Fuzzy fault tree analysis based on T–S model with application to INS/GPS navigation system

Abstract

A novel fault tree analysis (FTA) technique based on the Takagi and Sugeno (T-S) model is proposed in this paper. In the proposed technique, referred to as the TS-FTA, the events in the conventional FTA can be expressed in terms of fuzzy possibilities, and the gates that represent the relations among the top event and the primary events are replaced by T-S fuzzy gates derived from the T-S model. The magnitudes of the faults in the system are expressed in term of fuzzy variables. Since the proposed TS-FTA is derived from fuzzy logic and the T-S model, it can readily handle fuzzy information and uncertainties in the relationships among events. Therefore, the TS-FTA is suitable for systems where exact information on the fault probabilities of the components and the failure mechanisms are not available. The proposed TS-FTA is applied to analyze the reliability of an Inertial Navigation System and Global Position System (INS/GPS) integrated navigation system.

Reliability evaluation of a multi-state system based on interval-valued triangular fuzzy Bayesian networks

“…Table 2 shows that the results of interval-valued triangular fuzzy importance are the same as the previous results (Song et al 2009), it verified the feasibility of Eq. (8) and (9) 5 Illustrative example Intelligent substation early warning monitoring function is achieved by intelligent electronic devices in the IEC61850 standards-based seamless real-time communication system.…”

“…Using a navigation system T-S fault tree as an example (Song et al 2009), the constructed Bayesian network parameters are the same as the T-S fault tree. The intervalvalued fuzzification of the probability of failure of all components in the reference, and they are represented by interval-valued triangular fuzzy subset i.e., the failure probability fuzzy subset is {0.9 9 10 -6 , 1.9 9 10 -6 , 2.9 9 10 -6 } when component x 1 is at fault state 1, and it can be expressed by the interval-valued triangular fuzzy subset [(0.9 9 10 -6 , 0.9 9 10 -6 ); 1.9 9 10 -6 ; (2.9 9 10 -6 , 2.9 9 10 -6 )].…”

Unconventional methods of failure risk analysis in collective water supply systems

“…model Mamdaniego [21], Takagi-Sugeno [31]), 3) agregacja reguł (grupowanie) oraz wnioskowanie (inferencja) na podstawie reguły globalnej, 4) wyostrzanie (ang. defuzzification) otrzymanego wyniku, jeśli wyjściem z modelu jest wartość rozmyta.…”

“…Adaptive-Network-Based Fuzzy Inference System, ANFIS). System ANFIS jest oparty na modelu rozmytym Takagi-Sugeno-Kanga (TSK) [31], w którym uczenie przebiega z zastosowaniem metody wstecznej propagacji błędów [11]. System ten został zaproponowany przez J.S.R.…”

Research on Fault Diagnosis for Pumping Station Based on T-S Fuzzy Fault Tree and Bayesian Network

“…(3) Every event can be described only with two states: {0, 1}. The T-S fuzzy gate fault tree analysis method proposed by Song et al [9] integrates the fuzzy theory into the fault tree, which can not only overcome shortcoming (1) through describing the connection between events as an uncertain item but also describe multiple states of the system conveniently. But there are still some disadvantages, such as poor compatibility, complex reasoning process, and only oneway reasoning.…”