{"title":"Joint Optimization of Condition-Based Maintenance and Spare Parts Ordering for a Hidden Multi-State Deteriorating System","authors":"Xia Tang, Hui Xiao, Gang Kou, Yisha Xiang","doi":"10.1109/tr.2024.3385297","DOIUrl":"https://doi.org/10.1109/tr.2024.3385297","url":null,"abstract":"","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"45 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140840805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reliability analysis for structural systems relies on an accurate surrogate model. Currently, several multiple Kriging methods are utilized to calculate the failure probability. However, existing multiple Kriging methods for the reliability analysis have generally not incorporated model form selection into the modeling process, resulting in inaccurate probability of failure estimates. To overcome the shortcomings of existing multiple Kriging methods, this article presents an adaptive Bayesian model averaging-based multiple Kriging method. The proposed method utilizes Bayesian model averaging to incorporate an ensemble of individual Kriging models, each composed of different basis functions. The effect heredity principle is employed to enhance the model search efficiency, and the Occam's Window selection strategy is implemented to remove the Kriging models with poor prediction performance from the candidate set. For the final ensemble predictions, each single Kriging model is weighted based on its corresponding posterior model probability. Four benchmark examples are applied to validate the proposed new methods. Results revealed that the proposed method notably improves efficiency and accuracy estimates of failure probability.
{"title":"A Novel Adaptive Bayesian Model Averaging-Based Multiple Kriging Method for Structural Reliability Analysis","authors":"Manman Dong;Yongbo Cheng;Liangqi Wan","doi":"10.1109/TR.2024.3389959","DOIUrl":"10.1109/TR.2024.3389959","url":null,"abstract":"Reliability analysis for structural systems relies on an accurate surrogate model. Currently, several multiple Kriging methods are utilized to calculate the failure probability. However, existing multiple Kriging methods for the reliability analysis have generally not incorporated model form selection into the modeling process, resulting in inaccurate probability of failure estimates. To overcome the shortcomings of existing multiple Kriging methods, this article presents an adaptive Bayesian model averaging-based multiple Kriging method. The proposed method utilizes Bayesian model averaging to incorporate an ensemble of individual Kriging models, each composed of different basis functions. The effect heredity principle is employed to enhance the model search efficiency, and the Occam's Window selection strategy is implemented to remove the Kriging models with poor prediction performance from the candidate set. For the final ensemble predictions, each single Kriging model is weighted based on its corresponding posterior model probability. Four benchmark examples are applied to validate the proposed new methods. Results revealed that the proposed method notably improves efficiency and accuracy estimates of failure probability.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 1","pages":"2185-2199"},"PeriodicalIF":5.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140800200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
When cyber-physical systems (CPSs) are operational, its computing units frequently interact with complex and uncertain physical environments in time and space. To ensure the safety of the system, it is often necessary that the physical entities and information systems of CPS operate in a consistent manner at the temporal and spatial levels. However, most of the existing studies on spatio-temporal consistency modeling and verification of CPS are limited in the ability to deal with uncertainties. To address this issues, in this article, we propose a modeling and verification method for spatio-temporal consistency of CPS in uncertain environments. First, we propose a modeling language (stochastic spatio-temporal modeling language, SSTL) for the spatio-temporal domain of CPS. It can explicitly model the spatio-temporal constraints of CPS as well as deal with the spatio-temporal behavior of accompanying probabilities. Second, we propose a framework for spatio-temporal consistency verification. In the first step of this framework, we propose a worst-case time satisfiability algorithm to verifying the time safety of CPS. In the second step, we develop a prototype tool called “SSTL2NSHA” that is able to convert SSTL into the NHSA model supported by UPPAAL-statistical model checking (UPPAAL-SMC). Thereby the CPS model described by SSTL can be verified in UPPAAL-SMC for spatial safety constraints. Finally, we illustrate the effectiveness of the approach in this article with a traffic alert and collision avoidance system.
