Reliability Engineering & System Safety最新文献_第3页

Multivariate degradation modeling using generalized cauchy process and application in life prediction of dye-sensitized solar cells

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-11-17 DOI: 10.1016/j.ress.2024.110651

Ali Asgari , Wujun Si , Wei Wei , Krishna Krishnan , Kunpeng Liu

{"title":"Multivariate degradation modeling using generalized cauchy process and application in life prediction of dye-sensitized solar cells","authors":"Ali Asgari , Wujun Si , Wei Wei , Krishna Krishnan , Kunpeng Liu","doi":"10.1016/j.ress.2024.110651","DOIUrl":"10.1016/j.ress.2024.110651","url":null,"abstract":"<div><div>Recently, the Generalized Cauchy (GC) process has been applied to capture a Long Memory (LM) phenomenon in product degradation modeling and life prediction. Compared with the traditional fractional Brownian motion that captures the LM using a single Hurst parameter, the GC process has two free parameters (Hurst and fractal dimension parameters) that flexibly capture both global LM and local irregularity. However, all existing GC-based degradation models are for a single Degradation Characteristic (DC). In this article, motivated by a real degradation problem of dye-sensitized solar cells that jointly exhibits multiple DCs, global LM, local irregularity and DC-wise cross-correlation, we propose a novel GC-based Multivariate Degradation Model (GC-MDM) to simultaneously capture the aforementioned effects. A maximum likelihood estimation approach is developed to estimate parameters of the GC-MDM. Subsequently, product life prediction based on the GC-MDM is developed. The proposed GC-MDM is validated through a simulation study and a physical experiment of dye-sensitized solar cells. Results show that the proposed GC-MDM fundamentally improves the life prediction accuracy in comparison with conventional degradation models which significantly misestimate the uncertainty of product life.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110651"},"PeriodicalIF":9.4,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744675","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}

引用次数: 0

A dynamic collaborative adversarial domain adaptation network for unsupervised rotating machinery fault diagnosis

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-11-17 DOI: 10.1016/j.ress.2024.110662

Xin Wang, Hongkai Jiang, Mingzhe Mu, Yutong Dong

{"title":"A dynamic collaborative adversarial domain adaptation network for unsupervised rotating machinery fault diagnosis","authors":"Xin Wang, Hongkai Jiang, Mingzhe Mu, Yutong Dong","doi":"10.1016/j.ress.2024.110662","DOIUrl":"10.1016/j.ress.2024.110662","url":null,"abstract":"<div><div>Acquiring sufficient fault data labels for new tasks in rotating machinery fault diagnosis is tricky. Accurately identifying faults in unlabeled scenarios is a critical and urgent practical need. Unsupervised domain adaptation (UDA) is a mainstream method to address this issue. However, most existing UDA models are static and struggle to dynamically adjust according to changes in the target task, resulting in limited diagnostic performance. To address this limitation, a dynamic collaborative adversarial domain adaptation network (DCADAN) is proposed for unsupervised rotating machinery fault diagnosis. Firstly, a multi-objective dynamic collaborative generator is designed to endow with dynamic characteristics for adjusting its own architecture, enhancing the capture capability of key domain adaptation features. Secondly, a dual-system dynamic collaborative adversarial mode is established to dynamically adjust the network training architecture, forming task-oriented refined diagnostic decision edges to steadily improve domain adaptation diagnostic capability. Finally, a multi-source domain dynamic collaborative loss is developed to match the force of multiple source domains, forming an efficient collaborative diagnostic pattern with dynamic adjustment across multi-source domains. Two case studies indicate that DCADAN demonstrates superior diagnostic performance when executing cross-domain diagnosis tasks without target labels.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110662"},"PeriodicalIF":9.4,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744555","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}

引用次数: 0

Lifting wavelet-informed hierarchical domain adaptation network: An interpretable digital twin-driven gearbox fault diagnosis method 提升小波信息分层域自适应网络：可解释的数字孪生驱动齿轮箱故障诊断方法

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-11-17 DOI: 10.1016/j.ress.2024.110660

