Recognition and determination of fuzzy logical relationship in the system fault evolution process

https://doi.org/10.1016/j.ipm.2021.102630Get rights and content

Highlights

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    The concepts of system fault evolution process and space fault network theory are proposed by the authors.

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    Referring to 14 kinds of logical relationships in flexible logic, the author transforms them into the event occurrence logical relationship formula, which can be regarded as the basic logical relationships; the latter uses enumeration method to change the fuzzy membership degree, and obtains the optimal fuzzy membership degree when the fitness function is closest to 0. Finally, the concrete form of fuzzy logical structure function is obtained.

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    The fuzzy logical relationship between the cause event and the result event is determined by using neural network; and the algorithm analysis diagram and steps are given. The method can effectively solve the problem of determining the fuzzy logical relationship between events in the system fault evolution process.

Abstract

To study the fuzzy logical relationship between the cause event and result event in the system fault evolution process, a method of forming the expression of the fuzzy logical relationship by superposition of the basic logical relationships and fuzzy membership degree is proposed. It is called the fuzzy logical structure function. Two problems need to be solved to determine the fuzzy logical structure function. One is to determine the basic logical relationships; the other is to determine the fuzzy membership degree. The authors transform the 14 kinds of flexible logical relationships into the event occurrence logical relationship formula, which can be regarded as the basic logical relationships; the latter uses the enumeration method to change the fuzzy membership degree and obtains the optimal fuzzy membership degree when the fitness function is closest to 0. Finally, the form of the fuzzy logical structure function is obtained. The fuzzy logical relationship between the cause event and the result event is determined by using a neural network. With an example, it can be seen that the logical relationships between the cause event and the result event are implication and average, and its fuzzy membership degree is more than 80%; when using a neural network, the fuzzy membership degree has no practical significance, but it can obtain the result event probability more accurately through the cause event probability. Finally, the advantages and disadvantages of the method are summarised.

Section snippets

Introductions

In the system fault evolution process (SFEP), the events, influencing factors and the logical relationships between events are three core elements and problems, but they are difficult to solve. First, events are the division and classification of each stage in the SFEP, which not only needs the knowledge of related fields but also has a direct relationship with the personnel's own experience. For the same SFEP, it is difficult for different people to divide and summarise the same events. The

Space fault tree and space fault network

Space fault tree (SFT) theory is a methodology proposed by the author to study the relationship between system reliability and influencing factors. At present, it is divided into four stages, including the basic theory of space fault tree, intelligent space fault tree, space fault network, system movement space and system mapping theory. The content involves many previous studies of the authors [[33], [34], [35], [36], [37], [38], [39], [40], [46], [47], [48], [49], [50], [51], [52]].

System fault evolution and fuzzy logical relationship

The occurrence, development, climax and end of any system fault are processes. The system here includes artificial systems and natural systems. The former is made by humans to accomplish specific purposes, such as high-speed railways and aircraft; the latter is a system formed by nature according to its own laws, such as rock mass systems and climate systems. System faults manifest as artificial system faults and natural system disasters, such as high-speed rail, aircraft faults and natural

Fuzzy logical relationship principles and system construction

According to the fuzzy logical relationship, the fuzzy logical system is established, and the fuzzy logical structure function is determined. According to the definition of the fuzzy logical structure function, the fuzzy membership degree is introduced to superimpose a variety of logical relationships into a fuzzy logical relationship to express the probability relationship between cause events and result events. When the events and factors are determined, the cause events represent the result

Determine the fuzzy logical relationship based on flexible logic

According to the fuzzy logical relationship system model such as Eq. (2), the key to determining the fuzzy logical structure function of the result event caused by the two cause events is the last formula. The formula shows that the fuzzy logical structure function is a function formed by the superposition of various logical relationships according to the membership degree of different logical relationships. However, there are two problems in determining the specific form of P(qx, qy): one is

Using a neural network to determine the fuzzy logical relationship

The last section mentioned the basic idea of using a neural network to obtain fuzzy logical relationships. To determine the fuzzy membership degree αb(b=1,2,...,B), a neural network is used to determine the weight of the input parameters on the output parameters. The algorithm analysis diagram is shown in Fig. 4.

The analysis steps are as follows:

1) Determine the occurrence probability set of event x and event y, as shown in Eq. (7).{qx(dx)=qx(d1),qx(d2),...,qx(Dx);dx=1,...,Dx;qx∈[0,100%]qy(dy)=q

Using enumeration

Suppose that the fault process of a system is as follows, with two cause events x and y as input and one result event as output; that is, the input data are qx and qy, and the output data are P(qx, qy). Enumerating qx and qy, taking 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 and 1, respectively, the probability combination of 100 cause events is obtained, and the corresponding result event probability is 100 values of P(qx, qy). When Dx,Dy = 10, dx,dy∈{1,2,3,4,5,6,7,8,9,10}, establish the

Conclusions

The main purpose of this paper is to determine the fuzzy logical relationship with multiple logic superposition and the method to determine the fuzzy logical structure function in SFEP.

The relationship between fault evolution and fuzzy logic is discussed. The fuzzy logical relationship is defined. It is considered that there is a logical, statistical relationship between events in a macroscopic view. The fuzzy membership degree is introduced in combination with the basic logical relationship to

CRediT authorship contribution statement

Tiejun CUI: Conceptualization, Methodology, Investigation, Writing - original draft. Shasha LI: Validation, Formal analysis, Data curation, Writing - review & editing.

Acknowledgements

No author associated with this paper has disclosed any potential or pertinent conflicts that may be perceived to have an impending conflict with this work.

The author wishes to thank all his friends for their valuable critics, comments and assistance with this paper. This study was partially supported by grants (Grant Nos. 52004120, 51704141, 2017YFC1503102) from the Natural Science Foundation of China, Liaoning Provincial Education Department Project (Grant Nos. LJ2020QNL018), Discipline

Tiejun CUI was born in Shenyang, Liaoning, China, 1983. He received the Ph.D. degree in safety technology and engineering from Liaoning Technical University, Fuxin, Liaoning, China, 2015. He is associate professor with the college of safety science and engineering. His research interest includes the safety system engineering, system reliability and system fault evolution process. He is the author of some books and some papers indexed by SCI and EI. He is a peer-reviewer of international

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    • Cited by (0)

    Tiejun CUI was born in Shenyang, Liaoning, China, 1983. He received the Ph.D. degree in safety technology and engineering from Liaoning Technical University, Fuxin, Liaoning, China, 2015. He is associate professor with the college of safety science and engineering. His research interest includes the safety system engineering, system reliability and system fault evolution process. He is the author of some books and some papers indexed by SCI and EI. He is a peer-reviewer of international journals of SCI and Publishing Group.

    Shasha LI was born in Panjin, Liaoning, China, 1988. She received the Ph.D. degrees in safety management engineering from Liaoning Technical University. She was a lecturer with the school of business administration, Liaoning Technical University. Her research interest includes the safety management engineering and system reliability. She is the author of several publications in international journals and Chinese journals and books, including some papers indexed by SCI and EI.

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