{"title":"Modeling and Verification Methods for Spatio-Temporal Consistency of CPS in Uncertain Environments","authors":"Shuqi Pan;Changjing Wang;Wuping Xie;Jiaxing Lu;Qing Huang;Zhengkang Zuo","doi":"10.1109/TR.2024.3384702","DOIUrl":"10.1109/TR.2024.3384702","url":null,"abstract":"When cyber-physical systems (CPSs) are operational, its computing units frequently interact with complex and uncertain physical environments in time and space. To ensure the safety of the system, it is often necessary that the physical entities and information systems of CPS operate in a consistent manner at the temporal and spatial levels. However, most of the existing studies on spatio-temporal consistency modeling and verification of CPS are limited in the ability to deal with uncertainties. To address this issues, in this article, we propose a modeling and verification method for spatio-temporal consistency of CPS in uncertain environments. First, we propose a modeling language (stochastic spatio-temporal modeling language, SSTL) for the spatio-temporal domain of CPS. It can explicitly model the spatio-temporal constraints of CPS as well as deal with the spatio-temporal behavior of accompanying probabilities. Second, we propose a framework for spatio-temporal consistency verification. In the first step of this framework, we propose a worst-case time satisfiability algorithm to verifying the time safety of CPS. In the second step, we develop a prototype tool called “SSTL2NSHA” that is able to convert SSTL into the NHSA model supported by UPPAAL-statistical model checking (UPPAAL-SMC). Thereby the CPS model described by SSTL can be verified in UPPAAL-SMC for spatial safety constraints. Finally, we illustrate the effectiveness of the approach in this article with a traffic alert and collision avoidance system.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"73 4","pages":"1849-1862"},"PeriodicalIF":5.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140800199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chenyang Ma;Ke Feng;Xianzhi Wang;Zhiqiang Cai;Yongbo Li
Bearing health monitoring (BHM) is vital in preventing unforeseen machinery shutdowns caused by frequent bearing failures. Within the BHM process, constructing health indicators takes center stage, serving the dual purpose of detecting incipient faults and assessing the monotonous degradation trend for predicting residual useful life. In terms of detecting incipient faults, permutation entropy (PE) serves as a promising tool due to its simplicity and rapid computation. However, when it comes to assessing irreversible degradation, PE often exhibits notable fluctuations and nonmonotonicity even after signal denoising processes. This issue arises from PE's vulnerability to impulsive noise and its invariance to monotonic signal transformations. To tackle this challenge, the article introduces a novel approach termed condition-adaptive permutation entropy (CAPE) for BHM. CAPE begins with a condition-based signal processing method to mitigate the influence of impulsive noise, followed by an amplitude-aware algorithm to break PE's invariance to monotonic signal processing. Moreover, CAPE adaptively selects fault-relevant permutation patterns to enhance its monotonicity. The effectiveness, superiority, and applicability of CAPE are rigorously demonstrated using simulation data and two experimental datasets.
{"title":"Condition-Adaptive Permutation Entropy: A Novel Dynamic Complexity-Based Health Indicator for Bearing Health Monitoring","authors":"Chenyang Ma;Ke Feng;Xianzhi Wang;Zhiqiang Cai;Yongbo Li","doi":"10.1109/TR.2024.3382121","DOIUrl":"10.1109/TR.2024.3382121","url":null,"abstract":"Bearing health monitoring (BHM) is vital in preventing unforeseen machinery shutdowns caused by frequent bearing failures. Within the BHM process, constructing health indicators takes center stage, serving the dual purpose of detecting incipient faults and assessing the monotonous degradation trend for predicting residual useful life. In terms of detecting incipient faults, permutation entropy (PE) serves as a promising tool due to its simplicity and rapid computation. However, when it comes to assessing irreversible degradation, PE often exhibits notable fluctuations and nonmonotonicity even after signal denoising processes. This issue arises from PE's vulnerability to impulsive noise and its invariance to monotonic signal transformations. To tackle this challenge, the article introduces a novel approach termed condition-adaptive permutation entropy (CAPE) for BHM. CAPE begins with a condition-based signal processing method to mitigate the influence of impulsive noise, followed by an amplitude-aware algorithm to break PE's invariance to monotonic signal processing. Moreover, CAPE adaptively selects fault-relevant permutation patterns to enhance its monotonicity. The effectiveness, superiority, and applicability of CAPE are rigorously demonstrated using simulation data and two experimental datasets.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 1","pages":"2394-2407"},"PeriodicalIF":5.0,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140629372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Many production systems undergo a multistate deterioration process before failure, during which inspection and repair are used to detect and remove the defects. Given the limitations of technology and random noise, inspection and repair are always imperfect. This study considered imperfect inspection and repair for a system subject to a three-stage degradation process. The concepts of virtual age and the improvement factor were adopted to characterize the imperfect repair effect. To verify the effectiveness of the proposed model, we applied it to the case of a steel converter plant and used the genetic algorithm to search for the optimal solution. The numerical results indicated that an optimal arrangement of inspection policy could significantly reduce maintenance costs.
