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Conference

International Conference on Reliability, Maintainability and Safety 

About: International Conference on Reliability, Maintainability and Safety is an academic conference. The conference publishes majorly in the area(s): Reliability (statistics) & Reliability theory. Over the lifetime, 932 publications have been published by the conference receiving 2627 citations.

Papers published on a yearly basis

Papers
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Proceedings ArticleDOI
12 Jun 2011
TL;DR: This paper presents a simple classification schema for MBSA techniques based on two criteria — provenance of the model and engineering semantics of component dependencies captured by the model.
Abstract: Since its emergence in 1990s, Model-Based Safety Assessment (MBSA) has enjoyed significant interest from both academia and industry. The last decade has seen not only the development of a number of methods, techniques and tools, but also the gradual adoption of MBSA techniques by industry and its acceptance by regulators. However, the field of MBSA encompasses a large number of fundamentally dissimilar techniques. This paper presents a simple classification schema for MBSA techniques based on two criteria — provenance of the model and engineering semantics of component dependencies captured by the model. The classification organizes the existing techniques into a number of coherent families. Applicability, limitations and challenges of most prominent families of MBSA techniques are presented, and some of the common challenges faced by MBSA discipline are discussed.

63 citations

Proceedings ArticleDOI
20 Jul 2009
TL;DR: In this paper, the frequent failure modes and mechanisms such as chip failure and packaging failure were investigated through several failure analysis cases, and several methods to improve LED's reliability are reported in this paper.
Abstract: The reliability of LED is key to its application system. In this paper, the frequent failure modes and mechanisms such as chip failure and packaging failure were investigated through several failure analysis cases. Based on these analysis results, several methods to improve LED's reliability are reported in this paper.

44 citations

Proceedings ArticleDOI
01 Oct 2018
TL;DR: A battery RUL prediction approach based on a new recurrent neural network (RNN), i.e. the RNN with Gated Recurrent Unit (GRU) is proposed which overcomes the drawback on dealing with long term relationship of RNN.
Abstract: Lithium-ion battery has been widely applied as an energy storage component in various industrial applications including electric vehicles, distributed grids and space crafts. However, the battery performance degrades gradually due to the SEI growth, li-plating and other irreversible electro-chemical reactions. These inevitable reactions directly influence the reliability of the energy storage system and may further cause catastrophic consequences to the host system. Remaining useful life (RUL) is one of critical indicators to evaluate the battery performance. This paper proposes a battery RUL prediction approach based on a new recurrent neural network (RNN), i.e. the RNN with Gated Recurrent Unit (GRU). The proposed method overcomes the drawback on dealing with long term relationship of RNN. The structure of the RNN-GRU is much simpler which contributes to a higher computational complexity. The experiments based on the NMC lithium-ion battery cycle life testing data are conducted and the results indicate that the mean error of different battery cells are both less than 3% which means the proposed method is accurate and robust for battery RUL predictions.

30 citations

Proceedings ArticleDOI
01 Aug 2014
TL;DR: A generic definition of cloud computing system dependability, which including availability, performability, security, recoverability and so on is provided, and a novel simulating approach is proposed with well-designed framework and procedure.
Abstract: With the continuous growth of application requirements and a significant advance in the research of cloud computing systems, a large number of cloud computing systems nowadays based on different structures and virtualization technologies are still being developed. However, dependability of cloud computing system is always a critical issue for all the cloud service providers, brokers, carriers and consumers around the world. How to ensure the dependability of cloud computing systems is still not well solved by former researchers. This paper first introduces the infrastructure of a cloud computing system and its major actors. Based on the research of the running mode of a cloud computing system, main influencing factors of cloud computing dependability and its normal failure modes are given. It provides a generic definition of cloud computing system dependability, which including availability, performability, security, recoverability and so on. We also discuss some methods to establish cloud computing dependability models, such as analytic method, state space method and simulating method. In order to evaluate the validity of the cloud dependability measurement method, a novel simulating approach is proposed with well-designed framework and procedure. Through reasonable simulating and analysis, proper solutions can be promoted to find and solve the problem related to cloud computing systems dependability. Our future work will focus on an integrated software platform for cloud computing system dependability simulating and verification.

30 citations

Proceedings ArticleDOI
20 Jul 2009
TL;DR: In this article, an artificial neural network (ANN) based method is developed for achieving more accurate remaining useful life prediction of equipment subject to condition monitoring, which takes the age and multiple condition monitoring measurement values at the present and previous inspection points as the inputs, and the life percentage as the output.
Abstract: Accurate equipment remaining useful life prediction is critical to effective condition based maintenance for improving reliability and reducing overall maintenance cost. An artificial neural network (ANN) based method is developed for achieving more accurate remaining useful life prediction of equipment subject to condition monitoring. The ANN model takes the age and multiple condition monitoring measurement values at the present and previous inspection points as the inputs, and the life percentage as the output. Techniques are introduced to reduce the effects of the noise factors that are irrelevant to equipment degradation. The proposed method is validated using real-world vibration monitoring data.

24 citations

Performance
Metrics
No. of papers from the Conference in previous years
YearPapers
202255
201871
2016103
2014161
2011249
2009292