An Efficient Universal Generating Function-Based Analyzing Approach for Multistate System with Imperfect Coverage Failure

A system with more than two states is called a multistate system (MSS), and such systems have already become a general trend in the arena of complex industrial products and/or systems. Fault-tolerant technology often plays a very important role in improving the reliability of an MSS. However, the existence of imperfect coverage failure (ICF) in a work-sharing group (WSG) decreases the reliability of MSS. A method is proposed to assess the reliability and sensitivity of an MSS with ICF. The components in a WSG can cooperate so as to improve overall efficiency by increasing performance levels. Using the technique of the universal generating function (UGF), a component’s UGF expression with ICF can be incorporated in two steps. During the computation of the system’s UGF, an algorithm based on matrix (ABM) is developed to reduce the computational complexity. Consequently, indices of reliability can be easily calculated based on the UGF expression of an MSS. Sensitivity analysis can help engineers judge which WSG should be eliminated first under various resource limitations. Examples illustrate and validate this method.

Reliability Estimation of 4 × 4 SENs Using UGF Method

Shuffle Exchange Networks (SENs) are considered as an appropriate interconnection network because they consist of switching elements of small size and possess a straight forward and simple configuration. In this paper, we have proposed a method for analyzing reliability of 4×4 SEN, 4×4 SEN+1 and 4×4 SEN+2. The reliability has been obtained on the basis of three indices, namely, terminal reliability, broadcast reliability and network reliability by using universal generating function (UGF) method. This study also examines effect of adding the additional stages in 4×4 shuffle exchange networks (SENs).

Reliability Evaluation of Repairable Parallel-Series Multi-State System Implementing Interval Valued Universal Generating Function

In this paper, we have studied a repairable parallel-series multi-state system. The proposed system consists of m components in series and n components in parallel in which each component has three possible states. The interval universal generating function (IUGF) is presented, and the corresponding composition operators are defined. The reliability assessment of the considered system is done with the help of the IUGF approach. It is worth mentioning that IUGF got attention from various researchers due to its straightforwardness, less complexity, and universal applications. In the present model, probabilities of different components, reliability, sensitivity, and mean time to failure are evaluated with the help of the Markov process; Laplace-Steiltjes transform method applying IUGF. A numerical example has also been taken to illustrate the proposed technique.

Reliability assessment of wind turbine generators by fuzzy universal generating function

Wind power has been widely used in the past decade because of its safety and cleanness. Double fed induction generator (DFIG), as one of the most popular wind turbine generators, suffers from degradation. Therefore, reliability assessment for this type of generator is of great significance. The DFIG can be characterized as a multi-state system (MSS) whose components have more than two states. However, due to the limited data and/or vague judgments from experts, it is difficult to obtain the accurate values of the states and thus it inevitably contains epistemic uncertainty. In this paper, the fuzzy universal generating function (FUGF) method is utilized to conduct the reliability assessment of the DFIG by describing the states using fuzzy numbers. First, the fuzzy states of the DFIG system’s components are defined and the entire system state is calculated based the system structure function. Second, all components’ states are determined as triangular fuzzy numbers (TFN) according to experts’ experiences. Finally, the reliability assessment of the DFIG based on the FUGF is conducted.

A combined universal generating function and physics of failure Reliability Prediction Method for an LED driver

The accurate and effective reliability prediction of light emitting diode (LED) drivers has emerged as a key issue in LED applications. However, previous studies have mainly focused on the reliability of electrolytic capacitors or other single components while ignoring circuit topology. In this study, universal generating function (UGF) and physics of failure (PoF) are integrated to predict the reliability of LED drivers. Utilizing PoF, lifetime data for each component are obtained. A system reliability model with multi-phase is established, and system reliability can be predicted using UGF. Illustrated by a two-channel LED driver, the beneficial effects of capacitors and MOSFETs for the reliability of LED drivers is verified. This study (i) provides a universal numerical approach to predict the lifetime of LED drivers considering circuit topology, (ii) enhances the modelling and reliability evaluation of circuits, and (iii) bridges the gap between component and circuit system levels.