An Improved Model of Functional Failure Identification and Propagation Based on Mathematical Logic

Fault detection and identification in mechatronic systems with complex interdependencies between subsystems is a very active research area. Various alternative quantitative and qualitative methods have been proposed in the literature for fault identification on industrial processes, making it difficult for researchers and industrial practitioners to choose a method for their application. The Functional Failure Identification and Propagation (FFIP) framework has been proposed in past research for risk assessment of early complex system designs. FFIP is a versatile framework which has been extended in prior work to automatically evaluate sets of alternative system designs, perform sensitivity analysis, and event trees generation from critical event scenario simulation results. This paper’s contribution is an FFIP extension, used to generate the training and testing data sets needed to develop fault detection systems based on data driven machine learning methods. The methodology is illustrated with a case study of a generic nuclear power plant where a fault or the location of a fault within the system is identified. Two fault detection methods are compared, based on an artificial neural network and a decision tree. The case study results show that the decision tree was more meaningful as a model and had better detection accuracy (97% success in identification of fault location).

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An Improved FFIP Method Based on Mathematical Logic and SysML

Applied Sciences ◽

10.3390/app11083534 ◽

2021 ◽

Vol 11 (8) ◽

pp. 3534

Author(s):

Jian Jiao ◽

Shujie Pang ◽

Jiayun Chu ◽

Yongfeng Jing ◽

Tingdi Zhao

Keyword(s):

Mathematical Logic ◽

Failure Process ◽

Modeling Language ◽

Fault Propagation ◽

Functional Failure ◽

Cut Set ◽

Simulation Results ◽

Propagation Paths ◽

Graphics Processing ◽

State Machine Diagram

In recent years, the model-based safety analysis (MBSA) has been developing continuously. The Functional Failure Identification and Propagation (FFIP) method is a graphics processing technology which supports the analysis of fault propagation paths before making costly design commitments. However, the traditional FFIP has some deficiencies. In this paper, we extend the functional failure logic (FFL) in the FFIP and introduce the concept of deviation. So, FFIP can be used to analyze the failure process of the systems and make the logical analysis of functional failure easier. Based on the extended FFL, we present a new overview of the FFIP. The FFIP is improved by using mathematical logic and Systems Modeling Language (SysML). The standard expression of FFL is realized, which is conducive to the subsequent modeling and modification. Additionally, we use the failure logic analysis in the FFIP to improve the state machine diagram (SMD) in SysML. Finally, the improved FFIP method is used to analyze the fault propagation paths of the system and Simulink is used for simulation. The fault tree is generated according to the simulation results, the minimum cut set is calculated, and the key failure parts of the system are obtained.

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Early integration of safety to the mechatronic system design process by the functional failure identification and propagation framework

Mechatronics ◽

10.1016/j.mechatronics.2012.01.003 ◽

2012 ◽

Vol 22 (2) ◽

pp. 137-151 ◽

Cited By ~ 40

Author(s):

Seppo Sierla ◽

Irem Tumer ◽

Nikolaos Papakonstantinou ◽

Kari Koskinen ◽

David Jensen

Keyword(s):

System Design ◽

Design Process ◽

Mechatronic System ◽

Early Integration ◽

Functional Failure ◽

Failure Identification

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Mathematical Logic with Special Reference to the Natural Numbers

10.1017/cbo9780511897320 ◽

1972 ◽

Cited By ~ 2

Author(s):

S. W. P. Steen

Keyword(s):

Mathematical Logic ◽

Special Reference ◽

Natural Numbers

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A SURVEY OF MATHEMATICAL LOGIC

10.1016/s0049-237x(08)x7095-8 ◽

1963 ◽

Keyword(s):

Mathematical Logic

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An Improved Model for Short-Term Traffic Forecasting Considering Weather Impacts

CICTP 2017 ◽

10.1061/9780784480915.080 ◽

2018 ◽

Author(s):

Xinchao Chen ◽

Si Qin ◽

Jian Zhang ◽

Huachun Tan ◽

Yunxia Xu ◽

...

Keyword(s):

Short Term ◽

Traffic Forecasting ◽

Improved Model ◽

Weather Impacts

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Linguistic Data Model for Natural Languages and Artificial Intelligence. Part 5. Introduction to Logic

Discourse ◽

10.32603/2412-8562-2020-6-3-109-117 ◽

2020 ◽

Vol 6 (3) ◽

pp. 109-117

Author(s):

O. M. Polyakov

Keyword(s):

Artificial Intelligence ◽

Mathematical Logic ◽

Natural Language ◽

Semantic Interpretation ◽

General View ◽

World Model ◽

Semantic Approach ◽

Natural Languages ◽

The World ◽

Linguistic Aspect

Introduction. The article continues the series of publications on the linguistics of relations (hereinafter R–linguistics) and is devoted to an introduction to the logic of natural language in relation to the approach considered in the series. The problem of natural language logic still remains relevant, since this logic differs significantly from traditional mathematical logic. Moreover, with the appearance of artificial intelligence systems, the importance of this problem only increases. The article analyzes logical problems that prevent the application of classical logic methods to natural languages. This is possible because R-linguistics forms the semantics of a language in the form of world model structures in which language sentences are interpreted.Methodology and sources. The results obtained in the previous parts of the series are used as research tools. To develop the necessary mathematical representations in the field of logic and semantics, the formulated concept of the interpretation operator is used.Results and discussion. The problems that arise when studying the logic of natural language in the framework of R–linguistics are analyzed. These issues are discussed in three aspects: the logical aspect itself; the linguistic aspect; the aspect of correlation with reality. A very General approach to language semantics is considered and semantic axioms of the language are formulated. The problems of the language and its logic related to the most General view of semantics are shown.Conclusion. It is shown that the application of mathematical logic, regardless of its type, to the study of natural language logic faces significant problems. This is a consequence of the inconsistency of existing approaches with the world model. But it is the coherence with the world model that allows us to build a new logical approach. Matching with the model means a semantic approach to logic. Even the most General view of semantics allows to formulate important results about the properties of languages that lack meaning. The simplest examples of semantic interpretation of traditional logic demonstrate its semantic problems (primarily related to negation).

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