Pub Date : 2023-11-13DOI: 10.1080/09617353.2023.2264729
Björn Klamann, Hermann Winner
AbstractThe concept of a modular safety approval for automated vehicles dispenses with tests on vehicle or system level. Individually approved modules can be updated and reused without requiring new safety approvals. Similar to a system’s operational design domain description, an environmental description is required for a safety approval on module level. This paper presents how the environment of a module can be described at module interfaces. Uncertainty about other modules’ behaviour, dependencies between modules, and impacts of their outputs on the system behaviour are key reasons for missing specifications or tests of existing methods, leading to an erroneous approval of modules. To reduce uncertainties, we expand the state-of-the-art syntactical and semantic interface description and additionally describe dependencies to other modules’ behaviour or conditions and impacts of their outputs. The resulting detailed semantic interface description is categorised into syntax, semantics, influencing factors, and impacts. The novel description structure is a condensed way to consider the behaviour and its impacts on other modules in module development and testing. The description fundamentally supports the modular safety approval by identifying stimuli usually only seen during integration.Keywords: Safety approvalvalidationautomated drivingautonomous vehiclesmodularityinterfaceUNICARagil AcknowledgementThis research is accomplished within the project ‘UNICARagil’ (FKZ 16EMO0286).Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementAll data analysed during this study are included in the Appendix of this published article.Additional informationFundingWe acknowledge the financial support for the projects by the Federal Ministry of Education and Research of Germany (BMBF) based on a decision of the Deutsche Bundestag.Notes on contributorsBjörn KlamannBjörn Klamann finished his Master of Science Degree in Mechanical and Process Engineering at Technical University of Darmstadt. Since 2018 he is a research assistant at the Institute of Automotive Engineering at Technical University of Darmstadt. In his main research topic, the safety of automated vehicles, he investigates the approach of a modular safety approval.Hermann WinnerHermann Winner began working at Robert Bosch GmbH in 1987, after receiving his PhD in physics, focusing on the predevelopment of ‘by-wire’ technology and Adaptive Cruise Control (ACC). Beginning in 1995, he led the series development of ACC up to the start of production. Since 2002, he has been pursuing the research of systems engineering topics for driver assistance systems and automated driving as Professor of Automotive Engineering at the Technical University of Darmstadt. He discovered the ‘approval trap’ of autonomous driving, the still unsolved challenge to validate safety of autonomous driving before market introduction.
{"title":"Introducing the detailed semantic interface description to support a modular safety approval of automated vehicles – S <sup>2</sup> I <sup>2</sup>","authors":"Björn Klamann, Hermann Winner","doi":"10.1080/09617353.2023.2264729","DOIUrl":"https://doi.org/10.1080/09617353.2023.2264729","url":null,"abstract":"AbstractThe concept of a modular safety approval for automated vehicles dispenses with tests on vehicle or system level. Individually approved modules can be updated and reused without requiring new safety approvals. Similar to a system’s operational design domain description, an environmental description is required for a safety approval on module level. This paper presents how the environment of a module can be described at module interfaces. Uncertainty about other modules’ behaviour, dependencies between modules, and impacts of their outputs on the system behaviour are key reasons for missing specifications or tests of existing methods, leading to an erroneous approval of modules. To reduce uncertainties, we expand the state-of-the-art syntactical and semantic interface description and additionally describe dependencies to other modules’ behaviour or conditions and impacts of their outputs. The resulting detailed semantic interface description is categorised into syntax, semantics, influencing factors, and impacts. The novel description structure is a condensed way to consider the behaviour and its impacts on other modules in module development and testing. The description fundamentally supports the modular safety approval by identifying stimuli usually only seen during integration.Keywords: Safety approvalvalidationautomated drivingautonomous vehiclesmodularityinterfaceUNICARagil AcknowledgementThis research is accomplished within the project ‘UNICARagil’ (FKZ 16EMO0286).Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementAll data analysed during this study are included in the Appendix of this published article.Additional informationFundingWe acknowledge the financial support for the projects by the Federal Ministry of Education and Research of Germany (BMBF) based on a decision of the Deutsche Bundestag.Notes on contributorsBjörn KlamannBjörn Klamann finished his Master of Science Degree in Mechanical and Process Engineering at Technical University of Darmstadt. Since 2018 he is a research assistant at the Institute of Automotive Engineering at Technical University of Darmstadt. In his main research topic, the safety of automated vehicles, he investigates the approach of a modular safety approval.Hermann WinnerHermann Winner began working at Robert Bosch GmbH in 1987, after receiving his PhD in physics, focusing on the predevelopment of ‘by-wire’ technology and Adaptive Cruise Control (ACC). Beginning in 1995, he led the series development of ACC up to the start of production. Since 2002, he has been pursuing the research of systems engineering topics for driver assistance systems and automated driving as Professor of Automotive Engineering at the Technical University of Darmstadt. He discovered the ‘approval trap’ of autonomous driving, the still unsolved challenge to validate safety of autonomous driving before market introduction.","PeriodicalId":45573,"journal":{"name":"International Journal of Reliability Quality and Safety Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136282027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-10DOI: 10.1142/s0218539323500365
Sergiy Begun, Vasilij Begun
{"title":"A SaaS Concept Based Shopping Center Fire Risk Assessment Model for the Safety Management Applications","authors":"Sergiy Begun, Vasilij Begun","doi":"10.1142/s0218539323500365","DOIUrl":"https://doi.org/10.1142/s0218539323500365","url":null,"abstract":"","PeriodicalId":45573,"journal":{"name":"International Journal of Reliability Quality and Safety Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135186419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-04DOI: 10.1142/s0218539323500328
Yoshinobu Tamura, Shoichiro Miyamoto, Lei Zhou, Shigeru Yamada
This paper focuses on the sustainability based on the effort by using the fault big data of open source software (OSS). The fault detection phenomenon depends on the maintenance effort, because the number of software fault is influenced by the effort expenditure. Actually, the software reliability growth models with testing-effort have been proposed in the past. In this paper, we apply the deep learning approach to the OSS fault big data. Also, we propose the reliability assessment measure of sustainability. Then, we show several sustainability assessment measure based on the deep learning. Moreover, several numerical illustrations based on the proposed deep learning model are shown in this paper.
{"title":"OSS Sustainability Assessment Based on the Deep Learning Considering Effort Wiener Process Data","authors":"Yoshinobu Tamura, Shoichiro Miyamoto, Lei Zhou, Shigeru Yamada","doi":"10.1142/s0218539323500328","DOIUrl":"https://doi.org/10.1142/s0218539323500328","url":null,"abstract":"This paper focuses on the sustainability based on the effort by using the fault big data of open source software (OSS). The fault detection phenomenon depends on the maintenance effort, because the number of software fault is influenced by the effort expenditure. Actually, the software reliability growth models with testing-effort have been proposed in the past. In this paper, we apply the deep learning approach to the OSS fault big data. Also, we propose the reliability assessment measure of sustainability. Then, we show several sustainability assessment measure based on the deep learning. Moreover, several numerical illustrations based on the proposed deep learning model are shown in this paper.","PeriodicalId":45573,"journal":{"name":"International Journal of Reliability Quality and Safety Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135728300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1142/s0218539323500316
Dong-Ryeol Shin, Gayoung Chae, Minjae Park
In this study, AdaBoost-Bi-LSTM ensemble models are developed to predict the number of COVID-19 confirmed cases by effectively learning volatile and unstable data using a nonparametric method. The performance of the developed models in terms of prediction accuracy is compared with those of existing deep learning models such as GRU, LSTM, and Bi-LSTM. The COVID-19 outbreak in 2019 has resulted in a global pandemic with a significant number of deaths worldwide. There have long been ongoing efforts to prevent the spread of infectious diseases, and a number of prediction models have been developed for the number of confirmed cases. However, there are many variables that continuously mutate the virus and therefore affect the number of confirmed cases, which makes it difficult to accurately predict the number of COVID-19 confirmed cases. The goal of this study is to develop a model with a lower error rate and higher predictive accuracy than existing models to more effectively monitor and handle endemic diseases. To this end, this study predicts COVID-19 confirmed cases from April to October 2022 based on the analysis of COVID-19 confirmed cases data from 16 December 2020 to 27 September 2022 using the developed models. As a result, the AdaBoost-Bi-LSTM model shows the best performance, even though the data from the period of high variability in the number of confirmed cases was used for model training. The AdaBoost-Bi-LSTM model achieved improved predictive power and shows an increased performance of 17.41% over the simple GRU/LSTM model and of 15.62% over the Bi-LSTM model.