Sixiang Jia, Dingyi Sun, Khandaker Noman, Xin Wang, Yongbo Li

{"title":"Lifting wavelet-informed hierarchical domain adaptation network: An interpretable digital twin-driven gearbox fault diagnosis method","authors":"Sixiang Jia, Dingyi Sun, Khandaker Noman, Xin Wang, Yongbo Li","doi":"10.1016/j.ress.2024.110660","DOIUrl":"10.1016/j.ress.2024.110660","url":null,"abstract":"<div><div>Digital twin (DT) has served as a dependable technology for supplementing reliable simulated fault data in gearbox fault diagnosis. However, the vast data distribution discrepancy and insufficient interpretability still significantly limit the industrial application of DT-driven fault diagnosis methods. To solve these problems, a lifting wavelet-informed hierarchical domain adaptation network (LHDAN) is proposed for transferring the diagnostic knowledge between the physical gearbox and DT model. LHDAN improves the interpretability of diagnostic knowledge transfer in terms of parameter initialization, physical constraints on the training process, and feature distribution adaptation. Specifically, LHDAN utilizes a lifting wavelet-informed convolutional neural network (LW-Conv) to mimic the cascade structure of lifting wavelet decomposition, in which the fully learnable prediction and update operators are initialized with existing wavelet bases and further constrained with high-pass and low-pass filters in the training process. Furthermore, a kurtosis-guided attention mechanism is proposed to fuse hierarchical features with diverse transferabilities flexibly. Finally, the fused hierarchical features of the actual gearbox and DT model are explicitly aligned to eliminate the feature distribution discrepancies. A high-fidelity DT model is established based on an industrial gearbox fault test bench. Compared to several state-of-the-art models, LHDAN demonstrates superior interpretability and diagnostic performance.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"254 ","pages":"Article 110660"},"PeriodicalIF":9.4,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142697253","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}

引用次数: 0

Emergency Response Inference Mapping (ERIMap): A Bayesian network-based method for dynamic observation processing

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-11-17 DOI: 10.1016/j.ress.2024.110640

Moritz Schneider , Lukas Halekotte , Tina Comes , Daniel Lichte , Frank Fiedrich

{"title":"Emergency Response Inference Mapping (ERIMap): A Bayesian network-based method for dynamic observation processing","authors":"Moritz Schneider , Lukas Halekotte , Tina Comes , Daniel Lichte , Frank Fiedrich","doi":"10.1016/j.ress.2024.110640","DOIUrl":"10.1016/j.ress.2024.110640","url":null,"abstract":"<div><div>In emergencies, high stake decisions often have to be made under time pressure and strain. In order to support such decisions, information from various sources needs to be collected and processed rapidly. The information available tends to be temporally and spatially variable, uncertain, and sometimes conflicting, leading to potential biases in decisions. Currently, there is a lack of systematic approaches for information processing and situation assessment which meet the particular demands of emergency situations. To address this gap, we present a Bayesian network-based method called <em>ERIMap</em> that is tailored to the complex information-scape during emergencies. The method enables the systematic and rapid processing of heterogeneous and potentially uncertain observations and draws inferences about key variables of an emergency. It thereby reduces complexity and cognitive load for decision makers. The output of the <em>ERIMap</em> method is a dynamically evolving and spatially resolved map of beliefs about key variables of an emergency that is updated each time a new observation becomes available. The method is illustrated in a case study in which an emergency response is triggered by an accident causing a gas leakage on a chemical plant site.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110640"},"PeriodicalIF":9.4,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Repairing smarter: Opportunistic maintenance for a closed-loop supply chain with spare parts dependency

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-11-17 DOI: 10.1016/j.ress.2024.110642

Abdelhamid Boujarif , David W. Coit , Oualid Jouini , Zhiguo Zeng , Robert Heidsieck

{"title":"Repairing smarter: Opportunistic maintenance for a closed-loop supply chain with spare parts dependency","authors":"Abdelhamid Boujarif , David W. Coit , Oualid Jouini , Zhiguo Zeng , Robert Heidsieck","doi":"10.1016/j.ress.2024.110642","DOIUrl":"10.1016/j.ress.2024.110642","url":null,"abstract":"<div><div>Adopting a closed-loop supply chain enhances spare part provisioning through repair, remanufacturing, and recycling. However, poor maintenance of components can have severe consequences. Unlike traditional opportunistic maintenance methods that assume regular inspections or precise degradation monitoring, we propose a model that leverages historical repair data to replace worn components preventively. It considers the real-world workflow where parts are often restored only to a functional level. We study maintenance strategies for repeatedly repaired multi-component systems by applying preventive operations only during corrective repairs. Our model considers component ages, failure time distributions, and structural and economic dependencies, favoring collective over individual replacements for cost efficiency. Stochastic dependencies are mapped using Nataf transformation for component subsets, and a genetic algorithm identifies optimal maintenance strategies to reduce long-term operational costs by balancing maintenance against potential failure penalties. We demonstrate the effectiveness of our approach with a case study on MRI power supply machines, showing that preventive actions can cut early life failures by up to 50% and extend useful life by over a year. Sensitivity analysis reveals that logistic costs, interest rates, and planning horizons influence decisions. Opportunistic maintenance can reduce logistic costs and increase the lifetime of spare parts after repair. Integrating stochastic dependency is computationally efficient for industrial systems and can help predict failures more accurately.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110642"},"PeriodicalIF":9.4,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744665","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}