{"title":"Optimal Inspection Policy for a Three-Stage System With Imperfect Inspection and Repair","authors":"Xia Tang;Hui Xiao;Gang Kou;Rui Peng","doi":"10.1109/TR.2024.3353755","DOIUrl":"10.1109/TR.2024.3353755","url":null,"abstract":"Many production systems undergo a multistate deterioration process before failure, during which inspection and repair are used to detect and remove the defects. Given the limitations of technology and random noise, inspection and repair are always imperfect. This study considered imperfect inspection and repair for a system subject to a three-stage degradation process. The concepts of virtual age and the improvement factor were adopted to characterize the imperfect repair effect. To verify the effectiveness of the proposed model, we applied it to the case of a steel converter plant and used the genetic algorithm to search for the optimal solution. The numerical results indicated that an optimal arrangement of inspection policy could significantly reduce maintenance costs.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"73 3","pages":"1669-1683"},"PeriodicalIF":5.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140616246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Data scarcity in prognostic and health management research presents a significant challenge, often hindering the performance of supervised models due to the difficulty of acquiring diverse fault mode data during prolonged faultless operation. Conversely, nominal operating condition (NOC) data, including both healthy and varied faulty data, are more readily available due to predelivery inspection. Subsequently, we study this novel and unresolved NOC premise that leverages NOC data along with healthy data from other conditions to construct a fault diagnoser called Res-1D-bootstrap your own latent (BYOL) with the proposed probability distribution generalization strategy. The initial step involves a novel approach to the contrastive transformation optimization with the criteria based solely on similarity loss obtained in the training stage. We then pretrain the fault detector based on our NOC premise, followed by finetuning the network exclusively with NOC data. Given the novelty of our premise, there are few models for direct comparison. Thus, we contrast our approach with a supervised baseline, MoCo, an unoptimized equivalent algorithm, and an equivalent algorithm that solely employs NOC data for pretraining the feature extractor. Empirical results demonstrate our model's superior distribution generalization capabilities through the improved classification accuracy across different operating conditions.
{"title":"Fault Diagnosis Generalization Improvement Through Contrastive Learning for a Multistage Centrifugal Pump","authors":"Jiapeng Wu;Diego Cabrera;Mariela Cerrada;René-Vinicio Sánchez;Fernando Sancho;Edgar Estupinan","doi":"10.1109/TR.2024.3381014","DOIUrl":"10.1109/TR.2024.3381014","url":null,"abstract":"Data scarcity in prognostic and health management research presents a significant challenge, often hindering the performance of supervised models due to the difficulty of acquiring diverse fault mode data during prolonged faultless operation. Conversely, nominal operating condition (NOC) data, including both healthy and varied faulty data, are more readily available due to predelivery inspection. Subsequently, we study this novel and unresolved NOC premise that leverages NOC data along with healthy data from other conditions to construct a fault diagnoser called Res-1D-bootstrap your own latent (BYOL) with the proposed probability distribution generalization strategy. The initial step involves a novel approach to the contrastive transformation optimization with the criteria based solely on similarity loss obtained in the training stage. We then pretrain the fault detector based on our NOC premise, followed by finetuning the network exclusively with NOC data. Given the novelty of our premise, there are few models for direct comparison. Thus, we contrast our approach with a supervised baseline, MoCo, an unoptimized equivalent algorithm, and an equivalent algorithm that solely employs NOC data for pretraining the feature extractor. Empirical results demonstrate our model's superior distribution generalization capabilities through the improved classification accuracy across different operating conditions.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 1","pages":"2373-2381"},"PeriodicalIF":5.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140616331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Daniel Z. Herr;Radislav Vaisman;Mitchell Scovell;Nikolai Kinaev