{"title":"A Study on the Prediction of COVID-19 Confirmed Cases Using Deep Learning and AdaBoost-Bi-LSTM model","authors":"Dong-Ryeol Shin, Gayoung Chae, Minjae Park","doi":"10.1142/s0218539323500316","DOIUrl":"https://doi.org/10.1142/s0218539323500316","url":null,"abstract":"In this study, AdaBoost-Bi-LSTM ensemble models are developed to predict the number of COVID-19 confirmed cases by effectively learning volatile and unstable data using a nonparametric method. The performance of the developed models in terms of prediction accuracy is compared with those of existing deep learning models such as GRU, LSTM, and Bi-LSTM. The COVID-19 outbreak in 2019 has resulted in a global pandemic with a significant number of deaths worldwide. There have long been ongoing efforts to prevent the spread of infectious diseases, and a number of prediction models have been developed for the number of confirmed cases. However, there are many variables that continuously mutate the virus and therefore affect the number of confirmed cases, which makes it difficult to accurately predict the number of COVID-19 confirmed cases. The goal of this study is to develop a model with a lower error rate and higher predictive accuracy than existing models to more effectively monitor and handle endemic diseases. To this end, this study predicts COVID-19 confirmed cases from April to October 2022 based on the analysis of COVID-19 confirmed cases data from 16 December 2020 to 27 September 2022 using the developed models. As a result, the AdaBoost-Bi-LSTM model shows the best performance, even though the data from the period of high variability in the number of confirmed cases was used for model training. The AdaBoost-Bi-LSTM model achieved improved predictive power and shows an increased performance of 17.41% over the simple GRU/LSTM model and of 15.62% over the Bi-LSTM model.","PeriodicalId":45573,"journal":{"name":"International Journal of Reliability Quality and Safety Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135011741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-30DOI: 10.1080/09617353.2023.2263728
Leila Omidi, Hossein Karimi, Gholamreza Moradi
AbstractThe current study aimed to, firstly, assess the roles of crisis management systems, resilience engineering, and proactive risk management in emergency management of high-risk manufacturing industry and, secondly, to compute the relative contribution of each factor by the entropy approach. Data were collected using three questionnaires. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was administered to rank study alternatives, which include managers at different hierarchical levels encompassing senior managers, middle‐level managers, and operating-level managers. The results of the entropy method considering crisis management data suggested that human and organisational aspects had the highest impact on emergency management. The highest percentages of influence considering resilience engineering factors were associated with flexibility and management commitment to safety. Among proactive risk management dimensions, training and communication about safety and risks were the most influential dimensions. TOPSIS results demonstrated that there are some gaps in the emergency management system of the plant from the operating managers’ perspectives. This means that operating managers believed that the emergency management system and resilience level should be improved in the plant to enhance the levels of safety and emergency risk management of the industry.Keywords: Emergency managementresilience engineeringproactive risk managemententropyTOPSIS AcknowledgementsThe authors would also like to thank the management of the study industry for their participation.Disclosure statementThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.Additional informationFundingThis research was funded by Tabriz University of Medical Sciences [grant number: 65781; the ethical code: IR.TBZMED.REC.1399.716].Notes on contributorsLeila OmidiLeila Omidi is an assistant professor at the Department of Occupational Health Engineering, Tehran University of Medical Sciences, Iran. Her research focuses on process safety, safety behaviour, and human factors influencing safety.Hossein KarimiHossein Karimi holds an MSc in Health, Safety, and Environment (HSE) from Tabriz University of Medical Sciences, Iran. His research interests include organizational safety, occupational safety, and safety behavior.Gholamreza MoradiGholamreza Moradi is an assistant professor at the Department of Occupational Health Engineering, Tabriz University of Medical Sciences, Iran. His research interests include occupational health and safety.