引用次数: 0

Risk analysis of weather-related railroad accidents in the United States

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-11-17 DOI: 10.1016/j.ress.2024.110647

Zhipeng Zhang , Chen-Yu Lin

{"title":"Risk analysis of weather-related railroad accidents in the United States","authors":"Zhipeng Zhang , Chen-Yu Lin","doi":"10.1016/j.ress.2024.110647","DOIUrl":"10.1016/j.ress.2024.110647","url":null,"abstract":"<div><div>Global climate change has led to more frequent extreme weather events in recent decades, and this has impacted critical infrastructure such as railway systems. Although train accidents caused by extreme weather events are not uncommon, the level of their risk is foreseen to rise to an unacceptable level. As a result, proper data collection and analysis for train accident related to extreme weather are pertinent to developing an effective railway climate change adaptation plan. This paper presents a comprehensive and quantitative analysis of weather-related railroad accidents in the United States. The analysis comprises time series, spatial, and causal elements to understand the temporal trends of weather-related railroad accidents, the predominant type of weather causes, and the effect of regional meteorological characteristics on them. The results showed that the likelihood of weather-related railroad accidents varies by meteorological regions and does not show a clear increasing or decreasing trend, but their above-average severity indicates opportunities to mitigate the risk in light of the projected increasing frequency. Results of this research contribute to better understanding of railway extreme weather risk and serve as a foundation for future research that addresses the effect of climate change on railroad system and develops proper railway climate change adaptation plans.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110647"},"PeriodicalIF":9.4,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744674","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}

引用次数: 0

Condition-based maintenance for multi-state systems with prognostic and deep reinforcement learning

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-11-16 DOI: 10.1016/j.ress.2024.110659

Huixian Zhang, Xiukun Wei, Zhiqiang Liu, Yaning Ding, Qingluan Guan

{"title":"Condition-based maintenance for multi-state systems with prognostic and deep reinforcement learning","authors":"Huixian Zhang, Xiukun Wei, Zhiqiang Liu, Yaning Ding, Qingluan Guan","doi":"10.1016/j.ress.2024.110659","DOIUrl":"10.1016/j.ress.2024.110659","url":null,"abstract":"<div><div>The utilization of prognostic information in practical engineering is increasing with the development of technology and predictive modeling. Current research on maintenance strategies for complex multi-state systems often neglects prognostic information or assumes complete availability of all component information. This paper investigates the joint maintenance strategies based on condition-based maintenance for complex multi-state systems, in which the predicted remaining useful life of some components is known. Firstly, a maintenance strategy framework is developed and the joint maintenance strategy is proposed for the studied problem. Then the deterioration process of the component, the imperfect maintenance, and prediction error models are constructed. The optimization problem is modeled as a Markov Decision Process to minimize the maintenance cost, and the system reliability constraints are established by using the universal generating function method. In addition, a deep Q-network is designed to solve the optimal maintenance policy. Finally, the traction system of a metro train is taken as an example to verify the applicability of the model and algorithm. The results show that the proposed maintenance strategy reduces the maintenance cost compared to the current maintenance strategy for both fixed maintenance intervals and dynamic maintenance intervals.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110659"},"PeriodicalIF":9.4,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744551","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}

引用次数: 0

Service risk evaluation of telecommunication core network: A perspective of routing resilience

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-11-15 DOI: 10.1016/j.ress.2024.110629

