle multivariate performance states and higher-order correlations. In a particular yet typical scenario of binary performance state variables with knowledge of their mean and pairwise correlation, the joint distribution reduces to the Ising model in statistical physics. In this context, we propose using a dichotomized Gaussian model as an efficient surrogate for the maximum entropy model, facilitating the application to large systems. Using the proposed method, we investigate the seismic collective behavior of a large-scale road network (with 8,694 nodes and 26,964 links) in San Francisco, showcasing the non-trivial collective behaviors of infrastructure systems.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"254 ","pages":"Article 110589"},"PeriodicalIF":9.4,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142527385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-20DOI: 10.1016/j.ress.2024.110601
Fucheng Han , Wenhua Wang , Xiao-Wei Zheng , Xu Han , Wei Shi , Xin Li
The probabilistic-based design method is gradually gaining attention in the wind industry because it provides more accurate modeling of uncertainty variables than that of traditional methods. Unfortunately, the numerous uncertainty variables involved in structural design are major obstacles to the successful application of this method. Therefore, this study presents a sensitivity analysis (SA) of a benchmark monopile offshore wind turbine (OWT) to screen the top-ranking variables from the viewpoint of reliability. Primarily, a comprehensive reliability SA framework of OWT is proposed, in which a novel measurement of soil uncertainties is conducted using quantitative analysis from the perspective of soil structure interaction (SSI). Subsequently, a reliability SA is conducted to explore the crucial variables influencing the structural safety from the uncertain clusters. The results indicate that Young's modulus, structural geometry, and SSI have significant effects on the structural reliability of excessive vibration failure. The hydrodynamic and aerodynamic load variables exhibit the most prominent influence on excessive deflection failure. Additionally, the SSI uncertainties exhibit a non-negligible effect in affecting the structural reliability, i.e., the lateral bending stiffness shows more sensitivity to the normal operation cases, whereas the impact of joint stiffness is more remarkable in parked scenarios.
{"title":"Investigation of essential parameters for the design of offshore wind turbine based on structural reliability","authors":"Fucheng Han , Wenhua Wang , Xiao-Wei Zheng , Xu Han , Wei Shi , Xin Li","doi":"10.1016/j.ress.2024.110601","DOIUrl":"10.1016/j.ress.2024.110601","url":null,"abstract":"<div><div>The probabilistic-based design method is gradually gaining attention in the wind industry because it provides more accurate modeling of uncertainty variables than that of traditional methods. Unfortunately, the numerous uncertainty variables involved in structural design are major obstacles to the successful application of this method. Therefore, this study presents a sensitivity analysis (SA) of a benchmark monopile offshore wind turbine (OWT) to screen the top-ranking variables from the viewpoint of reliability. Primarily, a comprehensive reliability SA framework of OWT is proposed, in which a novel measurement of soil uncertainties is conducted using quantitative analysis from the perspective of soil structure interaction (SSI). Subsequently, a reliability SA is conducted to explore the crucial variables influencing the structural safety from the uncertain clusters. The results indicate that Young's modulus, structural geometry, and SSI have significant effects on the structural reliability of excessive vibration failure. The hydrodynamic and aerodynamic load variables exhibit the most prominent influence on excessive deflection failure. Additionally, the SSI uncertainties exhibit a non-negligible effect in affecting the structural reliability, i.e., the lateral bending stiffness shows more sensitivity to the normal operation cases, whereas the impact of joint stiffness is more remarkable in parked scenarios.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"254 ","pages":"Article 110601"},"PeriodicalIF":9.4,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-20DOI: 10.1016/j.ress.2024.110578
Hang-Hang Gu , Run-Zi Wang , Kun Zhang , Kai-Shang Li , Li Sun , Xian-Cheng Zhang , Shan-Tung Tu
The high-temperature rotating structures (HTRS), e.g., steam turbine rotors, often operate in extremely harsh environments with a flexible load condition during peak shaving of power system. In this work, a damage-driven framework for reliability assessment is developed in terms of the cumulative damage-damage threshold interference (CD-DT) principle, in which the cumulative damage and damage threshold are regarded as two random parameters to address uncertainties. The CD-DT principle is founded on the engineering damage theory and incorporates physics-of-failure into the probabilistic modeling of high-temperature structural reliability. Probabilistic damage analysis, correlation analysis of weak sites, system-level reliability analysis, and sensitivity analysis have been encompassed in this framework. Three numerical examples are used to verify the effectiveness and applicability of the proposed framework. Application to steam turbine rotor involving multiple weak sites with multi-damage modes illustrate the implementation procedures of the framework. Results show that the reliability-based design life of rotor decreases with the increases of start-stop frequency, the implementation of a two-shift operation would pose a threat to meeting the safety requirement of a 30-year design life. Furthermore, sensitivity analysis highlights the critical influences of initial rotor temperature and speed rising rate on rotor reliability, providing insights for operational maintenance and reliability optimization.