{"title":"Assessment of emergency risk management and resilience engineering at management levels of a high hazard industry","authors":"Leila Omidi, Hossein Karimi, Gholamreza Moradi","doi":"10.1080/09617353.2023.2263728","DOIUrl":"https://doi.org/10.1080/09617353.2023.2263728","url":null,"abstract":"AbstractThe current study aimed to, firstly, assess the roles of crisis management systems, resilience engineering, and proactive risk management in emergency management of high-risk manufacturing industry and, secondly, to compute the relative contribution of each factor by the entropy approach. Data were collected using three questionnaires. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was administered to rank study alternatives, which include managers at different hierarchical levels encompassing senior managers, middle‐level managers, and operating-level managers. The results of the entropy method considering crisis management data suggested that human and organisational aspects had the highest impact on emergency management. The highest percentages of influence considering resilience engineering factors were associated with flexibility and management commitment to safety. Among proactive risk management dimensions, training and communication about safety and risks were the most influential dimensions. TOPSIS results demonstrated that there are some gaps in the emergency management system of the plant from the operating managers’ perspectives. This means that operating managers believed that the emergency management system and resilience level should be improved in the plant to enhance the levels of safety and emergency risk management of the industry.Keywords: Emergency managementresilience engineeringproactive risk managemententropyTOPSIS AcknowledgementsThe authors would also like to thank the management of the study industry for their participation.Disclosure statementThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.Additional informationFundingThis research was funded by Tabriz University of Medical Sciences [grant number: 65781; the ethical code: IR.TBZMED.REC.1399.716].Notes on contributorsLeila OmidiLeila Omidi is an assistant professor at the Department of Occupational Health Engineering, Tehran University of Medical Sciences, Iran. Her research focuses on process safety, safety behaviour, and human factors influencing safety.Hossein KarimiHossein Karimi holds an MSc in Health, Safety, and Environment (HSE) from Tabriz University of Medical Sciences, Iran. His research interests include organizational safety, occupational safety, and safety behavior.Gholamreza MoradiGholamreza Moradi is an assistant professor at the Department of Occupational Health Engineering, Tabriz University of Medical Sciences, Iran. His research interests include occupational health and safety.","PeriodicalId":45573,"journal":{"name":"International Journal of Reliability Quality and Safety Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136022816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-27DOI: 10.1142/s0218539323500353
Kuen-Suan Chen, Chun-Min Yu, Chi-Han Chen
{"title":"Smart Quality Decision-Making Model for Mobile Assistive Devices","authors":"Kuen-Suan Chen, Chun-Min Yu, Chi-Han Chen","doi":"10.1142/s0218539323500353","DOIUrl":"https://doi.org/10.1142/s0218539323500353","url":null,"abstract":"","PeriodicalId":45573,"journal":{"name":"International Journal of Reliability Quality and Safety Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136312484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-21DOI: 10.1142/s0218539323500274
Ayman M. Abd-Elrahman
In 2017, A. M. Abd-Elrahman [A new two-parameter lifetime distribution with decreasing, increasing or upside-down bathtub-shaped failure rate, Commun. Stat. - Theory Methods 46 (2017) 8865–8880, doi:10.1080/03610926.2016.1193198] introduced a generalization of the Bilal distribution, where a new two-parameter distribution, generalized Bilal distribution (GBD), was presented. He showed that its failure rate function can be upside-down bathtub-shaped. The failure rate can either be decreasing or increasing due to some mathematical and statistical reasons, which will be given below. In this paper, we introduce a simple and better alternative to the GBD, which will be denoted by WMD. We show that the WMD is a two-parameter distribution which can fit five different types of data sets with respect to their empirical hazard rate functions. Most properties of the WMD are investigated. Point and interval estimation procedures for the two unknown parameters are presented. The existence and uniqueness of the maximum likelihood estimates are proved. The moment estimates are obtained and we showed that one of these estimates is the minimum variance unbiased estimate (MVUE) for its corresponding parameter. A simulation study is provided and the paper is motivated by applications to four different real data sets. A detailed analysis for the Meeker and Escobar data is provided by the book of Meeker and Escobar [Statistical Methods for Reliability Data, 2nd edn. (John Wiley, 1998)]. The results may show that the new distribution provides a better fit than some other most recent existing and already known distributions in the literature. Finally, some concluding remarks are presented.