Due to stochastic gamma processes adaptability, they are now widely used to mimic a variety of degradation events. However, in certain situations, measurement errors are present in degradation data, and an intractable probability setting is emerging. When completing inference tasks, its intractableness causes significant practical difficulty. In order to overcome the difficulty of producing MLEs and the related confidence intervals for the model parameters, we propose a new technique. The rare-event problem, which has a significant influence on the estimator efficiency and, consequently, on the whole inference process, plagues previously employed Monte Carlo approaches for intractable likelihood estimation. We suggest using an alternative Monte Carlo method to address this, while avoiding the establishment of a rare-event issue. The cross-entropy optimization approach, which can handle objective functions that are tainted by noise, is then added to this technique. We demonstrate that the suggested mix can be implemented within an acceptable computation time and lays the foundation for efficient, generic, and scalable inference processes under the intractable likelihood scenario. Our results show that, given the stochastic gamma process degradation model assumption, the proposed technique may yield high-quality inference results.
{"title":"On Alternative Monte Carlo Methods for Parameter Estimation in Gamma Process Models With Intractable Likelihood","authors":"Daniel Z. Herr;Radislav Vaisman;Mitchell Scovell;Nikolai Kinaev","doi":"10.1109/TR.2024.3381126","DOIUrl":"10.1109/TR.2024.3381126","url":null,"abstract":"Due to stochastic gamma processes adaptability, they are now widely used to mimic a variety of degradation events. However, in certain situations, measurement errors are present in degradation data, and an intractable probability setting is emerging. When completing inference tasks, its intractableness causes significant practical difficulty. In order to overcome the difficulty of producing MLEs and the related confidence intervals for the model parameters, we propose a new technique. The rare-event problem, which has a significant influence on the estimator efficiency and, consequently, on the whole inference process, plagues previously employed Monte Carlo approaches for intractable likelihood estimation. We suggest using an alternative Monte Carlo method to address this, while avoiding the establishment of a rare-event issue. The cross-entropy optimization approach, which can handle objective functions that are tainted by noise, is then added to this technique. We demonstrate that the suggested mix can be implemented within an acceptable computation time and lays the foundation for efficient, generic, and scalable inference processes under the intractable likelihood scenario. Our results show that, given the stochastic gamma process degradation model assumption, the proposed technique may yield high-quality inference results.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 1","pages":"2118-2132"},"PeriodicalIF":5.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140616199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Heavy-duty gas turbines are key engines for clean energy utilization and efficient conversion in natural gas power plants. Gas-path components are the components with the highest failure rate in gas turbines, and their faults are highly hidden and destructive. In response to the shortcomings of existing gas-path diagnostic methods, a machine-learning-based diagnostic method for all gas-path components with the aid of thermodynamic model was proposed for the first time. A comprehensive rule base was established for the relationship between the internal fault modes of gas-path components and the external fault symptoms of gas-path measurable parameters. A mathematical model for all gas-path component fault diagnosis suitable for machine learning framework was established. The proposed method can be used to comprehensively diagnose the different types and severity of faults in all gas-path components under various operating conditions after grid connection. Case analysis shows that the proposed method can achieve a success rate of 100% for diagnosing different types of faults and can achieve an overall success rate of over 97% for diagnosing the types and severity of faults under a few base sample conditions. and the accuracy of fault diagnosis has increased at least by 3.4%. The proposed approach has excellent diagnostic accuracy and real-time performance.