{"title":"Damage-driven framework for reliability assessment of steam turbine rotors operating under flexible conditions","authors":"Hang-Hang Gu , Run-Zi Wang , Kun Zhang , Kai-Shang Li , Li Sun , Xian-Cheng Zhang , Shan-Tung Tu","doi":"10.1016/j.ress.2024.110578","DOIUrl":"10.1016/j.ress.2024.110578","url":null,"abstract":"<div><div>The high-temperature rotating structures (HTRS), e.g., steam turbine rotors, often operate in extremely harsh environments with a flexible load condition during peak shaving of power system. In this work, a damage-driven framework for reliability assessment is developed in terms of the cumulative damage-damage threshold interference (CD-DT) principle, in which the cumulative damage and damage threshold are regarded as two random parameters to address uncertainties. The CD-DT principle is founded on the engineering damage theory and incorporates physics-of-failure into the probabilistic modeling of high-temperature structural reliability. Probabilistic damage analysis, correlation analysis of weak sites, system-level reliability analysis, and sensitivity analysis have been encompassed in this framework. Three numerical examples are used to verify the effectiveness and applicability of the proposed framework. Application to steam turbine rotor involving multiple weak sites with multi-damage modes illustrate the implementation procedures of the framework. Results show that the reliability-based design life of rotor decreases with the increases of start-stop frequency, the implementation of a two-shift operation would pose a threat to meeting the safety requirement of a 30-year design life. Furthermore, sensitivity analysis highlights the critical influences of initial rotor temperature and speed rising rate on rotor reliability, providing insights for operational maintenance and reliability optimization.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"254 ","pages":"Article 110578"},"PeriodicalIF":9.4,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the escalating dependence on electricity and natural gas infrastructure, ensuring both reliability and economic efficiency becomes paramount. It necessitates reliability centric measures to mitigate disruptions that could cascade between these interconnected systems. To address this challenges, this paper introduces a reliability-constrained two-stage stochastic model to optimize power-to-gas (P2 G) and gas-to-power (G2P) unit placement and sizing, aiming to enhance the reliability of both systems under stochastic scenarios. The proposed model, employing Sequential Monte Carlo (SMC) within its optimization framework, seeks to minimize investment, operation, and reliability costs. By addressing temporal uncertainties in component outages for both systems and considering uncertainties in power and gas system loads with a high temporal resolution and annual load growth, the model provides a comprehensive reliability perspective. Furthermore, sensitivity analysis is conducted to explore the impact of varying Values of Lost Load (VOLL) on the planning results. Numerical evaluation, using two integrated energy systems including IEEE 14-bus-10-gas node, and large-scale energy systems including IEEE 118-bus-85-gas node integrated power-gas system (IPGS), demonstrates a significant 12.53 % improvement in overall system reliability. Furthermore, a 2.81 % reduction in operation costs and a substantial 26.3 % reduction in reliability costs, validating the effectiveness of the proposed model.