2017, A. M. Abd-Elrahman[浴缸形故障率降低、增加或倒置的新双参数寿命分布]，文献。Stat. - Theory Methods 46 (2017) 8865-8880, doi:10.1080/03610926.2016.1193198]引入了Bilal分布的泛化，其中提出了一种新的双参数分布，即广义Bilal分布(GBD)。他展示了它的故障率函数可以是倒置的浴缸形状。由于一些数学和统计原因，故障率可能会降低或增加，下面将给出这些原因。在本文中，我们介绍了一种简单而更好的替代GBD的方法，用WMD表示。我们证明了WMD是一个双参数分布，它可以拟合五种不同类型的数据集的经验危险率函数。研究了大规模杀伤性武器的大多数性质。给出了两个未知参数的点估计和区间估计方法。证明了极大似然估计的存在唯一性。得到了矩估计，并证明了其中一个估计是相应参数的最小方差无偏估计(MVUE)。本文通过对四个不同的真实数据集的应用进行了仿真研究。对Meeker和Escobar数据的详细分析由Meeker和Escobar的书[可靠性数据的统计方法，第2版]提供。(约翰·威利，1998)。结果可能表明，新的分布比文献中其他一些最新存在的和已知的分布提供了更好的拟合。最后，本文作了总结。
{"title":"A Better Alternative to the Generalized Bilal Distribution: A New Model and Applications","authors":"Ayman M. Abd-Elrahman","doi":"10.1142/s0218539323500274","DOIUrl":"https://doi.org/10.1142/s0218539323500274","url":null,"abstract":"In 2017, A. M. Abd-Elrahman [A new two-parameter lifetime distribution with decreasing, increasing or upside-down bathtub-shaped failure rate, Commun. Stat. - Theory Methods 46 (2017) 8865–8880, doi:10.1080/03610926.2016.1193198] introduced a generalization of the Bilal distribution, where a new two-parameter distribution, generalized Bilal distribution (GBD), was presented. He showed that its failure rate function can be upside-down bathtub-shaped. The failure rate can either be decreasing or increasing due to some mathematical and statistical reasons, which will be given below. In this paper, we introduce a simple and better alternative to the GBD, which will be denoted by WMD. We show that the WMD is a two-parameter distribution which can fit five different types of data sets with respect to their empirical hazard rate functions. Most properties of the WMD are investigated. Point and interval estimation procedures for the two unknown parameters are presented. The existence and uniqueness of the maximum likelihood estimates are proved. The moment estimates are obtained and we showed that one of these estimates is the minimum variance unbiased estimate (MVUE) for its corresponding parameter. A simulation study is provided and the paper is motivated by applications to four different real data sets. A detailed analysis for the Meeker and Escobar data is provided by the book of Meeker and Escobar [Statistical Methods for Reliability Data, 2nd edn. (John Wiley, 1998)]. The results may show that the new distribution provides a better fit than some other most recent existing and already known distributions in the literature. Finally, some concluding remarks are presented.","PeriodicalId":45573,"journal":{"name":"International Journal of Reliability Quality and Safety Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135463674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-11DOI: 10.1080/09617353.2023.2263727
Peter Okoh
AbstractFunctional safety has experienced evolution over the years aimed at further risk reduction in society. Changes have taken place in the form of the creation of new domain-specific standards such as ISO 26262 (automotive), EN 50129 (railway), ISO 13489 (machinery), etc. from the parent IEC 61508 standard. Besides, these standards also undergo periodic revisions to keep abreast of innovations in technology. As the technological space expands and increases in complexity, it needs more than procedural, passive and active risk reduction strategies to achieve optimal risk reduction due to potential deficiencies with the use of instruction manuals and physical safety barriers. Inherently safer design (ISD) is expected to bring about a consolidated and cost-effective risk reduction since it does not require the installation of degradable add-on features and can be applied across the product development life cycle. Hence, this paper aims to apply ISD to the functional safety aspect of safety system development according to IEC 61508. The paper focuses on hardware design and does not cover all aspects of active safety system design. The main objective is to investigate how ISD can reduce risk by reducing random and systematic failures. The paper builds on the review of literature and standards.Keywords: Inherent safetyfunctional safetyIEC 61508 Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsPeter OkohPeter Okoh holds a PhD in Reliability, Availability, Maintainability and Safety (RAMS). He studied at the Department of Mechanical and Industrial Engineering, at Norwegian University of Science and Technology, Trondheim, Norway.
