Discrete Stress-strength Interference Research Articles

Reliability model for wind turbine blade composites under alternate action of normal and extreme wind loads

Wind turbine blade is the key component to capture wind power, and it is mainly composed of glass fiber reinforced polymer (GFRP). Due to the randomness and volatility of wind speed in nature, wind turbine blade is usually subjected to the alternate action of normal and extreme wind loads during its long-term service. In this study, a reliability model that considering the stochastic wind loads and strength degradation of GFRP is proposed. Firstly, a residual strength model is developed based on the Palmgren-Miner (P-M) damage theory and the same damage state (SDS) principle, which is capable of characterizing the strength degradation law of GFRP under the normal wind load. Then, the alternate actions of normal and extreme wind loads are considered, and a dynamic reliability model is presented based on Poisson process and mathematical derivation. Finally, the traditional discrete stress-strength interference (DSSI) model is extended to calculate the dynamic reliability when probability distributions of stochastic wind loads and residual strength of GFRP are unknown. The wind load data and GFRP test data are utilized to demonstrate the effectiveness of the proposed model. The result shows that the reliability of GFRP keeps at a high level in the early stage, then it rapidly decreases due to the accumulation of fatigue damage.

An expert knowledge-based dynamic maintenance task assignment model using discrete stress–strength interference theory

Expert knowledge has become an important factor in optimization decision-making for complex equipment maintenance. Motivated by the challenges of quantifying expert knowledge as a decision basis, we presented an expert knowledge-based dynamic maintenance task assignment model by using discrete stress–strength interference (DSSI) theory. We constructed the task assignment framework consisting of three parts: building expert database, selecting experts for tasks, and implementing the tasks, in which selecting experts for tasks based on expert knowledge is the key part of the model. To quantify tacit knowledge (experience) in optimization decision for expert recommendation, experience was defined as a probability, which is relevant to two random variables: quantity of task successfully implemented (strength) and quantity of task failed (stress), and experience is defined as the probability that the former (strength) is larger than the latter (stress). Further, universal generating function (UGF) method was used to calculate the experience, and decision rule was designed for the dynamic maintenance task assignment. The model can help collaborative maintenance platform periodically review experts’ performances and assign the corresponding task to the most suitable expert at different periods. A case study shows that the proposed model helps not only to achieve rational allocation of expert resources, but to promote positive competition among experts.

A Discrete Stress–Strength Interference Theory-Based Dynamic Supplier Selection Model for Maintenance Service Outsourcing

Maintenance service outsourcing is a strategic driver for asset intensive industries pursuing the enhancement of supply chain performance. Maintenance service supplier selection plays a relevant role in this premise since its significant impact on equipment availability, and hence, on business success. To periodically review suppliers’ performances and update the outsourcing contract, a discrete stress–strength interference (DSSI) theory-based dynamic supplier selection model is presented for maintenance service outsourcing process. Taken account of the influence of randomness and uncertainty, a novel and universal evaluation criterion, demand fulfillment level (DFL) is introduced based on the DSSI theory. DFL is relevant to two random variables, which are random user's service order quantity (stress) and random supplier's service fulfillment quantity (strength), and DFL is defined as the probability that the latter (strength) is larger than the former (stress). Based on DFL, the proposed model can help users outsource the corresponding maintenance service to the most suitable supplier (maximum supplier reliability) at different periods. The decision rule can be described as a dynamic 3-D diagram, according to which decision makers can periodically review suppliers’ performances and update the outsourcing contract. A case study on maintenance service supplier selection problem for a steel company illustrates the effectiveness of the proposed model.

Modeling Random Forwarding Actions for Information Diffusion over Mobile Social Networks

Modeling information diffusion over social networks has attracted a lot of attention from both academia and industry. Based on universal generating function method and discrete stress-strength interference theory, a novel method is proposed to model the users’ random forwarding actions, and the most susceptible users are extracted. The effect of a user on information diffusion is quantified as node susceptibility (NS), and NS is defined as the probability that quantity of information (message) the user forwards is larger than that he receives. The model can address three questions: which users are most susceptible, which types of information they are most susceptible to, and when they are most susceptible. The solutions of these questions are very helpful for the practitioners. A case study is used to illustrate the feasibility and practicality of the proposed model.

Trivariant Lagrange Factor Polynomial for Discrete Rayleigh Distributed Reliability Analysis of Aeroengine Combustion Chamber Cylinder

In most of the existing stress-strength interference (SSI) models, stress and strength are assumed to be independent structural variants. However, under severe thermal conditions, such as in aeroengine combustion chamber, this assumption may not hold. One structural variant, such as strength, may become unilateral dependent on another variant, such as stress or temperature. In addition, to evaluate the discrete reliability of structures using unilateral dependent structural variants, discrete SSI models were developed using not just linear polynomial or line segments, but higher order polynomials. These models are based on the trivariant Lagrange factor polynomial approach. Normal distributed temperature dependent stress and Rayleigh distributed thermal stress dependent strength are represented by discrete structural variants that possess unilateral dependent probability mean functions. Based on their dependence formulations, the trivariant Lagrange factor polynomial of the discrete SSI model was generated. Applicability of this method was validated by a specific aeroengine combustion chamber cylinder using different molding alloys. Meanwhile the application range of some existing SSI models is extended for interval shifted data. Comparing machinability, reliability, and economic factors, 1Cr11MoV was the most suitable alloy in the design.

