Deep learning of system reliability under multi-factor influence based on space fault tree

Cui, Tiejun; Li, Shasha

doi:10.1007/s00521-018-3416-2

Cited by 19 publications

(4 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors in this paper initially proposed Space Fault Tree (SFT) in 2012 [24] , which comprises four stages. These stages encompass the fundamentals of SFT [25,26] , intelligent SFT [27,28] , and the development of an SFN [29] for the SFEP.…”

Section: Related Concepts and Our Work On Sfnmentioning

confidence: 99%

Edge Device Fault Probability Based Intelligent Calculations for Fault Probability of Smart Systems

Li,

Cui,

Viriyasitavat

2024

Tsinghua Sci. Technol.

View full text Add to dashboard Cite

In a smart system, the faults of edge devices directly impact the system's overall fault. Further, complexity arises when different edge devices provide varying fault data. To study the Smart System Fault Evolution Process (SSFEP) under different fault data conditions, an intelligent method for determining the Smart System Fault Probability (SSFP) is proposed. The data types provided by edge devices include the following: (1) only known edge device fault probability; (2) known Edge Device Fault Probability Distribution (EDFPD); (3) known edge device fault number and EDFPD; (4) known factor state of the edge device fault and EDFPD. Moreover, decision methods are proposed for each data case. Transfer Probability (TP) is divided into Continuity Transfer Probability (CTP) and Filterability Transfer Probability (FTP). CTP asserts that a CauseEvent (CE) must lead to a Result Event (RE), while FTP requires CF probability to exceed a threshold before RF occurs. These probabilities are used to calculate SSFP. This paper introduces a decision method using the information diffusion principle for low-data SSFP determination, along with an improved method. The method is based on space fault network theory, abstracting SSFEP into a System Fault Evolution Process (SFEP) for research purposes.

show abstract

Section: Related Concepts and Our Work On Sfnmentioning

confidence: 99%

Edge Device Fault Probability Based Intelligent Calculations for Fault Probability of Smart Systems

Li,

Cui,

Viriyasitavat

2024

Tsinghua Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…SFT is a method proposed by the author in 2012 to analyze the relationship between influencing factors and system reliability. In the first stage, continuous space fault tree [25], [26], discrete space fault tree [27], [28], system structure inverse analysis [29] and related fault data processing methods [30] are formed. However, the actual fault data often has randomness, discreteness and fuzziness, but generally show regularity, and the amount of these data is large.…”

Section: Space Fault Tree and Space Fault Networkmentioning

confidence: 99%

Research on Complex Structures in Space Fault Network for Fault Data Mining in System Fault Evolution Process

Tie-jun

2019

IEEE Access

View full text Add to dashboard Cite

To study the system fault evolution process, and mine the relationships among the fault data, fault factors and fault events, we propose the theory of space fault network. The paper is composed of three parts. In the first part, the shortcomings of the existing studies on unidirectional ring in space fault network are discussed, and three new network description forms of typical unidirectional rings are formulated and the corresponding space fault tree transformation mechanism. The concepts of ring structure, ordered relation, equivalence event and equivalence connection are defined according to the need. Three methods for calculating the target event probability are given. The second part studies the all event induction fault evolution process. All of its edge events and process events lead to the target event. The meaning of all event induction fault evolution process is discussed. Two methods, general fault evolution process and all event induction fault evolution process, are used to calculate the target event probability in the evolution process of a single route and network structure. In the third part, the impact of some event repeatability on the fault and the time characteristics of the evolution process are studied, and the definitions of event repeatability and timeliness are proposed. The occurrence time and duration of events and connections are given to characterize the time characteristics. Finally, according to repeatability and timeliness, the measures to prevent the target event are given. The paper provides a new theory and method for mining system fault evolution process from fault data, fault factors and fault events. INDEX TERMS Safety system engineering, space fault network, unidirectional ring structure, all event induction, repeatability and timeliness, fault data mining.

show abstract

“…Considering the effect of a wide variety of factors on the system fault probability, Cui and Li [27] proposed a state absorption method and a state recurrence method in accordance with the Space Fault Tree (SFT) to study the logical relationship between reliability and factors. Further considering the effect of multi-factors and taking the electrical system as the research object, the failure probability distribution of the components and the system under the two factors (including time and temperature), as well as the probability importance and criticality importance of the components were obtained [26]. Chen et al [7] proposed a continuoustime multi-dimensional T-S DFTA and applied it to the reliability analysis of the hydraulic system of concrete pump trucks.…”

Section: Introductionmentioning

confidence: 99%

Multi-dimensional T-S dynamic fault tree analysis method involving failure correlation

Chen,

Liu,

Yao

et al. 2023

IFS

View full text Add to dashboard Cite

The lack of effective failure correlation analysis is one main reason for the gap between the reliability models and the actual complex systems with mixed static and dynamic characteristics. Takagi and Sugeno (T-S) dynamic fault tree is one powerful tool to analyze the static and dynamic failure logic relationship but it assumes the failure probability of the event is independent. Therefore, this paper proposes a multi-dimensional T-S dynamic fault tree analysis method involving failure correlation. The method integrates the failure probability distribution function of basic events with multi-factors and the multi-dimensional copula function, and the important measure of this method is also deduced. The reliability model expression for systems with failure correlations, both in series and in parallel, is discussed and verified. Compare the proposed method with the assumption that the probability of a failure event is independent. This method solves the problem of a large error when ignoring the failure correlation between parts and the degree of the correlation between variables can be characterized. The reliability analysis can be conducted on complex systems affected both by multi-factors and failure correlations. The proposed method is applied to the reliability analysis of a hydraulic height adjustment system and the correctness and superiority of the method are verified.

show abstract

Deep learning of system reliability under multi-factor influence based on space fault tree

Cited by 19 publications

References 33 publications

Edge Device Fault Probability Based Intelligent Calculations for Fault Probability of Smart Systems

Edge Device Fault Probability Based Intelligent Calculations for Fault Probability of Smart Systems

Research on Complex Structures in Space Fault Network for Fault Data Mining in System Fault Evolution Process

Multi-dimensional T-S dynamic fault tree analysis method involving failure correlation

Contact Info

Product

Resources

About