{"title":"The application of inherent safety to functional safety","authors":"Peter Okoh","doi":"10.1080/09617353.2023.2263727","DOIUrl":"https://doi.org/10.1080/09617353.2023.2263727","url":null,"abstract":"AbstractFunctional safety has experienced evolution over the years aimed at further risk reduction in society. Changes have taken place in the form of the creation of new domain-specific standards such as ISO 26262 (automotive), EN 50129 (railway), ISO 13489 (machinery), etc. from the parent IEC 61508 standard. Besides, these standards also undergo periodic revisions to keep abreast of innovations in technology. As the technological space expands and increases in complexity, it needs more than procedural, passive and active risk reduction strategies to achieve optimal risk reduction due to potential deficiencies with the use of instruction manuals and physical safety barriers. Inherently safer design (ISD) is expected to bring about a consolidated and cost-effective risk reduction since it does not require the installation of degradable add-on features and can be applied across the product development life cycle. Hence, this paper aims to apply ISD to the functional safety aspect of safety system development according to IEC 61508. The paper focuses on hardware design and does not cover all aspects of active safety system design. The main objective is to investigate how ISD can reduce risk by reducing random and systematic failures. The paper builds on the review of literature and standards.Keywords: Inherent safetyfunctional safetyIEC 61508 Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsPeter OkohPeter Okoh holds a PhD in Reliability, Availability, Maintainability and Safety (RAMS). He studied at the Department of Mechanical and Industrial Engineering, at Norwegian University of Science and Technology, Trondheim, Norway.","PeriodicalId":45573,"journal":{"name":"International Journal of Reliability Quality and Safety Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136211296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-10DOI: 10.1142/s0218539323500286
Sunita Sharma, Vinod Kumar
This paper proposes the Bayesian paradigm to analyze the reliability characteristics of a [Formula: see text]-out-of-[Formula: see text] system consisting of [Formula: see text] independent and identically distributed components, using Weighted Exponential-Lindley distribution as failure times. The Bayesian approach is utilized to estimate reliability characteristics like system reliability and mean time to system failure. Lindley’s approximation is employed along with Jeffery prior under a squared error loss function to obtain the estimators for these reliability measures. A simulation study is carried out for comparing the performances of these estimators. Finally, a real data set is used to illustrate the findings.
{"title":"Bayesian Analysis of <i>k</i>-out-of-<i>n</i> System using Weighted Exponential Lindley Distribution","authors":"Sunita Sharma, Vinod Kumar","doi":"10.1142/s0218539323500286","DOIUrl":"https://doi.org/10.1142/s0218539323500286","url":null,"abstract":"This paper proposes the Bayesian paradigm to analyze the reliability characteristics of a [Formula: see text]-out-of-[Formula: see text] system consisting of [Formula: see text] independent and identically distributed components, using Weighted Exponential-Lindley distribution as failure times. The Bayesian approach is utilized to estimate reliability characteristics like system reliability and mean time to system failure. Lindley’s approximation is employed along with Jeffery prior under a squared error loss function to obtain the estimators for these reliability measures. A simulation study is carried out for comparing the performances of these estimators. Finally, a real data set is used to illustrate the findings.","PeriodicalId":45573,"journal":{"name":"International Journal of Reliability Quality and Safety Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136254757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-29DOI: 10.1142/s021853932350033x
Sudhansu S. Maiti, Amartya Bhattacharya, Mriganka Mouli Choudhury, Arindam Gupta
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