Discrete Stress-strength Interference Model of Reliability Analysis under Multi-operating Conditions

The conventional stress-strength interference(SSI) model is a basic model for reliability analysis of mechanical components. In this model, the component reliability is defined as the probability of the strength being larger than the stress, where the component stress is generally represented by a single random variable(RV). But for a component under multi-operating conditions, its reliability can not be calculated directly by using the SSI model. The problem arises from that the stress on a component under multi-operating conditions can not be described by a single RV properly. Current research concerning the SSI model mainly focuses on the calculation of the static or dynamic reliability of the component under single operation condition. To evaluate the component reliability under multi-operating conditions, this paper uses multiple discrete RVs based on the actual stress range of the component firstly. These discrete RVs have identical possible values and different corresponding probability value, which are used to represent the multi-operating conditions of the component. Then the component reliability under each operating condition is calculated, respectively, by employing the discrete SSI model and the universal generating function technique, and from this the discrete SSI model under multi-operating conditions is proposed. Finally the proposed model is applied to evaluate the reliability of a transmission component of the decelerator installed in an aeroengine. The reliability of this component during taking-off, cruising and landing phases of an aircraft are calculated, respectively. With this model, a basic method for reliability analysis of the component under complex load condition is provided, and the application range of the conventional SSI model is extended.

A Discrete Stress-Strength Interference Model With Stress Dependent Strength

In structural reliability engineering, one often encounters situations where the strength of a structure is influenced by the stress, but the stress is irrelevant to the strength. This phenomenon can be called a unilateral dependency of strength on stress. To evaluate structural reliability in such cases, the stress on a structure is proposed to be a discrete random variable, and the stress dependent strength is represented by a discrete random variable that has different conditional probability mass functions under different stress amplitudes. Then a discrete stress-strength interference model with stress dependent strength is presented based on the universal generating function technique. Finally, the effectiveness of this model is demonstrated by an illustrative example.

A discrete stress–strength interference model based on universal generating function

Continuous stress–strength interference (SSI) model regards stress and strength as continuous random variables with known probability density function. This, to some extent, results in a limitation of its application. In this paper, stress and strength are treated as discrete random variables, and a discrete SSI model is presented by using the universal generating function (UGF) method. Finally, case studies demonstrate the validity of the discrete model in a variety of circumstances, in which stress and strength can be represented by continuous random variables, discrete random variables, or two groups of experimental data.

Fuzzy reliability using a discrete stress-strength interference model : J. D. Wang and T. S. Liu. IEEE Transactions on Reliability, 45 (1), 145 (1996)

Fuzzy reliability using a discrete stress-strength interference model : J. D. Wang and T. S. Liu. IEEE Transactions on Reliability, 45 (1), 145 (1996)

Fuzzy reliability using a discrete stress-strength interference model

This paper uses a fuzzy line-segment method to calculate the fuzzy unreliability of a system when only discrete-interval probabilities of stress and strength inside an interference region are available. The discrete-interval probabilities are treated as fuzzy numbers. A stress-strength interference model and extended operations of fuzzy numbers are used to calculate the fuzzy unreliability. Probability density functions are approximated by piecewise fuzzy line-segments that are expressed by linear fuzzy polynomials; the centroid of the membership function for the fuzzy unreliability is treated as the point estimate of unreliability. This method requires less computer time than previous methods in the literature, and is useful since it does not require information on distribution types of stress and strength. Numerical examples demonstrate the method.

A discrete stress-strength interference model for unreliability bounds

Abstract This study presents a multiple-line-segment method of implementing the stress-strength interference model when only discrete interval probabilities of stress and strength inside an interference region are available. Based on the discrete interval probabilities, the probability density functions are represented by line segments, which are in turn expressed by piecewise linear polynomials. Thus, unlike in existing empirical methods, when both stress and strength fall into the same subinterval, one can calculate the probability of failure by directly integrating the joint probability of stress and strength over the subinterval. Quadratic programming optimization is implemented to determine the upper and lower bounds on the failure probability, and the average value of the bounds is treated as the point estimate of the failure probability. Finally, numerical examples are carried out to demonstrate the proposed method. Three quantities are used to evaluate the accuracy and effectiveness of the proposed method—the relative error of the point estimate of the unreliability, the width of the bounds on the unreliability, and the minimum number of subintervals needed to generate the bounds that contain the exact unreliability. These three quantities are compared with results in the literature.