Zongqi Xue, Zhenglin Liang

{"title":"Service risk evaluation of telecommunication core network: A perspective of routing resilience","authors":"Zongqi Xue, Zhenglin Liang","doi":"10.1016/j.ress.2024.110629","DOIUrl":"10.1016/j.ress.2024.110629","url":null,"abstract":"<div><div>The increasing dependence on telecommunication networks for digitalization raises apprehensions about their vulnerability to disruptions, potentially impacting service performance. Classic risk analysis for telecommunication networks often overlooks the compensating effect of rerouting and its resulting resilience. Our study is devoted to conducting an evaluation of service risk within telecommunication core networks, with a particular emphasis on rerouting resilience. Telecommunication core networks operate on a large scale, where various autonomous systems interconnect through pivotal anchor points. Its routing is typically governed by multiple protocols, including Interior Gateway Protocol and Border Gateway Protocol. We apply percolation theory and Monte Carlo Tree Search to effectively analyze the potential service risk and sequential rerouting effects caused by disruptions marked by a noteworthy probability and substantial impact. Our findings highlight the dual nature of rerouting: it enhances service connectivity while potentially increasing service flows in nearby routers, leading to heightened time delays and packet loss ratios. To comprehensively assess network service risk considering this double-edged sword impact, we devise a performance vector covering service connectivity, time delay, and packet loss across various data transmissions and disruptions. The overall approach is tested on two telecommunication core networks.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"255 ","pages":"Article 110629"},"PeriodicalIF":9.4,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744679","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}

引用次数: 0

Multi-label domain adversarial reinforcement learning for unsupervised compound fault recognition 用于无监督复合故障识别的多标签域对抗强化学习

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-11-15 DOI: 10.1016/j.ress.2024.110638

Zisheng Wang , Jianping Xuan , Tielin Shi , Yan-Fu Li

{"title":"Multi-label domain adversarial reinforcement learning for unsupervised compound fault recognition","authors":"Zisheng Wang , Jianping Xuan , Tielin Shi , Yan-Fu Li","doi":"10.1016/j.ress.2024.110638","DOIUrl":"10.1016/j.ress.2024.110638","url":null,"abstract":"<div><div>Compound fault composed of coinstantaneous multiple faults frequently causes the failure of a manufacturing system, which greatly reduces the reliability. When measuring the compound fault, two difficulties generally exist: (1) the complex correlation between different single faults, and (2) collected target samples without labels. To accomplish the cross-domain unsupervised compound fault recognition, this study proposes a multi-label domain adversarial reinforcement learning (ML-DARL) framework that implements two multi-label deep reinforcement learning (ML-DRL) models with adversarial domain adaptation. First, a source ML-DRL model is adopted to train a source feature network (SFN) and a policy network by using a dataset with labels (source domain). Then, a discriminator and a target ML-DRL model that includes a target feature network (TFN) are jointly trained with adversarial adaptation by simultaneously using the dataset without labels (target domain) and the source domain. Specifically, two outputs of TFN and SFN are regarded as fake and real components, respectively. Notably, the reward function in the target ML-DRL model is related inversely to the output of the discriminator for the fake component. Finally, a cross-speed case and a cross-location case are executed to verify the adaptation ability of the proposed method on cross-domain unsupervised compound fault recognition.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"254 ","pages":"Article 110638"},"PeriodicalIF":9.4,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142697250","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}

引用次数: 0

A novel sim2real reinforcement learning algorithm for process control 用于过程控制的新型模拟真实强化学习算法

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2024-11-15 DOI: 10.1016/j.ress.2024.110639

Huiping Liang , Junyao Xie , Biao Huang , Yonggang Li , Bei Sun , Chunhua Yang

{"title":"A novel sim2real reinforcement learning algorithm for process control","authors":"Huiping Liang , Junyao Xie , Biao Huang , Yonggang Li , Bei Sun , Chunhua Yang","doi":"10.1016/j.ress.2024.110639","DOIUrl":"10.1016/j.ress.2024.110639","url":null,"abstract":"<div><div>While reinforcement learning (RL) has potential in advanced process control and optimization, its direct interaction with real industrial processes can pose safety concerns. Model-based pre-training of RL may alleviate such risks. However, the intricate nature of industrial processes complicates the establishment of entirely accurate simulation models. Consequently, RL-based controllers relying on simulation models can easily suffer from model-plant mismatch. On the one hand, utilizing offline data for pre-training of RL can also mitigate safety risks. However, it requires well-represented historical datasets. This is demanding because industrial processes mostly run under a regulatory mode with basic controllers. To handle these issues, this paper proposes a novel sim2real reinforcement learning algorithm. First, a state adaptor (SA) is proposed to align simulated states with real states to mitigate the model-plant mismatch. Then, a fix-horizon return is designed to replace traditional infinite-step return to provide genuine labels for the critic network, enhancing learning efficiency and stability. Finally, applying proximal policy optimization (PPO), the SA-PPO method is introduced to implement the proposed sim2real algorithm. Experimental results show that SA-PPO improves performance in MSE by 1.96% and in R by 21.64% on average for roasting process simulation. This verifies the effectiveness of the proposed method.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"254 ","pages":"Article 110639"},"PeriodicalIF":9.4,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142697245","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}

引用次数: 0