{"title":"Reliability-constrained configuration optimization for integrated power and natural gas energy systems: A stochastic approach","authors":"Mostafa Shabanian-Poodeh , Rahmat-Allah Hooshmand , Miadreza Shafie-khah","doi":"10.1016/j.ress.2024.110600","DOIUrl":"10.1016/j.ress.2024.110600","url":null,"abstract":"<div><div>With the escalating dependence on electricity and natural gas infrastructure, ensuring both reliability and economic efficiency becomes paramount. It necessitates reliability centric measures to mitigate disruptions that could cascade between these interconnected systems. To address this challenges, this paper introduces a reliability-constrained two-stage stochastic model to optimize power-to-gas (P2 G) and gas-to-power (G2P) unit placement and sizing, aiming to enhance the reliability of both systems under stochastic scenarios. The proposed model, employing Sequential Monte Carlo (SMC) within its optimization framework, seeks to minimize investment, operation, and reliability costs. By addressing temporal uncertainties in component outages for both systems and considering uncertainties in power and gas system loads with a high temporal resolution and annual load growth, the model provides a comprehensive reliability perspective. Furthermore, sensitivity analysis is conducted to explore the impact of varying Values of Lost Load (VOLL) on the planning results. Numerical evaluation, using two integrated energy systems including IEEE 14-bus-10-gas node, and large-scale energy systems including IEEE 118-bus-85-gas node integrated power-gas system (IPGS), demonstrates a significant 12.53 % improvement in overall system reliability. Furthermore, a 2.81 % reduction in operation costs and a substantial 26.3 % reduction in reliability costs, validating the effectiveness of the proposed model.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"254 ","pages":"Article 110600"},"PeriodicalIF":9.4,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142527386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Human factors play an important role in the take-over process of Level 3 (L3) automated driving. This paper combines Systems Theoretic Process Analysis (STPA) and Information, Decision and Action in Crew context (IDAC) for qualitative analysis and Bayesian Network (BN) and Success Likelihood Index Method (SLIM) for quantitative calculation to obtain the main performance shaping factors (PSFs) and evaluation indicators that cause human errors. Firstly, the STPA-IDAC method is used to analyze unsafe control actions (UCAs) for take-over process and form the mapping relationship of UCAs-IDA-PSFs. Secondly, the BN of human reliability analysis for take-over process is constructed based on the BN-SLIM method. Uncertainty in rates of PSFs and evaluation indicators is addressed in a probabilistic manner using expert opinions and empirical data. After diagnostic reasoning of BN, mean variation is used to identify the main PSFs and evaluation indicators. This method can effectively identify the main PSFs and evaluation indicators that cause human errors, facilitate risk assessment and management, and reduce the human error probability (HEP).
{"title":"A human reliability analysis method based on STPA-IDAC and BN-SLIM for driver take-over in Level 3 automated driving","authors":"Wenyi Liao , Yidan Qiao , Tongxin Dong , Zhiming Gou , Dengkai Chen","doi":"10.1016/j.ress.2024.110577","DOIUrl":"10.1016/j.ress.2024.110577","url":null,"abstract":"<div><div>Human factors play an important role in the take-over process of Level 3 (L3) automated driving. This paper combines Systems Theoretic Process Analysis (STPA) and Information, Decision and Action in Crew context (IDAC) for qualitative analysis and Bayesian Network (BN) and Success Likelihood Index Method (SLIM) for quantitative calculation to obtain the main performance shaping factors (PSFs) and evaluation indicators that cause human errors. Firstly, the STPA-IDAC method is used to analyze unsafe control actions (UCAs) for take-over process and form the mapping relationship of UCAs-IDA-PSFs. Secondly, the BN of human reliability analysis for take-over process is constructed based on the BN-SLIM method. Uncertainty in rates of PSFs and evaluation indicators is addressed in a probabilistic manner using expert opinions and empirical data. After diagnostic reasoning of BN, mean variation is used to identify the main PSFs and evaluation indicators. This method can effectively identify the main PSFs and evaluation indicators that cause human errors, facilitate risk assessment and management, and reduce the human error probability (HEP).</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"254 ","pages":"Article 110577"},"PeriodicalIF":9.4,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142527383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-18DOI: 10.1016/j.ress.2024.110560
Mehdi Saman Azari , Stefania Santini , Farid Edrisi , Francesco Flammini
In recent years, intelligent fault diagnosis based on domain adaptation has been used to address domain shifts in cyber–physical systems; however, the need for acquiring target data sufficiently limits their applicability to unseen working conditions. To overcome such limitations, domain generalization techniques have been introduced to enhance the capacity of fault diagnostic models to operate under unseen working conditions. Nevertheless, existing approaches assume access to extensive labeled training data from various source domains, posing challenges in real-world engineering scenarios due to resource constraints. Moreover, the absence of a mechanism for updating diagnostic models over time calls for the exploration of self-adaptive generalized diagnosis models that are capable of autonomous reconfiguration in response to new unseen working conditions. In such a context, this paper proposes a self-adaptive fault diagnosis system that combines several paradigms, namely Monitor-Analyze-Plan-Execute over a shared Knowledge (MAPE-K), Domain Generalization Network Models (DGNMs), and Digital Twins (DT). The MAPE-K loop enables run-time adaptation to dynamic industrial environments without human intervention. To address the scarcity of labeled training data, digital twins are used to generate supplementary data and continuously tune parameters to reflect the dynamics of new unseen working conditions. DGNM incorporates adversarial learning and a domain-based discrepancy metric to enhance feature diversity and generalization. The introduction of multi-domain data augmentation enhances feature diversity and facilitates learning correlations among multiple domains, ultimately improving the generalization of feature representations. The proposed fault diagnosis system has been evaluated on three publicly available rotating machinery datasets to demonstrate its higher performance in cross-work operation and cross-machine tasks compared to other state-of-the-art methods.
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