{"title":"A Novel Machine Learning Based Fault Diagnosis Method for All Gas-Path Components of Heavy Duty Gas Turbines With the Aid of Thermodynamic Model","authors":"Jingchao Li;Yulong Ying","doi":"10.1109/TR.2024.3383922","DOIUrl":"10.1109/TR.2024.3383922","url":null,"abstract":"Heavy-duty gas turbines are key engines for clean energy utilization and efficient conversion in natural gas power plants. Gas-path components are the components with the highest failure rate in gas turbines, and their faults are highly hidden and destructive. In response to the shortcomings of existing gas-path diagnostic methods, a machine-learning-based diagnostic method for all gas-path components with the aid of thermodynamic model was proposed for the first time. A comprehensive rule base was established for the relationship between the internal fault modes of gas-path components and the external fault symptoms of gas-path measurable parameters. A mathematical model for all gas-path component fault diagnosis suitable for machine learning framework was established. The proposed method can be used to comprehensively diagnose the different types and severity of faults in all gas-path components under various operating conditions after grid connection. Case analysis shows that the proposed method can achieve a success rate of 100% for diagnosing different types of faults and can achieve an overall success rate of over 97% for diagnosing the types and severity of faults under a few base sample conditions. and the accuracy of fault diagnosis has increased at least by 3.4%. The proposed approach has excellent diagnostic accuracy and real-time performance.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"73 4","pages":"1805-1818"},"PeriodicalIF":5.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140564822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The process of transferring land records (LRs) and ownership between users is facilitated by a LR transfer system. This system encompasses a series of procedures, including conducting title search, establishing agreements, executing legal documentation, verifying and transferring ownership, and updating LRs. Despite its importance, the system encounters notable challenges, such as insufficient tamper-proof record-keeping, lack of system compatibility, time-consuming processes, and the presence of intermediaries and brokers leading to potentially fraudulent claims. To address these challenges, a novel solution called LandChain is proposed in this article. The LandChain utilizes MultiChain, consisting of MainChain and SideChain, to securely transfer LRs among users, such as buyers, sellers, land donors, and owners. The LandChain incorporates innovative algorithms like record forwarder selection (RFS), trust establishment (TE), and record transfer and confirmation (RTC). Furthermore, LandChain verifies the legitimacy of users before transferring LRs through the verify user legitimacy algorithm. Security analysis shows LandChain is secure from double-spending, liveness, Sybil, replay, and man-in-the-middle attacks. The implementation of LandChain is developed and tested on the docker engine platform. According to performance analysis, the LandChain reduces record confirmation latency by 18% (MultiChain) and 50% (Blockchain). LandChain also increases throughput by 34% (MultiChain) and 45% (Blockchain) when compared to state-of-the-art approaches.
{"title":"LandChain: A MultiChain Based Novel Secure Land Record Transfer System","authors":"Amritesh Kumar;Lokendra Vishwakarma;Debasis Das","doi":"10.1109/TR.2024.3382490","DOIUrl":"10.1109/TR.2024.3382490","url":null,"abstract":"The process of transferring land records (LRs) and ownership between users is facilitated by a LR transfer system. This system encompasses a series of procedures, including conducting title search, establishing agreements, executing legal documentation, verifying and transferring ownership, and updating LRs. Despite its importance, the system encounters notable challenges, such as insufficient tamper-proof record-keeping, lack of system compatibility, time-consuming processes, and the presence of intermediaries and brokers leading to potentially fraudulent claims. To address these challenges, a novel solution called <italic>LandChain</i> is proposed in this article. The <italic>LandChain</i> utilizes MultiChain, consisting of MainChain and SideChain, to securely transfer LRs among users, such as buyers, sellers, land donors, and owners. The <italic>LandChain</i> incorporates innovative algorithms like record forwarder selection (RFS), trust establishment (TE), and record transfer and confirmation (RTC). Furthermore, <italic>LandChain</i> verifies the legitimacy of users before transferring LRs through the verify user legitimacy algorithm. Security analysis shows <italic>LandChain</i> is secure from double-spending, liveness, Sybil, replay, and man-in-the-middle attacks. The implementation of <italic>LandChain</i> is developed and tested on the docker engine platform. According to performance analysis, the <italic>LandChain</i> reduces record confirmation latency by 18% (MultiChain) and 50% (Blockchain). <italic>LandChain</i> also increases throughput by 34% (MultiChain) and 45% (Blockchain) when compared to state-of-the-art approaches.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 1","pages":"2320-2332"},"PeriodicalIF":5.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140596247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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